diff --git a/.DS_Store b/.DS_Store
index 7dd6299318eff92ea60bc577e86e374643134dfe..7adcf4963bd4b17a62433595c4724161e4a57e58 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.gitignore b/.gitignore
index ef690c9b96775a43a5d9430ce335df3b62ed52ec..505f101ac1bcff47ec9c4325400e33a276a78555 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,11 @@ appstore/static/__pycache__/
venv/
+appstore/repos/
+
+appstore/static/camera-trap-tools/images/
+appstore/static/il2bb/images
+appstore/static/megedetector/images
appstore/static/zamba/media/
appstore/static/zamba/train/videos/
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index 8078425594a090464e53808a5b50c101a3a42e41..0000000000000000000000000000000000000000
--- a/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-# Use an official Python runtime as the base image
-FROM python:3.9-slim
-
-# Install dependencies
-RUN apt-get update && apt-get install -y \
- git \
- gcc \
- g++
-
-RUN apt-get update && apt-get install -y docker.io docker-compose
-
-# Set the working directory in the container
-WORKDIR /app
-
-# Copy the requirements file into the container
-COPY requirements.txt .
-
-# Install the dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy the rest of the application code into the container
-COPY . .
-
-# Define the command to run your application
-CMD [ "flask", "--app", "appstore", "run", "--host=0.0.0.0", "--port=5001", "--debug" ]
\ No newline at end of file
diff --git a/Dockerfile.api b/Dockerfile.api
new file mode 100644
index 0000000000000000000000000000000000000000..6a3b071885ae77c4bfd6bf9fcb85701aefb38ce5
--- /dev/null
+++ b/Dockerfile.api
@@ -0,0 +1,62 @@
+# Use an official Python runtime as the base image
+FROM python:3.9-slim
+
+# Install dependencies
+RUN apt-get update && apt-get install -y \
+ git \
+ gcc \
+ g++ \
+ libgl1-mesa-glx \
+ libglib2.0-0 \
+ curl \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install Docker CLI
+RUN curl -fsSL https://get.docker.com -o get-docker.sh && sh get-docker.sh
+
+# Install Docker Compose V2
+RUN mkdir -p ~/.docker/cli-plugins/ \
+ && curl -SL https://github.com/docker/compose/releases/download/v2.17.2/docker-compose-linux-x86_64 -o ~/.docker/cli-plugins/docker-compose \
+ && chmod +x ~/.docker/cli-plugins/docker-compose
+
+# Install Docker
+RUN apt-get update && apt-get install -y \
+ apt-transport-https \
+ ca-certificates \
+ gnupg \
+ lsb-release
+RUN curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
+RUN echo \
+ "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/debian \
+ $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null
+RUN apt-get update && apt-get install -y docker-ce docker-ce-cli containerd.io
+
+# Install Docker Compose
+RUN curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
+RUN chmod +x /usr/local/bin/docker-compose
+
+# Install envsubst
+RUN apt-get update && apt-get install -y gettext-base
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY app.py .
+COPY ./appstore ./appstore
+
+# Set permissions for the Trapper directory
+RUN chmod -R 777 /app/appstore/repos/trapper
+
+# Expose the port the app runs on
+EXPOSE 5001
+
+# Define the command to run your application
+CMD ["python", "app.py"]
diff --git a/Dockerfile.client b/Dockerfile.client
new file mode 100644
index 0000000000000000000000000000000000000000..9b79eab08a9f5a12d1b46e91b5f9b7b1a2b4b528
--- /dev/null
+++ b/Dockerfile.client
@@ -0,0 +1,20 @@
+# Use an official Node runtime as the base image
+FROM node:20-alpine
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy package.json and package-lock.json
+COPY frontend/package*.json ./
+
+# Install dependencies
+RUN npm install
+
+# Copy the rest of the frontend code
+COPY frontend/ .
+
+# Expose the port the app runs on
+EXPOSE 3001
+
+# Define the command to run your application
+CMD ["npm", "start"]
diff --git a/app.py b/app.py
index b49757cdc6151e17130f920fc14db6ab6e150135..26c2133d575d13507072449db24e154e9c946cff 100644
--- a/app.py
+++ b/app.py
@@ -1,4 +1,4 @@
from appstore import app
if __name__ == '__main__':
- app.run(debug=True)
\ No newline at end of file
+ app.run(host='0.0.0.0', port=5001, debug=True)
\ No newline at end of file
diff --git a/appstore/.DS_Store b/appstore/.DS_Store
index f813590c606f8c51ea6d3f3aaba8e70680f98b84..35f42065ed700cd3cecd3202e14a0b01e13eb89e 100644
Binary files a/appstore/.DS_Store and b/appstore/.DS_Store differ
diff --git a/appstore/__init__.py b/appstore/__init__.py
index 269808c7750821f2a05c297ae40fb224e5375c26..1ac5e6229cb3d52a80fecb4216b4bffe503d0896 100644
--- a/appstore/__init__.py
+++ b/appstore/__init__.py
@@ -1,42 +1,72 @@
from flask import Flask
from flask_cors import CORS
-from sqlalchemy import create_engine
+from flask_login import LoginManager
+from flask_bcrypt import Bcrypt
from celery import Celery
-from appstore.models import Base, AppMetadata
+from flask_sqlalchemy import SQLAlchemy
+import boto3
app = Flask(__name__, static_folder='static', template_folder='templates')
# Enable CORS for all routes
-CORS(app)
+CORS(app, supports_credentials=True)
# Load configuration from config.py
app.config.from_object('appstore.config')
+# AWS S3 client
+s3_client = boto3.client(
+ 's3',
+ aws_access_key_id=app.config['AWS_ACCESS_KEY_ID'],
+ aws_secret_access_key=app.config['AWS_SECRET_ACCESS_KEY'],
+ region_name=app.config['AWS_REGION']
+)
+
+# Database connection
+db = SQLAlchemy(app)
+from appstore.models import AppMetadata, User
+
+# Initialise LoginManager
+login_manager = LoginManager()
+login_manager.init_app(app)
+login_manager.login_view = 'main.login'
+
+# Initialise Bcrypt
+bcrypt = Bcrypt(app)
+
# Celery configuration
celery = Celery(app.name, broker=app.config['CELERY_BROKER_URL'], backend=app.config['CELERY_RESULT_BACKEND'])
celery.conf.update(app.config)
-# Database connection
-engine = create_engine(app.config['DATABASE_URI'], echo=True)
-connection = engine.connect()
-Base.metadata.create_all(engine)
+@login_manager.user_loader
+def load_user(user_id):
+ return User.query.get(user_id)
# Import and register blueprints
-from appstore.routes import main_bp, zamba_bp, trapper_bp, animl_bp, megadetector_bp
+from appstore.routes import main_bp, zamba_bp, trapper_bp, animl_bp, megadetector_bp, ecosecrets_bp, il2bb_bp, camera_trap_tools_bp, wildCoFaceBlur_bp, dataSpace_bp, cameraTrapWorkflow_bp
app.register_blueprint(main_bp)
app.register_blueprint(zamba_bp)
app.register_blueprint(trapper_bp)
app.register_blueprint(animl_bp)
app.register_blueprint(megadetector_bp)
-
+app.register_blueprint(ecosecrets_bp)
+app.register_blueprint(il2bb_bp)
+app.register_blueprint(camera_trap_tools_bp)
+app.register_blueprint(wildCoFaceBlur_bp)
+app.register_blueprint(dataSpace_bp)
+app.register_blueprint(cameraTrapWorkflow_bp)
# Import utils after app initialization
-from appstore.utils import trapper_metadata, create_app_metadata, animl_metadata
-from sqlalchemy import text
-
-# Create Trapper metadata if it doesn't exist
-for app_name in ['Trapper', 'Animl']:
- result = connection.execute(text("SELECT * FROM app_metadata WHERE name = :name"), {"name": app_name}).fetchone()
- if not result:
- app_metadata = globals()[f"{app_name.lower()}_metadata"]
- create_app_metadata(app_metadata)
\ No newline at end of file
+from appstore.utils import create_app_metadata, trapper_metadata, animl_metadata, ecosecrets_metadata, il2bb_metadata, cameratraptools_metadata, wildcofaceblur_metadata, cameratrapworkflow_metadata, zamba_metadata, megadetector_metadata
+
+# Create app metadata if it doesn't exist
+def create_initial_metadata():
+ for app_name in ['Trapper', 'Animl', 'EcoSecrets', 'IL2BB', 'CameraTrapTools', 'WildCoFaceBlur', 'CameraTrapWorkflow', 'Zamba', 'MegaDetector']:
+ existing = AppMetadata.query.filter_by(name=app_name).first()
+ if not existing:
+ app_metadata = globals()[f"{app_name.lower()}_metadata"]
+ create_app_metadata(app_metadata)
+
+with app.app_context():
+ db.create_all()
+ create_initial_metadata()
\ No newline at end of file
diff --git a/appstore/__pycache__/__init__.cpython-39.pyc b/appstore/__pycache__/__init__.cpython-39.pyc
index 20a3a6886f14a5e5fda062f73cc57b6f559ba09e..d29ba4c538ff17cb0cc742bb51946a504b1867fe 100644
Binary files a/appstore/__pycache__/__init__.cpython-39.pyc and b/appstore/__pycache__/__init__.cpython-39.pyc differ
diff --git a/appstore/config.py b/appstore/config.py
index 1e376eba92aa152cf85e89e0686d7e7860ea0997..de9952015ecb2ae598457fbbd472da2e0f0af7fb 100644
--- a/appstore/config.py
+++ b/appstore/config.py
@@ -4,6 +4,13 @@ import os
SECRET_KEY = 'your_secret_key_here'
DEBUG = True
+# AWS S3 settings
+AWS_ACCESS_KEY_ID = 'AKIA2FXAD64WVBFK572X'
+AWS_SECRET_ACCESS_KEY = '462VZngMjz4n2gfAJBeJAB9G5jVtFFC42UBpRYrY'
+# AWS_SECRET_ACCESS_KEY = 'randomsecretkeyidonhavemoneytopay'
+AWS_REGION = 'eu-west-1'
+S3_BUCKET_NAME = 'camtrap-appstore'
+
# Zamba file upload settings
UPLOAD_FOLDER = 'static/zamba/media'
TRAIN_FOLDER = 'static/zamba/train'
@@ -13,12 +20,25 @@ TRAIN_VIDEOS_FOLDER = 'static/zamba/train/videos'
MEGADETECTOR_UPLOAD_FOLDER = 'static/megadetector/images'
MEGADETECTOR_MAIN_FOLDER = 'static/megadetector'
+# IL2BB file upload settings
+IL2BB_MAIN_FOLDER = 'static/il2bb'
+IL2BB_UPLOAD_FOLDER = 'static/il2bb/images'
+
+# Camera Trap Tools file upload settings
+CAMERA_TRAP_TOOLS_MAIN_FOLDER = 'static/camera-trap-tools'
+CAMERA_TRAP_TOOLS_UPLOAD_FOLDER = 'static/camera-trap-tools/images'
+
+# WildCo-FaceBlur file upload settings
+WILDCO_FACEBLUR_MAIN_FOLDER = 'static/wildco-faceblur'
+WILDCO_FACEBLUR_UPLOAD_FOLDER = 'static/wildco-faceblur/images'
+
# Celery settings
CELERY_BROKER_URL = 'redis://localhost:6379/0'
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
# Database settings
-DATABASE_URI = 'mysql+pymysql://c22097859:Masterdata822!@csmysql.cs.cf.ac.uk:3306/c22097859_dissertation'
+SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://c22097859:Masterdata822!@csmysql.cs.cf.ac.uk:3306/c22097859_dissertation'
+SQLALCHEMY_TRACK_MODIFICATIONS = False
# GitLab container registry settings
GITLAB_REGISTRY_URL = 'registry.git.cf.ac.uk'
diff --git a/appstore/models.py b/appstore/models.py
index eeb8a3094da4f39412705543a3e45d4b51c550bc..758bde4be47236a58032dc8bf87a3710b7083561 100644
--- a/appstore/models.py
+++ b/appstore/models.py
@@ -1,14 +1,24 @@
-from sqlalchemy import Column, Integer, String, Text
-from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy import Column, Integer, String, Text, JSON
+from uuid import uuid4
+from flask_login import UserMixin
+from appstore import db
-Base = declarative_base()
-class AppMetadata(Base):
+def get_uuid():
+ return uuid4().hex
+
+class AppMetadata(db.Model):
__tablename__ = 'app_metadata'
id = Column(Integer, primary_key=True)
name = Column(String(255), nullable=False)
+ display_name = Column(String(255), nullable=False)
description = Column(Text, nullable=True)
+ full_description = Column(Text, nullable=True)
+ logo = Column(String(255), nullable=True)
+ image = Column(String(255), nullable=True)
+ link = Column(String(255), nullable=True)
+ tags = Column(JSON, nullable=True)
repository_url = Column(String(255), nullable=False)
docker_compose_file = Column(String(255), nullable=True)
docker_image = Column(String(255), nullable=True)
@@ -16,4 +26,20 @@ class AppMetadata(Base):
stop_command = Column(Text, nullable=True)
def __repr__(self):
- return f"<AppMetadata(name='{self.name}', id={self.id})>"
\ No newline at end of file
+ return f"<AppMetadata(id={self.id}, name='{self.name}', display_name='{self.display_name}', description='{self.description}', full_description='{self.full_description}', logo='{self.logo}', image='{self.image}', link='{self.link}', tags='{self.tags}', repository_url='{self.repository_url}')>"
+
+class User(db.Model, UserMixin):
+ __tablename__ = 'users'
+
+ id = Column(String(36), primary_key=True, default=get_uuid, unique=True)
+ username = Column(String(150), nullable=False)
+ password = Column(String(150), nullable=False)
+ email = Column(String(150), nullable=False)
+ organisation = Column(String(150), nullable=True)
+ title = Column(String(150), nullable=True)
+
+ def get_id(self):
+ return str(self.id)
+
+ def __repr__(self):
+ return f"<User(username='{self.username}', id={self.id})>"
\ No newline at end of file
diff --git a/appstore/repos/.DS_Store b/appstore/repos/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..e79916851f4fd8ba4551ae2fd724c9fea19bdebe
Binary files /dev/null and b/appstore/repos/.DS_Store differ
diff --git a/appstore/routes/__init__.py b/appstore/routes/__init__.py
index 2b73ffd86ed6eeb71a948750408febdad193bd3e..60f3f6c585f90ddb724fa0bd0cd9554db0ecc1ab 100644
--- a/appstore/routes/__init__.py
+++ b/appstore/routes/__init__.py
@@ -2,4 +2,10 @@ from .main import bp as main_bp
from .zamba import bp as zamba_bp
from .trapper import bp as trapper_bp
from .animl import bp as animl_bp
-from .megadetector import bp as megadetector_bp
\ No newline at end of file
+from .megadetector import bp as megadetector_bp
+from .ecosecrets import bp as ecosecrets_bp
+from .il2bb import bp as il2bb_bp
+from .cameraTrapTools import bp as camera_trap_tools_bp
+from .wildCoFaceBlur import bp as wildCoFaceBlur_bp
+from .dataSpace import bp as dataSpace_bp
+from .cameraTrapWorkflow import bp as cameraTrapWorkflow_bp
diff --git a/appstore/routes/animl.py b/appstore/routes/animl.py
index 5c69b149d9855ed64bb35ce0d132cb3e31680066..52798ce7c9f0c2d23e8d1cf1d2eff3dda95e8159 100644
--- a/appstore/routes/animl.py
+++ b/appstore/routes/animl.py
@@ -9,14 +9,18 @@ bp = Blueprint('animl', __name__, url_prefix='/animl')
@bp.route('/start', methods=['POST'])
def start_animl():
- start_animl_endpoint()
+ response, status_code = start_animl_endpoint()
+ if status_code != 202:
+ return response, status_code
max_retries = 60
retries = 0
- animl_url = "http://127.0.0.1:5173/"
+ internal_url = "http://animl-container:5173/"
+ external_url = "http://localhost:5173/" # This is the URL that the frontend will use
while retries < max_retries:
- if is_container_running_by_name('animl-container') and is_server_ready(animl_url):
- return jsonify({'status': 'running', 'url': animl_url}), 200
+ if is_container_running_by_name('animl-container'):
+ if is_server_ready(internal_url):
+ return jsonify({'status': 'running', 'url': external_url}), 200
time.sleep(5)
retries += 1
@@ -48,7 +52,16 @@ def start_animl_endpoint():
image.id,
name='animl-container',
ports={'5173/tcp': 5173},
- detach=True
+ detach=True,
+ network='appstore_network'
)
- return jsonify({'message': 'Animl started', 'container_id': container.id}), 202
\ No newline at end of file
+ # Ensure the container is connected to the appstore_network
+ appstore_network = client.networks.get('appstore_network')
+ try:
+ appstore_network.connect(container)
+ except docker.errors.APIError as e:
+ if 'already exists' not in str(e):
+ raise
+
+ return jsonify({'message': 'Animl started', 'container_id': container.id}), 202
diff --git a/appstore/routes/cameraTrapTools.py b/appstore/routes/cameraTrapTools.py
new file mode 100644
index 0000000000000000000000000000000000000000..78effbd03a253b12a8526966b85d3aa42b4aa602
--- /dev/null
+++ b/appstore/routes/cameraTrapTools.py
@@ -0,0 +1,361 @@
+from flask import Blueprint, jsonify, request, send_file
+from appstore.utils import get_app_metadata, is_container_running_by_name
+from appstore import app, s3_client
+import docker
+import os
+import tarfile
+import io
+import time
+import zipfile
+
+bp = Blueprint("camera_trap_tools", __name__, url_prefix="/camera-trap-tools")
+client = docker.from_env()
+BUCKET_NAME = app.config['S3_BUCKET_NAME']
+S3_FOLDER = 'uploads'
+
+@bp.route("/start", methods=["GET", "POST"])
+def start_camera_trap_tools():
+ start_camera_trap_tools_endpoint()
+
+ max_retries = 120
+ retries = 0
+ while retries < max_retries:
+ if is_container_running_by_name('camera-trap-tools-container'):
+ return jsonify({'status': 'running'}), 200
+ time.sleep(5)
+ retries += 1
+
+ return jsonify({'error': 'Failed to start Camera Trap Tools', 'status': 'timeout'}), 500
+
+def start_camera_trap_tools_endpoint():
+ app_metadata = get_app_metadata("CameraTrapTools")
+
+ try:
+ container = client.containers.get("camera-trap-tools-container")
+ if container.status != "running":
+ container.start()
+ print("Started existing camera-trap-tools-container")
+ except docker.errors.NotFound:
+ print("No existing camera-trap-tools-container found, creating a new one")
+ # Login to GitLab container registry
+ client.login(
+ username=app.config['GITLAB_REGISTRY_USERNAME'],
+ password=app.config['GITLAB_ACCESS_TOKEN'],
+ registry=app.config['GITLAB_REGISTRY_URL']
+ )
+ # Pull and run the latest image
+ image = client.images.pull(app_metadata['docker_image'])
+ container = client.containers.run(
+ image.id,
+ name='camera-trap-tools-container',
+ detach=True
+ )
+
+ return jsonify({"message": "Camera Trap Tools started", "container_id": container.id}), 202
+
+@bp.route("/autocopy/upload", methods=["POST"])
+def autocopy_upload():
+ # example: python3 autocopy.py -c example_data/example.config
+ container = client.containers.get("camera-trap-tools-container")
+ img_src_path = os.path.join(os.path.dirname(__file__), '..', app.config['CAMERA_TRAP_TOOLS_UPLOAD_FOLDER'])
+ img_dest_path = '/app/data/SD_Card_Images'
+
+ # Save image files to local directory
+ uploaded_images = []
+ image_paths = request.form.getlist('images')
+ for s3_path in image_paths:
+ try:
+ # Extract the filename from the S3 path
+ filename = os.path.basename(s3_path)
+ local_file_path = os.path.join(img_src_path, filename)
+ print("Local file path: ", local_file_path)
+
+ # Download the file from S3
+ s3_client.download_file(BUCKET_NAME, s3_path, local_file_path)
+
+ if os.path.exists(local_file_path):
+ uploaded_images.append(filename)
+ else:
+ return jsonify({"status": "error", "message": f"Failed to download {filename} from S3"})
+ except Exception as e:
+ return jsonify({"status": "error", "message": f"Error processing {s3_path}: {str(e)}"})
+
+ if len(uploaded_images) == len(image_paths):
+ print("Images downloaded from S3")
+ else:
+ print("Some images failed to download from S3. Files: ", uploaded_images)
+
+ # Copy local files to container
+ tar_stream = io.BytesIO()
+ with tarfile.open(fileobj=tar_stream, mode='w') as tar:
+ for filename in os.listdir(img_src_path):
+ tar.add(os.path.join(img_src_path, filename), arcname=filename)
+ tar_stream.seek(0)
+
+ # Check if the directory exists in the container
+ check_dir = container.exec_run(f"test -d {img_dest_path}")
+ if check_dir.exit_code != 0:
+ container.exec_run(f"mkdir -p {img_dest_path}")
+
+ # Now we can safely put the archive in the directory
+ container.put_archive(img_dest_path, tar_stream)
+
+ return jsonify({
+ "status": "success",
+ "messgae": "Images uploaded"
+ }), 202
+
+@bp.route("/autocopy/process", methods=["POST"])
+def autocopy_process():
+ # use default config file (autocopy.config)
+ container = client.containers.get("camera-trap-tools-container")
+ animal_detection = request.json.get('animalDetection', False)
+ if animal_detection:
+ container.exec_run("python3 autocopy.py -c autocopy_detect.config")
+ else:
+ container.exec_run("python3 autocopy.py")
+ print("Autocopy triggered")
+
+ max_wait_time = 60
+ start_time = time.time()
+ while time.time() - start_time < max_wait_time:
+ try:
+ log_file = container.exec_run("cat /app/data/autocopy_errors.log")
+ raw_img_folder = container.exec_run("ls /app/data/RawImages")
+ if log_file.exit_code == 0 and raw_img_folder.exit_code == 0 and raw_img_folder.output:
+ print("Autocopy completed successfully")
+ return jsonify({"status": "success", "message": "Autocopy completed successfully", "log": log_file.output.decode('utf-8')}), 202
+ except docker.errors.NotFound:
+ pass
+ time.sleep(2)
+
+ print("Autocopy failed or timed out")
+ return jsonify({"status": "error", "message": "Autocopy failed or timed out"}), 500
+
+@bp.route("/create-video", methods=["POST"])
+def create_video():
+ container = client.containers.get("camera-trap-tools-container")
+ container.exec_run("python3 create_video.py -f -c autocopy.config")
+ print("Video creation triggered")
+
+ max_wait_time = 60
+ start_time = time.time()
+ while time.time() - start_time < max_wait_time:
+ try:
+ video_folder = container.exec_run("ls /app/data/Video")
+ if video_folder.exit_code == 0 and video_folder.output:
+ print("Videos created successfully")
+ return jsonify({"status": "success", "message": "Videos created successfully"}), 202
+ except docker.errors.NotFound:
+ pass
+ time.sleep(2)
+
+ print("Video creation failed or timed out")
+ return jsonify({"status": "error", "message": "Video creation failed or timed out"}), 500
+
+@bp.route("/create-annotation", methods=["POST"])
+def create_annotation():
+ container = client.containers.get("camera-trap-tools-container")
+ container.exec_run("python3 create_annotations.py -c autocopy_detect.config")
+ print("Craeting annotation")
+
+ max_wait_time = 60
+ start_time = time.time()
+ while time.time() - start_time < max_wait_time:
+ try:
+ annotations_folder = container.exec_run("ls /app/data/Annotations")
+ if annotations_folder.exit_code == 0 and annotations_folder.output:
+ print("Annotation created successfully")
+ return jsonify({"status": "success", "message": "Annotation created successfully"}), 202
+ except docker.errors.NotFound:
+ pass
+ time.sleep(2)
+
+ print("Annotation creation failed or timed out")
+ return jsonify({"status": "error", "message": "Annotation creation failed or timed out"}), 500
+
+@bp.route("/create-annotation/report", methods=["POST"])
+def annotation_report():
+ container = client.containers.get("camera-trap-tools-container")
+ result = container.exec_run("python3 annotation_report.py -c autocopy_detect.config -o data/annotation_report.csv")
+ if result.exit_code != 0:
+ return jsonify({"status": "error", "message": "Annotation report creation failed"}), 500
+ print("Annotation report created")
+ return jsonify({"status": "success", "message": "Annotation report created successfully"}), 202
+
+@bp.route("/capture-report", methods=["POST"])
+def capture_report():
+ form_data = request.form
+ month = form_data.get("capture-report-month")
+ container = client.containers.get("camera-trap-tools-container")
+ result = container.exec_run(f"python3 capture_report.py -c autocopy_detect.config -m {month}")
+ if result.exit_code != 0:
+ return jsonify({"status": "error", "message": "Capture report creation failed"}), 500
+ print("Capture report created")
+ return jsonify({"status": "success", "message": "Capture report created successfully"}), 202
+
+
+# Download results - needs tidy up
+@bp.route("/autocopy/download", methods=["GET"])
+def autocopy_download():
+ container = client.containers.get("camera-trap-tools-container")
+ target_path = "/app/data"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_files = container.exec_run(f"find {target_path} -type f -not -path '*/SD_Card_Images/*'")
+ if find_files.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_files.output.decode('utf-8').split('\n')
+ print(f"Files found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapTools_autocopy_result.zip'
+ ), 202
+
+@bp.route("/create-video/download", methods=["GET"])
+def create_video_download():
+ container = client.containers.get("camera-trap-tools-container")
+ target_path = "/app/data/Video"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_video = container.exec_run(f"find {target_path} -type f")
+ if find_video.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_video.output.decode('utf-8').split('\n')
+ print(f"Videos found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapTools_createvideos_result.zip'
+ ), 202
+
+@bp.route("/create-annotation/download", methods=["GET"])
+def create_annotation_download():
+ container = client.containers.get("camera-trap-tools-container")
+ target_path = "/app/data/Annotations"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_files = container.exec_run(f"find {target_path} -type f")
+ if find_files.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_files.output.decode('utf-8').split('\n')
+ print(f"Files found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapTools_annotation_files.zip'
+ ), 202
+
+@bp.route("/create-annotation/report/download", methods=["GET"])
+def annotation_report_download():
+ container = client.containers.get("camera-trap-tools-container")
+
+ memory_file = io.BytesIO()
+ try:
+ report_content, _ = container.get_archive("/app/data/annotation_report.csv")
+ for chunk in report_content:
+ memory_file.write(chunk)
+ except docker.errors.NotFound:
+ return jsonify({'error': 'File not found in the container'}), 404
+ except docker.errors.APIError as e:
+ return jsonify({'error': f'API error: {str(e)}'}), 500
+
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="text/csv",
+ as_attachment=True,
+ download_name='CameraTrapTools_annotation_report.csv'
+ ), 202
+
+@bp.route("/capture-report/download", methods=["POST"])
+def capture_report_download():
+ container = client.containers.get("camera-trap-tools-container")
+ month = request.json['capture-report-month']
+ print(f"Downloading capture report for {month}")
+ memory_file = io.BytesIO()
+ try:
+ report_content, _ = container.get_archive(f"/app/capture_report_{month}.csv")
+ for chunk in report_content:
+ memory_file.write(chunk)
+ except docker.errors.NotFound:
+ return jsonify({'error': 'File not found in the container'}), 404
+ except docker.errors.APIError as e:
+ return jsonify({'error': f'API error: {str(e)}'}), 500
+ except Exception as e:
+ return jsonify({'error': f'Unexpected error: {str(e)}'}), 500
+
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="text/csv",
+ as_attachment=True,
+ download_name=f'CameraTrapTools_capture_report_{month}.csv'
+ ), 202
\ No newline at end of file
diff --git a/appstore/routes/cameraTrapWorkflow.py b/appstore/routes/cameraTrapWorkflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5f7ec495a952ab5baacb565138af41800414e78
--- /dev/null
+++ b/appstore/routes/cameraTrapWorkflow.py
@@ -0,0 +1,360 @@
+from flask import Blueprint, jsonify, send_file, request
+from appstore.utils import get_app_metadata, is_container_running_by_name
+from appstore import app, s3_client
+import docker
+import io
+import zipfile
+import tarfile
+import os
+from botocore.exceptions import ClientError
+import requests
+import time
+
+bp = Blueprint("camera_trap_workflow", __name__, url_prefix="/camera-trap-workflow")
+client = docker.from_env()
+BUCKET_NAME = app.config['S3_BUCKET_NAME']
+S3_FOLDER = 'uploads'
+
+@bp.route("/start", methods=["GET", "POST"])
+def start():
+ start_camera_trap_workflow_endpoint()
+ max_retries = 120
+ retries = 0
+ while retries < max_retries:
+ if is_container_running_by_name('camera-trap-workflow-container'):
+ return jsonify({'status': 'running'}), 200
+ time.sleep(5)
+ retries += 1
+
+ return jsonify({'error': 'Failed to start Camera Trap Workflow', 'status': 'timeout'}), 500
+
+def start_camera_trap_workflow_endpoint():
+ app_metadata = get_app_metadata("CameraTrapWorkflow")
+
+ try:
+ container = client.containers.get("camera-trap-workflow-container")
+ if container.status != "running":
+ container.start()
+ print("Started existing camera-trap-workflow-container")
+ except docker.errors.NotFound:
+ print("No existing camera-trap-workflow-container found, creating a new one")
+ # Login to GitLab container registry
+ client.login(
+ username=app.config['GITLAB_REGISTRY_USERNAME'],
+ password=app.config['GITLAB_ACCESS_TOKEN'],
+ registry=app.config['GITLAB_REGISTRY_URL']
+ )
+ # Pull and run the latest image
+ image = client.images.pull(app_metadata['docker_image'])
+ container = client.containers.run(
+ image.id,
+ name='camera-trap-workflow-container',
+ ports={'3838/tcp': 3838},
+ detach=True
+ )
+
+ return jsonify({"message": "Camera Trap Workflow started", "container_id": container.id}), 202
+
+@bp.route("/upload", methods=["POST"])
+def upload():
+ container = client.containers.get("camera-trap-workflow-container")
+ img_dest_path = '/app/data/raw_images'
+
+ folder_paths = request.form.getlist('images')
+ for folder_path in folder_paths:
+ print('folder path: ', folder_path)
+
+ # Create a in-memory tar archive
+ tar_buffer = io.BytesIO()
+ with tarfile.open(fileobj=tar_buffer, mode='w') as tar:
+ try:
+ # List objects in the S3 folder
+ s3_objects = s3_client.list_objects_v2(Bucket=BUCKET_NAME, Prefix=f"{S3_FOLDER}/{folder_path}/")
+
+ for obj in s3_objects.get('Contents', []):
+ file_key = obj['Key']
+ file_name = os.path.relpath(file_key, f"{S3_FOLDER}/{folder_path}")
+
+ # Create a file-like object for the S3 file
+ file_obj = io.BytesIO()
+ s3_client.download_fileobj(BUCKET_NAME, file_key, file_obj)
+ file_obj.seek(0)
+
+ # Add file to tar archive
+ info = tarfile.TarInfo(name=file_name)
+ info.size = len(file_obj.getvalue())
+ tar.addfile(info, file_obj)
+
+ # After the loop, print out what's been saved to docker
+ print("Files saved to Docker:")
+ for member in tar.getmembers():
+ print(f" - {member.name}")
+
+ except ClientError as e:
+ print(f"Error accessing S3: {e}")
+ return jsonify({'error': 'Failed to access S3'}), 500
+
+ # Reset buffer to the beginning
+ tar_buffer.seek(0)
+
+ # Stream the tar archive to Docker
+ container.exec_run(f"mkdir -p {img_dest_path}/{folder_path}")
+ container.put_archive(f"{img_dest_path}/{folder_path}", tar_buffer.getvalue())
+
+ return jsonify({"message": "Upload completed"}), 200
+
+
+@bp.route("/extract-metadata/process", methods=["POST"])
+def extract_metadata():
+ # OUTPUT: metadata file per site in metadata folder; renamed images in images_renamed folder
+ container = client.containers.get("camera-trap-workflow-container")
+ result = container.exec_run("Rscript /app/R/01_extract_metadata_rename_images.R")
+ return jsonify({
+ "status": "success" if result.exit_code == 0 else "error",
+ "message": "Metadata extraction completed" if result.exit_code == 0 else "Metadata extraction failed",
+ "output": result.output.decode('utf-8')
+ }), 200 if result.exit_code == 0 else 500
+
+@bp.route("/extract-metadata/download", methods=["GET"])
+def download_metadata():
+ container = client.containers.get("camera-trap-workflow-container")
+ metadata_path = "/app/data/metadata"
+ renamed_path = "/app/data/images_renamed"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_metadata = container.exec_run(f"find {metadata_path} -type f")
+ find_renamed = container.exec_run(f"find {renamed_path} -type f")
+ if find_metadata.exit_code != 0 or find_renamed.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ metadata_files = find_metadata.output.decode('utf-8').split('\n')
+ renamed_files = find_renamed.output.decode('utf-8').split('\n')
+ print(f"Metadata files found: {metadata_files}")
+ print(f"Renamed files found: {renamed_files}")
+
+ for file_path in metadata_files + renamed_files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ tar_stream = io.BytesIO(file_data.read())
+ tar = tarfile.open(fileobj=tar_stream)
+ file_content = tar.extractfile(tar.getmembers()[0]).read()
+
+ # add file to zip
+ if file_path.startswith(metadata_path):
+ zip_path = f"metadata/{file_path.replace(f'{metadata_path}/', '')}"
+ else:
+ zip_path = f"images_renamed/{file_path.replace(f'{renamed_path}/', '')}"
+ zip_file.writestr(zip_path, file_content)
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapWorkload_metadata_renamed.zip'
+ ), 202
+
+@bp.route("/auto-classify", methods=["POST"])
+def auto_classify():
+ # OUTPUT: automatic_classification folder
+ container = client.containers.get("camera-trap-workflow-container")
+ result = container.exec_run("Rscript /app/R/02_classify_images_automatically.R")
+ return jsonify({
+ "status": "success" if result.exit_code == 0 else "error",
+ "message": "Automatic classification completed" if result.exit_code == 0 else "Automatic classification failed",
+ "output": result.output.decode('utf-8')
+ }), 200 if result.exit_code == 0 else 500
+
+@bp.route("/auto-classify/download", methods=["GET"])
+def download_auto_classify():
+ container = client.containers.get("camera-trap-workflow-container")
+ target_path = "/app/data/automatic_classification"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_files = container.exec_run(f"find {target_path} -type f")
+ if find_files.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_files.output.decode('utf-8').split('\n')
+ print(f"Files found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapWorkload_auto_classification.zip'
+ ), 202
+
+@bp.route("/quality-check", methods=["POST"])
+def quality_check():
+ # OUTPUT: A file with labels in quality_check folder
+ container = client.containers.get("camera-trap-workflow-container")
+ container.exec_run("Rscript /app/R/03_quality_check_app.R 3838")
+ shiny_url = "http://0.0.0.0:3838"
+ # Check if Shiny app is ready
+ max_retries = 30
+ retry_interval = 2
+ for _ in range(max_retries):
+ try:
+ response = requests.get(shiny_url, timeout=5, verify=False)
+ if response.status_code == 200:
+ return jsonify({
+ "status": "success",
+ "message": "Quality check Shiny app is ready",
+ "shiny_url": shiny_url
+ }), 200
+ except requests.RequestException:
+ pass
+ time.sleep(retry_interval)
+ # If we've exhausted all retries, return an error
+ return jsonify({
+ "status": "error",
+ "message": "Failed to start Quality check Shiny app",
+ }), 500
+
+@bp.route("/quality-check/download", methods=["GET"])
+def download_quality_check():
+ container = client.containers.get("camera-trap-workflow-container")
+ target_path = "/app/data/quality_check"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_files = container.exec_run(f"find {target_path} -type f")
+ if find_files.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_files.output.decode('utf-8').split('\n')
+ print(f"Files found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapWorkload_quality_check.zip'
+ ), 202
+
+@bp.route("/modal-evaluation", methods=["POST"])
+def modal_evaluation():
+ container = client.containers.get("camera-trap-workflow-container")
+ result = container.exec_run("Rscript /app/R/04_model_evaluation.R")
+ return jsonify({
+ "status": "success" if result.exit_code == 0 else "error",
+ "message": "Modal evaluation completed" if result.exit_code == 0 else "Modal evaluation failed",
+ "output": result.output.decode('utf-8')
+ }), 200 if result.exit_code == 0 else 500
+
+@bp.route("/manual-classify", methods=["POST"])
+def manual_classify():
+ # OUTPUT: manual_classification folder
+ container = client.containers.get("camera-trap-workflow-container")
+ container.exec_run("Rscript /app/R/05_correct_model_labels_app.R 3838")
+ shiny_url = "http://0.0.0.0:3838"
+ # Check if Shiny app is ready
+ max_retries = 30
+ retry_interval = 2
+ for _ in range(max_retries):
+ try:
+ response = requests.get(shiny_url, timeout=5, verify=False)
+ if response.status_code == 200:
+ return jsonify({
+ "status": "success",
+ "message": "Manual classification Shiny app is ready",
+ "shiny_url": shiny_url
+ }), 200
+ except requests.RequestException:
+ pass
+ time.sleep(retry_interval)
+ return jsonify({
+ "status": "error",
+ "message": "Failed to start Manual classification Shiny app",
+ }), 500
+
+@bp.route("/formatting", methods=["POST"])
+def formatting():
+ # OUTPUT: formatted_data folder
+ container = client.containers.get("camera-trap-workflow-container")
+ result = container.exec_run("Rscript /app/R/06_format_final_data_file.R")
+ return jsonify({
+ "status": "success" if result.exit_code == 0 else "error",
+ "message": "Final formatting completed" if result.exit_code == 0 else "Final formatting failed",
+ "output": result.output.decode('utf-8')
+ }), 200 if result.exit_code == 0 else 500
+
+@bp.route("/formatting/download", methods=["GET"])
+def download_formatting():
+ container = client.containers.get("camera-trap-workflow-container")
+ target_path = "/app/data/formatted_data"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ find_files = container.exec_run(f"find {target_path} -type f")
+ if find_files.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = find_files.output.decode('utf-8').split('\n')
+ print(f"Files found: {files}")
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='CameraTrapWorkload_final_formatting.zip'
+ ), 202
\ No newline at end of file
diff --git a/appstore/routes/dataSpace.py b/appstore/routes/dataSpace.py
new file mode 100644
index 0000000000000000000000000000000000000000..19d43ce15b422c44b8c553e4aaf96702a1bf1dbe
--- /dev/null
+++ b/appstore/routes/dataSpace.py
@@ -0,0 +1,117 @@
+from flask import Blueprint, jsonify, request
+from appstore import app, s3_client
+from botocore.exceptions import ClientError
+import mimetypes
+
+bp = Blueprint('dataSpace', __name__, url_prefix='/dataSpace')
+
+@bp.route('/upload', methods=['POST'])
+def upload():
+ bucket_name = app.config['S3_BUCKET_NAME']
+ s3_folder = 'uploads/' if not request.form.get('folder') else 'uploads/' + request.form.get('folder') # Get folder from request, default to 'uploads/'
+ files = request.files.getlist('files')
+ print('Files to upload: ', files)
+
+ uploaded_files = []
+ try:
+ for file in files:
+ if file.filename == '':
+ continue
+
+ file_key = s3_folder + file.filename
+ print('Uploading file: ', file_key)
+ s3_client.upload_fileobj(file, bucket_name, file_key)
+ uploaded_files.append(file_key)
+
+ return jsonify({
+ 'message': 'Files uploaded successfully to S3',
+ 'uploaded_files': uploaded_files
+ }), 200
+
+ except ClientError as e:
+ print(f"Error uploading to S3: {str(e)}")
+ return jsonify({'error': 'Failed to upload files to S3'}), 500
+
+def get_s3_file_structure(bucket_name, prefix=''):
+ file_structure = {}
+ try:
+ response = s3_client.list_objects_v2(Bucket=bucket_name, Prefix=prefix, Delimiter='/')
+ if 'CommonPrefixes' in response:
+ for folder in response['CommonPrefixes']:
+ folder_name = folder['Prefix'].split('/')[-2]
+ file_structure[folder_name] = get_s3_file_structure(bucket_name, folder['Prefix'])
+ if 'Contents' in response:
+ file_structure['files'] = [{'name': obj['Key'].split('/')[-1], 'url': f"https://{bucket_name}.s3.amazonaws.com/{obj['Key']}", 'path': obj['Key']} for obj in response['Contents'] if obj['Key'] != prefix]
+ return file_structure
+ except ClientError as e:
+ print(f"Error fetching file structure from S3: {str(e)}")
+ return {}
+
+@bp.route('/list_files', methods=['GET'])
+def list_files():
+ bucket_name = app.config['S3_BUCKET_NAME']
+ s3_folder = 'uploads/'
+
+ try:
+ file_structure = get_s3_file_structure(bucket_name, s3_folder)
+ response = s3_client.list_objects_v2(Bucket=bucket_name, Prefix=s3_folder)
+ files = []
+ for obj in response.get('Contents', []):
+ file_url = f"https://{bucket_name}.s3.amazonaws.com/{obj['Key']}"
+ file_name = obj['Key'].replace(s3_folder, '')
+ file_extension = file_name.split('.')[-1].lower()
+
+ # Determine the MIME type
+ mime_type, _ = mimetypes.guess_type(file_name)
+ if mime_type:
+ file_category = mime_type.split('/')[0] # This will give 'image', 'video', etc.
+ else:
+ # Fallback to a simple check based on extension
+ if file_extension in ['jpg', 'jpeg', 'png', 'gif', 'bmp', 'webp', 'tiff']:
+ file_category = 'image'
+ elif file_extension in ['mp4', 'mov', 'avi', 'mkv', 'webm', 'flv', 'wmv']:
+ file_category = 'video'
+ else:
+ file_category = 'other'
+
+ file_content = s3_client.generate_presigned_url('get_object', Params={'Bucket': bucket_name, 'Key': obj['Key']}, ExpiresIn=3600)
+ file_upload_date = obj['LastModified']
+ files.append({
+ 'name': file_name,
+ 'url': file_url,
+ 'type': file_extension.upper(), # Original file type for display
+ 'category': file_category, # New field for grouping
+ 'extension': file_extension,
+ 'content': file_content,
+ 'uploadDate': file_upload_date,
+ 'path': obj['Key']
+ })
+
+ return jsonify({'files': files, 'file_structure': file_structure}), 200
+
+ except ClientError as e:
+ print(f"Error listing files from S3: {str(e)}")
+ return jsonify({'error': 'Failed to list files from S3'}), 500
+
+@bp.route('/delete', methods=['DELETE'])
+def delete_file():
+ bucket_name = app.config['S3_BUCKET_NAME']
+ file_path = request.args.get('path')
+ if not file_path:
+ return jsonify({'error': 'No file path provided'}), 400
+
+ try:
+ s3_client.delete_object(Bucket=bucket_name, Key=file_path)
+ return jsonify({'message': f'File {file_path} deleted successfully'}), 200
+ except Exception as e:
+ return jsonify({'error': str(e)}), 500
+
+# In Data Space page
+# 1. Receive images from UI and upload to S3
+# 2. Fetch files from S3
+# 3. Delete files from S3
+# 4. Move files between folders / Upload files to a specific folder
+
+# In each application
+# 1. Open window/popup for images selection (from Data Space)
+# 2. Get images from S3
\ No newline at end of file
diff --git a/appstore/routes/ecosecrets.py b/appstore/routes/ecosecrets.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e14f58fbafe797dd8607dbaf6b6aa8f8b69d74e
--- /dev/null
+++ b/appstore/routes/ecosecrets.py
@@ -0,0 +1,61 @@
+from flask import Blueprint, jsonify
+from appstore.utils import get_app_metadata, is_container_running, is_server_ready
+import os
+import git
+import shutil
+import subprocess
+import time
+
+bp = Blueprint('ecosecrets', __name__, url_prefix='/ecosecrets')
+
+@bp.route('/start', methods=['GET', 'POST'])
+def start_ecosecrets():
+ start_ecosecrets_endpoint()
+
+ max_retries = 100
+ retries = 0
+ ecosecrets_url = "http://localhost:8889/"
+ while retries < max_retries:
+ if is_container_running('traefik') and is_server_ready(ecosecrets_url):
+ return jsonify({'status': 'running', 'url': ecosecrets_url}), 200
+ time.sleep(5)
+ retries += 1
+
+ return jsonify({'status': 'timeout'}), 500
+
+def start_ecosecrets_endpoint():
+ app_metadata = get_app_metadata("EcoSecrets")
+ repo_dir = os.path.join(os.path.dirname(__file__), '..', 'repos', 'ecosecrets')
+
+ try:
+ # clone the repository if doesn't exist
+ if not os.path.exists(repo_dir):
+ git.Repo.clone_from(app_metadata["repository_url"], repo_dir)
+ os.chdir(repo_dir)
+
+ # check if .env file exists
+ if not os.path.exists("docker/.env"):
+ if os.path.exists("docker/.env.sample"):
+ shutil.copy("docker/.env.sample", "docker/.env")
+ print("Created .env file from .env.sample")
+ else:
+ print("No .env.sample file found")
+ return False
+ else:
+ print(".env file already exists")
+
+ # run the start command
+ command = app_metadata["start_command"].split()
+ print(f"Executing command: {' '.join(command)}")
+ result = subprocess.Popen(command, cwd=repo_dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+ print(f"Command exit code: {result.returncode}")
+ print(f"Command stdout: {result.stdout}")
+ print(f"Command stderr: {result.stderr}")
+
+ if result.returncode != 0:
+ return jsonify({'error': 'EcoSecrets start command failed', 'details': result.stderr}), 500
+
+ return jsonify({'message': 'EcoSecrets start initiated'}), 202
+
+ except Exception as e:
+ return jsonify({'error': 'Failed to start EcoSecrets', 'details': str(e)}), 500
\ No newline at end of file
diff --git a/appstore/routes/il2bb.py b/appstore/routes/il2bb.py
new file mode 100644
index 0000000000000000000000000000000000000000..32182ebc34ca8cbc554117a9fd9f658f34644659
--- /dev/null
+++ b/appstore/routes/il2bb.py
@@ -0,0 +1,200 @@
+from flask import Blueprint, jsonify, request, send_file
+from werkzeug.utils import secure_filename
+from appstore import app, s3_client
+from appstore.utils import get_app_metadata, is_container_running_by_name, is_server_ready
+import docker
+import os
+import tarfile
+import io
+import time
+import zipfile
+
+bp = Blueprint("il2bb", __name__, url_prefix="/il2bb")
+client = docker.from_env()
+BUCKET_NAME = app.config['S3_BUCKET_NAME']
+S3_FOLDER = 'uploads'
+
+@bp.route("/start", methods=["GET", "POST"])
+def start_l2bb():
+ start_il2bb_endpoint()
+
+ max_retries = 60
+ retries = 0
+ # il2bb_url = "http://127.0.0.1:3001/il2bb"
+ while retries < max_retries:
+ if is_container_running_by_name('il2bb-container'):
+ return jsonify({'status': 'running'}), 200
+ time.sleep(5)
+ retries += 1
+
+ return jsonify({'error': 'Failed to start IL2BB', 'status': 'timeout'}), 500
+
+def start_il2bb_endpoint():
+ app_metadata = get_app_metadata("IL2BB")
+
+ try:
+ container = client.containers.get("il2bb-container")
+ if container.status != "running":
+ container.start()
+ print("Started existing il2bb-container")
+ except docker.errors.NotFound:
+ print("No existing il2bb-container found, creating a new one")
+ # Login to GitLab container registry
+ client.login(
+ username=app.config['GITLAB_REGISTRY_USERNAME'],
+ password=app.config['GITLAB_ACCESS_TOKEN'],
+ registry=app.config['GITLAB_REGISTRY_URL']
+ )
+ # Pull and run the latest image
+ image = client.images.pull(app_metadata['docker_image'])
+ container = client.containers.run(
+ image.id,
+ name='il2bb-container',
+ detach=True
+ )
+
+ return jsonify({"message": "IL2BB started", "container_id": container.id}), 202
+
+@bp.route("/upload", methods=["POST"])
+def upload_il2bb():
+ container = client.containers.get("il2bb-container")
+ img_src_path = os.path.join(os.path.dirname(__file__), '..', app.config['IL2BB_UPLOAD_FOLDER'])
+ img_dest_path = '/app/data/images'
+ mapping_src_path = os.path.join(os.path.dirname(__file__), '..', app.config['IL2BB_MAIN_FOLDER'])
+ mapping_dest_path = '/app/data'
+
+ # Save mapping CSV file to local directory
+ mappingCsv = request.files.get('mappingFile')
+ mappingCsvFilename = None
+ if mappingCsv and mappingCsv.filename != '':
+ mappingCsvFilename = secure_filename(mappingCsv.filename)
+ mappingCsv.save(os.path.join(mapping_src_path, mappingCsvFilename))
+
+ # Save image files to local directory
+ uploaded_images = []
+ image_paths = request.form.getlist('images')
+ for s3_path in image_paths:
+ try:
+ # Extract the filename from the S3 path
+ filename = os.path.basename(s3_path)
+ local_file_path = os.path.join(img_src_path, filename)
+ print("Local file path: ", local_file_path)
+
+ # Download the file from S3
+ s3_client.download_file(BUCKET_NAME, s3_path, local_file_path)
+
+ if os.path.exists(local_file_path):
+ uploaded_images.append(filename)
+ else:
+ return jsonify({"status": "error", "message": f"Failed to download {filename} from S3"})
+ except Exception as e:
+ return jsonify({"status": "error", "message": f"Error processing {s3_path}: {str(e)}"})
+
+ if len(uploaded_images) == len(image_paths):
+ print("Images downloaded from S3")
+ else:
+ print("Some images failed to download from S3. Files: ", uploaded_images)
+
+ # Create a tar archive of the uploaded files
+ tar_stream = io.BytesIO()
+ with tarfile.open(fileobj=tar_stream, mode='w') as tar:
+ for filename in os.listdir(img_src_path):
+ tar.add(os.path.join(img_src_path, filename), arcname=filename)
+ tar_stream.seek(0)
+
+ tar_stream_2 = io.BytesIO()
+ with tarfile.open(fileobj=tar_stream_2, mode='w') as tar:
+ tar.add(os.path.join(mapping_src_path, mappingCsvFilename), arcname=mappingCsvFilename)
+ tar_stream_2.seek(0)
+
+ # Copy the tar archive to the container
+ container.put_archive(img_dest_path, tar_stream)
+ container.put_archive(mapping_dest_path, tar_stream_2)
+ return jsonify({
+ "status": "success",
+ "message": "Images and mapping CSV file uploaded",
+ }), 202
+
+@bp.route("/trigger_stage1", methods=["POST"])
+def trigger_stage1():
+ # Stage 1: Create images batches
+ labelMap = request.get_data(as_text=True) # get filename
+
+ container = client.containers.get("il2bb-container")
+
+ stage1_result = container.exec_run("python stage1_batch.py ./data/" + labelMap + " ./data/images ./data/batches")
+ print(f"Stage 1 result: {stage1_result.output.decode('utf-8')}")
+
+ # Check if the stage1_batch.py script has finished and batches created
+ max_wait_time = 60
+ start_time = time.time()
+ while time.time() - start_time < max_wait_time:
+ try:
+ batch_content = container.exec_run("ls ./data/batches/batch_001")
+ if batch_content.exit_code == 0 and batch_content.output:
+ print("Stage 1 completed, now starting Stage 2")
+ # trigger_stage2()
+ return jsonify({'status': 'success', 'message': 'Stage 1 completed.'}), 202
+ except docker.errors.NotFound:
+ time.sleep(2)
+
+ return jsonify({'status': 'error', 'message': 'Failed to trigger Stage 1.'}), 500
+
+@bp.route("/trigger_stage2", methods=["POST"])
+def trigger_stage2():
+ # Stage 2: Create bounding boxes
+ # sample command: python ../stage2_gen_bbox.py batches/batch_001/
+ container = client.containers.get("il2bb-container")
+ stage2_result = container.exec_run("python stage2_gen_bbox.py ./data/batches/batch_001/")
+ print(f"Stage 2 result: {stage2_result.output.decode('utf-8')}")
+
+ max_wait_time = 180
+ start_time = time.time()
+ while time.time() - start_time < max_wait_time:
+ try:
+ # output = container.get_archive("./data/batches/batch_001/batch_001_il2bb.bbx")
+ output = container.exec_run("ls -l ./data/batches/batch_001")
+ if 'batch_001_il2bb.bbx' in output.output.decode('utf-8'):
+ return jsonify({'status': 'success', 'message': 'Stage 2 completed.'}), 202
+ except docker.errors.NotFound:
+ time.sleep(2)
+
+ return jsonify({'status': 'error', 'message': 'Failed to trigger Stage 2.'}), 202
+
+@bp.route("/download", methods=["GET"])
+def download():
+ container = client.containers.get("il2bb-container")
+ target_path = "/app/data/batches/batch_001"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ exec_result = container.exec_run(f"find {target_path} -type f")
+ if exec_result.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = exec_result.output.decode('utf-8').split('\n')
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='IL2BB_batch_result.zip'
+ ), 202
\ No newline at end of file
diff --git a/appstore/routes/main.py b/appstore/routes/main.py
index d68ef7ece58de55ff178850da0b778ecf4652b80..7c475f0e2b6c870e8f2c51424268539c160e8f65 100644
--- a/appstore/routes/main.py
+++ b/appstore/routes/main.py
@@ -1,13 +1,77 @@
-from flask import Blueprint, render_template, jsonify, send_from_directory, current_app
-from appstore import celery
+from flask import Blueprint, jsonify, send_from_directory, current_app, request, session
+from appstore import celery, bcrypt, db
+from appstore.models import User, get_uuid, AppMetadata
+from flask_login import login_user, logout_user, current_user, login_required
import os
+import docker
+from datetime import datetime
+import pytz
bp = Blueprint('main', __name__)
-@bp.route('/')
-@bp.route('/home')
-def home():
- return render_template('index.html')
+@bp.route('/login', methods=['POST'])
+def login():
+ username = request.json['username']
+ password = request.json['password']
+ user = User.query.filter_by(username=username).first()
+
+ if not user or not bcrypt.check_password_hash(user.password, password):
+ return jsonify({'message': 'Invalid username or password'}), 401
+
+ login_user(user)
+
+ return jsonify({
+ 'status': 'success',
+ 'message': 'Login successful',
+ 'id': user.id,
+ 'username': user.username
+ }), 200
+
+@bp.route('/logout', methods=['POST'])
+@login_required
+def logout():
+ logout_user()
+ return jsonify({'message': 'Logout successful'}), 200
+
+@bp.route('/register', methods=['POST'])
+def register():
+ formData = request.form
+ username = formData['username']
+ password = formData['password']
+ email = formData['email']
+ organisation = formData['organisation']
+ hashed_password = bcrypt.generate_password_hash(password)
+
+ user_exists = User.query.filter_by(username=username).first()
+ if user_exists:
+ return jsonify({'message': 'User already exists'}), 400
+
+ new_user_id = get_uuid() # Import get_uuid from models.py
+ new_user = User(id=new_user_id, username=username, password=hashed_password, email=email, organisation=organisation)
+ db.session.add(new_user)
+ db.session.commit()
+
+ session['user_id'] = new_user_id
+
+ return jsonify({
+ 'message': 'Registration successful',
+ 'id': new_user_id,
+ 'username': username
+ }), 200
+
+@bp.route('/user', methods=['GET'])
+@login_required
+def get_user():
+ if current_user.is_authenticated:
+ return jsonify({
+ 'id': current_user.id,
+ 'username': current_user.username,
+ 'email': current_user.email,
+ 'organisation': current_user.organisation,
+ 'title': current_user.title
+ }), 200
+ else:
+ return jsonify({'message': 'Not authenticated'}), 401
@bp.route('/task_status/<task_id>', methods=['GET'])
def task_status(task_id):
@@ -19,14 +83,99 @@ def task_status(task_id):
}
return jsonify(data)
-# uncatergorised routes
-@bp.route('/Users/<path:filepath>')
-def serve_user_files(filepath):
- # This route handles the absolute paths in the HTML
- if 'static' in filepath:
- relative_path = filepath.split('static/')[-1]
- return send_from_directory(os.path.join(current_app.root_path, 'static'), relative_path)
- elif 'templates' in filepath:
- relative_path = filepath.split('templates/')[-1]
- return send_from_directory(os.path.join(current_app.root_path, 'templates'), relative_path)
- return "File not found", 404
\ No newline at end of file
+@bp.route('/profile/update', methods=['POST'])
+@login_required
+def update_user():
+ data = request.json
+ print('Data received: ', data)
+ current_user.username = data.get('username', current_user.username)
+ current_user.email = data.get('email', current_user.email)
+ current_user.organisation = data.get('organisation', current_user.organisation)
+ current_user.title = data.get('title', current_user.title)
+ try:
+ db.session.commit()
+ return jsonify({'message': 'User updated successfully'}), 200
+ except Exception as e:
+ db.session.rollback()
+ return jsonify({'message': f'Error updating user: {str(e)}'}), 500
+
+@bp.route('/apps', methods=['GET'])
+def get_apps():
+ apps = AppMetadata.query.all()
+ apps_list = []
+ for app in apps:
+ apps_list.append({
+ 'id': app.id,
+ 'name': app.name,
+ 'display_name': app.display_name,
+ 'description': app.description,
+ 'full_description': app.full_description,
+ 'logo': app.logo,
+ 'image': app.image,
+ 'link': app.link,
+ 'tags': app.tags,
+ 'repository_url': app.repository_url
+ })
+ return jsonify(apps_list), 200
+
+@bp.route('/apps/usage', methods=['GET'])
+def get_apps_usage():
+ client = docker.from_env()
+ containers = client.containers.list(all=True)
+ usage_data = {}
+ total_runtime = 0
+
+ def parse_docker_time(time_str):
+ # Remove nanoseconds
+ time_str = time_str.split('.')[0] + 'Z'
+ return datetime.strptime(time_str, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=pytz.UTC)
+
+ for container in containers:
+ container_info = container.attrs
+ labels = container_info['Config']['Labels']
+
+ # Check if the container is part of a stack
+ if 'com.docker.compose.project' in labels:
+ app_name = labels['com.docker.compose.project']
+ else:
+ app_name = container_info['Name'].strip('/')
+
+ state = container_info['State']
+
+ if state['Status'] == 'running':
+ start_time = parse_docker_time(state['StartedAt'])
+ current_time = datetime.now(pytz.utc)
+ runtime = (current_time - start_time).total_seconds()
+ else:
+ if state['FinishedAt'] != '0001-01-01T00:00:00Z':
+ start_time = parse_docker_time(state['StartedAt'])
+ end_time = parse_docker_time(state['FinishedAt'])
+ runtime = (end_time - start_time).total_seconds()
+ else:
+ runtime = 0
+
+ if app_name not in usage_data:
+ usage_data[app_name] = {
+ 'runtime_seconds': 0,
+ 'status': 'running' if state['Status'] == 'running' else 'stopped'
+ }
+
+ usage_data[app_name]['runtime_seconds'] += runtime
+ if state['Status'] == 'running':
+ usage_data[app_name]['status'] = 'running'
+
+ total_runtime += runtime
+
+ # Convert dictionary to list for JSON serialization
+ usage_list = [
+ {
+ 'app_name': app_name,
+ 'runtime_seconds': data['runtime_seconds'],
+ 'status': data['status']
+ } for app_name, data in usage_data.items()
+ ]
+
+ return jsonify({
+ 'usage_data': usage_list,
+ 'total_runtime': total_runtime
+ }), 200
diff --git a/appstore/routes/megadetector.py b/appstore/routes/megadetector.py
index 82aa7ab54775c5e69bec1456644f73f84e94df44..3fbdc3151eb5eb1c3e93ee92f8b6417c77648bbc 100644
--- a/appstore/routes/megadetector.py
+++ b/appstore/routes/megadetector.py
@@ -1,5 +1,5 @@
-from flask import Blueprint, request, render_template, jsonify, send_file, send_from_directory, current_app
-from appstore import app
+from flask import Blueprint, request, jsonify, send_from_directory, current_app
+from appstore import app, s3_client
from megadetector.detection.run_detector_batch import load_and_run_detector_batch, write_results_to_file
from megadetector.postprocessing.postprocess_batch_results import process_batch_results, PostProcessingOptions
from megadetector.utils import path_utils
@@ -7,17 +7,39 @@ import os
import subprocess
bp = Blueprint('megadetector', __name__, url_prefix='/megadetector')
+BUCKET_NAME = app.config['S3_BUCKET_NAME']
+S3_FOLDER = 'uploads'
-@bp.route('/', methods=['GET', 'POST'])
-def megadetector():
- if request.method == 'POST':
- # image = request.files['image']
- images = request.files.getlist('image')
- for image in images:
- image.save(os.path.join(os.path.dirname(__file__), '..', app.config['MEGADETECTOR_UPLOAD_FOLDER'], image.filename))
- return render_template('megadetector.html')
+@bp.route('/upload', methods=['POST'])
+def megadetector_upload():
+ if not request.form:
+ return jsonify({'error': 'Missing required files'}), 400
+
+ image_paths = request.form.getlist('image')
+ upload_folder = os.path.join(os.path.dirname(__file__), '..', app.config['MEGADETECTOR_UPLOAD_FOLDER'])
+
+ uploaded_files = []
+ for s3_path in image_paths:
+ try:
+ # Extract the filename from the S3 path
+ filename = os.path.basename(s3_path)
+ local_file_path = os.path.join(upload_folder, filename)
+ print("Local file path: ", local_file_path)
+
+ # Download the file from S3
+ s3_client.download_file(BUCKET_NAME, s3_path, local_file_path)
+
+ if os.path.exists(local_file_path):
+ uploaded_files.append(filename)
+ else:
+ return jsonify({"status": "error", "message": f"Failed to download {filename} from S3"})
+ except Exception as e:
+ return jsonify({"status": "error", "message": f"Error processing {s3_path}: {str(e)}"})
+
+ if len(uploaded_files) == len(image_paths):
+ return jsonify({"status": "success", "message": "Images downloaded from S3", "files": uploaded_files})
else:
- return render_template('megadetector.html')
+ return jsonify({"status": "partial_success", "message": "Some images failed to download from S3", "files": uploaded_files})
@bp.route('/run/batch', methods=['POST'])
def megadetector_run_batch():
@@ -25,6 +47,7 @@ def megadetector_run_batch():
image_folder = os.path.join(os.path.dirname(__file__), '..', app.config['MEGADETECTOR_UPLOAD_FOLDER'])
output_file = os.path.join(os.path.dirname(__file__), '..', app.config['MEGADETECTOR_MAIN_FOLDER'], 'output.json')
model = request.form.get('model')
+ print("Model: ", model)
# Install TensorFlow using pip if the model is MDV4 - may break things now
if model == 'MDV4':
@@ -55,11 +78,15 @@ def postprocess_results(output_file, image_folder):
@bp.route('/report')
def megadetector_report():
- return render_template('megadetector-results/index.html')
+ with open(os.path.join(current_app.root_path, 'templates', 'megadetector-results', 'index.html'), 'r') as file:
+ html_content = file.read()
+ return jsonify({"html_content": html_content})
@bp.route('/detections_animal.html')
def megadetector_detections_animal_report():
- return render_template('megadetector-results/detections_animal.html')
+ with open(os.path.join(current_app.root_path, 'templates', 'megadetector-results', 'detections_animal.html'), 'r') as file:
+ html_content = file.read()
+ return jsonify({"html_content": html_content})
@bp.route('/detections_animal/<path:filename>')
def serve_detections_animal(filename):
diff --git a/appstore/routes/trapper.py b/appstore/routes/trapper.py
index 367f4368f2b4a438e303116e833ac65d569b388a..20fb5bfacbc8c6e1e53e6eecab3307f8fab9e671 100644
--- a/appstore/routes/trapper.py
+++ b/appstore/routes/trapper.py
@@ -6,66 +6,72 @@ import docker
import git
import os
import shutil
+import logging
+import socket
+from pathlib import Path
bp = Blueprint('trapper', __name__, url_prefix='/trapper')
+# Set up logging at the beginning of your script
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
@bp.route('/start', methods=['POST'])
def start_trapper():
+ internal_url = "http://trapper:8000/"
+ external_url = "http://localhost:8000/"
+ if is_server_ready(internal_url) or is_container_running('trapper'):
+ return jsonify({'status': 'running', 'url': external_url}), 200
+
+ print("Starting Trapper")
start_trapper_endpoint()
max_retries = 60
retries = 0
- trapper_url = "http://0.0.0.0:8000/"
while retries < max_retries:
- if is_container_running('trapper') and is_server_ready(trapper_url):
- return jsonify({'status': 'running', 'url': trapper_url}), 200
+ if is_container_running('trapper') and is_server_ready(internal_url):
+ return jsonify({'status': 'running', 'url': external_url}), 200
time.sleep(5)
retries += 1
return jsonify({'error': 'Failed to start Trapper', 'status': 'timeout'}), 500
def start_trapper_endpoint():
+ client = docker.from_env()
app_metadata = get_app_metadata("Trapper")
- repo_dir = f"/tmp/trapper"
+ trapper_dir = Path('/app/appstore/repos/trapper')
+ # trapper_dir = os.path.join(os.path.dirname(__file__), '..', 'repos', 'trapper') # for local testing
+ logger.info(f"Trapper directory: {trapper_dir}")
- try:
- # clone the repository if doesn't exist
- print("Checking if repository exists")
- if not os.path.exists(repo_dir):
- print("Cloning repository")
- git.Repo.clone_from(app_metadata["repository_url"], repo_dir)
- print("Repository eixsts: ", repo_dir)
- os.chdir(repo_dir)
-
- # Check if .env file exists
- # TODO: amend Dockerfile.dev (add -o to line 53)
- if not os.path.exists(".env"):
- if os.path.exists("trapper.env"):
- shutil.copy("trapper.env", ".env")
- print("Created .env file from trapper.env")
- else:
- print("No trapper.env file found")
- return False
+ if not os.path.exists(trapper_dir):
+ logger.error(f"Trapper directory not found: {trapper_dir}")
+ return jsonify({'error': 'Trapper directory not found'}), 500
+
+ os.chdir(trapper_dir)
+
+ # Ensure .env file exists
+ if not os.path.exists(".env"):
+ if os.path.exists("trapper.env"):
+ shutil.copy("trapper.env", ".env")
+ logger.info("Created .env file from trapper.env")
else:
- print(".env file already exists")
-
- # Run the start command
- print("Current directory:", os.getcwd())
- command = app_metadata["start_command"].split() # Split the command into a list
- print(f"Executing command: {' '.join(command)}")
- result = subprocess.Popen(command, cwd=repo_dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-
- print(f"Command exit code: {result.returncode}")
- print(f"Command stdout: {result.stdout}")
- print(f"Command stderr: {result.stderr}")
-
- if result.returncode != 0:
- return jsonify({'error': 'Trapper start command failed', 'details': result.stderr}), 500
-
- return jsonify({'message': 'Trapper start initiated'}), 202
- except Exception as e:
- print(f"Exception occurred: {str(e)}")
- return jsonify({'error': 'Failed to start Trapper', 'details': str(e)}), 500
+ logger.error("No .env or trapper.env file found")
+ return jsonify({'error': 'No .env file found'}), 500
+
+ # Build and start Trapper services
+ command = app_metadata['start_command'].split()
+ logger.info(f"Executing command: {' '.join(command)}")
+
+ result = subprocess.run(command, cwd=trapper_dir, capture_output=True, text=True)
+
+ logger.info(f"Command exit code: {result.returncode}")
+ logger.info(f"Command stdout: {result.stdout}")
+ logger.info(f"Command stderr: {result.stderr}")
+
+ if result.returncode != 0:
+ return jsonify({'error': 'Trapper start command failed', 'details': result.stderr}), 500
+
+ return jsonify({'message': 'Trapper start initiated'}), 202
@bp.route('/logs', methods=['GET'])
def get_trapper_log():
@@ -74,4 +80,4 @@ def get_trapper_log():
logs = container.logs(tail=100)
with open('trapper.log', 'wb') as logFile:
logFile.write(logs)
- return logs.decode('utf-8')
\ No newline at end of file
+ return logs.decode('utf-8')
diff --git a/appstore/routes/wildCoFaceBlur.py b/appstore/routes/wildCoFaceBlur.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c2d8ce806cd7d48378f4ebaa41af3f5491c3449
--- /dev/null
+++ b/appstore/routes/wildCoFaceBlur.py
@@ -0,0 +1,183 @@
+from flask import Blueprint, jsonify, send_file, request
+from appstore import app, s3_client
+from appstore.utils import get_app_metadata, is_container_running_by_name
+import docker
+import io
+import tarfile
+import zipfile
+import os
+import time
+from botocore.exceptions import ClientError
+
+bp = Blueprint('wildCoFaceBlur', __name__, url_prefix='/wildCoFaceBlur')
+client = docker.from_env()
+
+@bp.route('/start', methods=['POST'])
+def start():
+ start_wildcofaceblur_endpoint()
+ max_retries = 120
+ retries = 0
+ while retries < max_retries:
+ if is_container_running_by_name('wildco-faceblur-container'):
+ return jsonify({'status': 'running'}), 200
+ time.sleep(5)
+ retries += 1
+
+ return jsonify({'error': 'Failed to start WildCo-FaceBlur', 'status': 'timeout'}), 500
+
+def start_wildcofaceblur_endpoint():
+ app_metadata = get_app_metadata("WildCoFaceBlur")
+
+ try:
+ container = client.containers.get("wildco-faceblur-container")
+ if container.status != "running":
+ container.start()
+ print("Started existing wildco-faceblur-container")
+ except docker.errors.NotFound:
+ print("No existing wildco-faceblur-container found, creating a new one")
+ # Login to GitLab container registry
+ client.login(
+ username=app.config['GITLAB_REGISTRY_USERNAME'],
+ password=app.config['GITLAB_ACCESS_TOKEN'],
+ registry=app.config['GITLAB_REGISTRY_URL']
+ )
+ # Pull and run the latest image
+ image = client.images.pull(app_metadata['docker_image'])
+ container = client.containers.run(
+ image.id,
+ name='wildco-faceblur-container',
+ detach=True
+ )
+
+ return jsonify({"message": "WildCo-FaceBlur started", "container_id": container.id}), 202
+
+@bp.route('/trigger_script', methods=['POST'])
+def trigger_script():
+ data = request.json
+ date_folders = data.get('dateFolders', 'false')
+ blur_level = data.get('blurLevel', '7')
+ conf_threshold = data.get('confThreshold', '0.25')
+
+ container = client.containers.get("wildco-faceblur-container")
+ command = container.exec_run(f"Rscript WildCoFaceBlur.R {date_folders} {blur_level} {conf_threshold}")
+ result = command.output.decode('utf-8')
+ print("result", result)
+ return jsonify({"result": result, "message": "WildCo-FaceBlur script triggered"}), 202
+
+@bp.route('/download', methods=['GET'])
+def download():
+ container = client.containers.get("wildco-faceblur-container")
+ target_path = "/app/output"
+
+ memory_file = io.BytesIO()
+ with zipfile.ZipFile(memory_file, 'w') as zip_file:
+ exec_result = container.exec_run(f"find {target_path} -type f")
+ if exec_result.exit_code != 0:
+ return jsonify({'error': 'Failed to list files in the container'}), 500
+
+ files = exec_result.output.decode('utf-8').split('\n')
+
+ for file_path in files:
+ if file_path.strip():
+ try:
+ file_data, _ = container.get_archive(file_path)
+ file_content = io.BytesIO()
+ for chunk in file_data:
+ file_content.write(chunk)
+ file_content.seek(0)
+
+ # add file to zip
+ zip_file.writestr(file_path.replace(f"{target_path}/", ""), file_content.getvalue())
+ except docker.errors.NotFound:
+ print(f"File not found in the container: {file_path}")
+ except docker.errors.APIError as e:
+ print(f"API error getting archive for {file_path}: {str(e)}")
+
+ memory_file.seek(0)
+
+ return send_file(
+ memory_file,
+ mimetype="application/zip",
+ as_attachment=True,
+ download_name='WildCoFaceBlur_output.zip'
+ ), 202
+
+@bp.route('/upload', methods=['POST'])
+def upload():
+ if not request.form or not request.files:
+ return jsonify({'error': 'Missing required files'}), 400
+ print("Form data:", request.form)
+ print("Files:", request.files)
+
+ container = client.containers.get("wildco-faceblur-container")
+ bucket_name = app.config['S3_BUCKET_NAME']
+ s3_folder = 'uploads'
+ megadetector_file = request.files.get('megadetector-file')
+
+ if 'folders' in request.form:
+ folder_paths = request.form.getlist('folders')
+ for folder_path in folder_paths:
+ print('folder path: ', folder_path)
+
+ # Create a in-memory tar archive
+ tar_buffer = io.BytesIO()
+ with tarfile.open(fileobj=tar_buffer, mode='w') as tar:
+ try:
+ # List objects in the S3 folder
+ s3_objects = s3_client.list_objects_v2(Bucket=bucket_name, Prefix=f"{s3_folder}/{folder_path}/")
+
+ for obj in s3_objects.get('Contents', []):
+ file_key = obj['Key']
+ file_name = os.path.relpath(file_key, f"{s3_folder}/{folder_path}")
+
+ # Create a file-like object for the S3 file
+ file_obj = io.BytesIO()
+ s3_client.download_fileobj(bucket_name, file_key, file_obj)
+ file_obj.seek(0)
+
+ # Add file to tar archive
+ info = tarfile.TarInfo(name=file_name)
+ info.size = len(file_obj.getvalue())
+ tar.addfile(info, file_obj)
+
+ # After the loop, print out what's been saved to docker
+ print("Files saved to Docker:")
+ for member in tar.getmembers():
+ print(f" - {member.name}")
+
+ except ClientError as e:
+ print(f"Error accessing S3: {e}")
+ return jsonify({'error': 'Failed to access S3'}), 500
+
+ # Reset buffer to the beginning
+ tar_buffer.seek(0)
+
+ # Stream the tar archive to Docker
+ container.exec_run(f"mkdir -p /app/input/{folder_path}")
+ container.put_archive(f"/app/input/{folder_path}", tar_buffer.getvalue())
+
+ if megadetector_file:
+ # Create a file-like object for the megadetector file
+ megadetector_buffer = io.BytesIO(megadetector_file.read())
+ megadetector_buffer.seek(0)
+
+ # Create a tar archive for the megadetector file
+ tar_buffer = io.BytesIO()
+ with tarfile.open(fileobj=tar_buffer, mode='w') as tar:
+ info = tarfile.TarInfo(name=megadetector_file.filename)
+ info.size = len(megadetector_buffer.getvalue())
+ tar.addfile(info, megadetector_buffer)
+
+ # Reset buffer to the beginning
+ tar_buffer.seek(0)
+
+ # Stream the tar archive to Docker
+ if folder_paths:
+ first_folder = folder_paths[0]
+ container.put_archive(f"/app/input/{first_folder}", tar_buffer.getvalue())
+ print(f"Megadetector file saved to Docker in folder: {first_folder}")
+ else:
+ container.put_archive('/app/input', tar_buffer.getvalue())
+ print("Megadetector file saved to Docker in root input folder")
+
+ return jsonify({'message': 'Uploaded files'}), 202
\ No newline at end of file
diff --git a/appstore/routes/zamba.py b/appstore/routes/zamba.py
index 5ab30295d159216ccd031c260a338458db9ea90b..80bb39ba5cdceedf7bd9bc2e4522b608ad739529 100644
--- a/appstore/routes/zamba.py
+++ b/appstore/routes/zamba.py
@@ -1,23 +1,44 @@
-from flask import Blueprint, render_template, request, jsonify, send_file
-from appstore import celery, app
+from flask import Blueprint, request, jsonify, send_file
+from appstore import celery, app, s3_client
from appstore.utils import process_zamba_task, train_zamba_task
from sqlalchemy import MetaData, Table
-from appstore import engine, connection
+from appstore import db
from werkzeug.utils import secure_filename
import os
bp = Blueprint('zamba', __name__, url_prefix='/zamba')
+BUCKET_NAME = app.config['S3_BUCKET_NAME']
+S3_FOLDER = 'uploads'
-@bp.route('/', methods=['GET', 'POST'])
-def zamba():
- models = ["blank_nonblank", "slowfast", "european"]
- if request.method == 'POST':
- files = request.files.getlist('file')
- for file in files:
- filename = secure_filename(file.filename)
- file.save(os.path.join(os.path.abspath(os.path.dirname(__file__)), app.config['UPLOAD_FOLDER'], filename))
- return render_template('zamba.html', models=models, files=files)
- return render_template('zamba.html', models=models)
+@bp.route('/upload', methods=['POST'])
+def zamba_upload():
+ if not request.form:
+ return jsonify({'error': 'Missing required files'}), 400
+ file_paths = request.form.getlist('file')
+ upload_folder = os.path.join(os.path.dirname(__file__), '..', app.config['UPLOAD_FOLDER'])
+
+ uploaded_files = []
+ for s3_path in file_paths:
+ try:
+ # Extract the filename from the S3 path
+ filename = os.path.basename(s3_path)
+ local_file_path = os.path.join(upload_folder, filename)
+ print("Local file path: ", local_file_path)
+
+ # Download the file from S3
+ s3_client.download_file(BUCKET_NAME, s3_path, local_file_path)
+
+ if os.path.exists(local_file_path):
+ uploaded_files.append(filename)
+ else:
+ return jsonify({"status": "error", "message": f"Failed to download {filename} from S3"})
+ except Exception as e:
+ return jsonify({"status": "error", "message": f"Error processing {s3_path}: {str(e)}"})
+
+ if len(uploaded_files) == len(file_paths):
+ return jsonify({"status": "success", "message": "Images downloaded from S3", "files": uploaded_files})
+ else:
+ return jsonify({"status": "partial_success", "message": "Some images failed to download from S3", "files": uploaded_files})
# Classifying unlabeled videos
@bp.route('/process', methods=['POST'])
@@ -35,61 +56,80 @@ def process():
return jsonify({'message': 'Processing started', 'task_id': task.id}), 202
@bp.route('/result', methods=['GET'])
-def display_result():
- result = get_result()
- return render_template('zamba-result.html', result=result)
-
def get_result():
print('start get result')
- metadata = MetaData(bind=engine)
+ metadata = MetaData(bind=db.engine)
zamba_csv = Table('zamba_csv', metadata, autoload=True)
# get results from the database
query = zamba_csv.select()
- result = connection.execute(query)
+ result = db.session.execute(query)
print('result: ', result)
# Convert the SQL result to a list of dictionaries
result_list = [dict(row) for row in result]
- print('result list: ', result_list)
- # connection.close()
- return result_list
+ return jsonify(result_list)
@bp.route('/download')
def download():
- path = '../zamba_predictions.csv'
- return send_file(path, as_attachment=True)
+ filename = 'zamba_predictions.csv'
+ file_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), '../..', filename)
+ print('file path: ', file_path)
+ if os.path.exists(file_path):
+ return send_file(file_path, as_attachment=True, download_name=filename)
+ else:
+ return jsonify({"error": "File not found"}), 404
# Training a model
-@bp.route('/train', methods=['GET', 'POST'])
-def train():
- # Initialize variables to None or empty
- labelCsv = None
- videofiles = []
-
- if request.method == 'POST':
- # Handle label CSV
- labelCsv = request.files.get('label-file')
- if labelCsv and labelCsv.filename != '':
- labelCsvFilename = secure_filename(labelCsv.filename)
- labelCsv.save(os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', app.config['TRAIN_FOLDER'], labelCsvFilename))
-
- # Handle video files
- videofiles = request.files.getlist('training-video-file')
- for video in videofiles:
- if video.filename: # Ensure there is a filename
- videoname = secure_filename(video.filename)
- video.save(os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', app.config['TRAIN_VIDEOS_FOLDER'], videoname))
+@bp.route('/train/upload', methods=['POST'])
+def train_upload():
+ uploaded_videos = []
+ upload_folder = os.path.join(os.path.dirname(__file__), '..', app.config['TRAIN_FOLDER'])
+ upload_videos_folder = os.path.join(os.path.dirname(__file__), '..', app.config['TRAIN_VIDEOS_FOLDER'])
+
+ # Handle label CSV
+ labelCsv = request.files.get('labelCsv')
+ if labelCsv and labelCsv.filename != '':
+ labelCsvFilename = secure_filename(labelCsv.filename)
+ labelCsv.save(os.path.join(upload_folder, labelCsvFilename))
- return render_template('zamba-train.html', labelCsv=labelCsv, videofiles=videofiles)
+ # Handle video files
+ videofile_paths = request.form.getlist('videos')
+ for s3_path in videofile_paths:
+ try:
+ # Extract the filename from the S3 path
+ filename = os.path.basename(s3_path)
+ local_file_path = os.path.join(upload_videos_folder, filename)
+ print("Local file path: ", local_file_path)
+
+ # Download the file from S3
+ s3_client.download_file(BUCKET_NAME, s3_path, local_file_path)
+
+ if os.path.exists(local_file_path):
+ uploaded_videos.append(filename)
+ else:
+ return jsonify({"status": "error", "message": f"Failed to download {filename} from S3"})
+ except Exception as e:
+ return jsonify({"status": "error", "message": f"Error processing {s3_path}: {str(e)}"})
+
+ if len(uploaded_videos) == len(videofile_paths):
+ return jsonify({"status": "success", "message": "Images downloaded from S3", "files": uploaded_videos})
+ else:
+ return jsonify({"status": "partial_success", "message": "Some images failed to download from S3", "files": uploaded_videos})
@bp.route('/train/start', methods=['POST'])
def train_start():
data = request.get_json()
model = data.get('model')
dryRun = data.get('dryRun')
- labels = 'appstore/' + app.config['TRAIN_FOLDER'] + '/' + data.get('labels')
- print('labels: ', labels)
- task = train_zamba_task.delay(model, dryRun, labels)
+ labels = data.get('labels')
+
+ if not labels:
+ return jsonify({'error': 'No label file specified'}), 400
+
+ labels_path = os.path.join('appstore/', app.config['TRAIN_FOLDER'], labels)
+
+ print('labels:', labels_path, 'model:', model, 'dryRun:', dryRun)
+ task = train_zamba_task.delay(model, dryRun, labels_path)
return jsonify({'message': 'Training started', 'task_id': task.id}), 202
\ No newline at end of file
diff --git a/appstore/static/megadetector/output.json b/appstore/static/megadetector/output.json
deleted file mode 100644
index 73ac199f6cfb811dbccb935e3c376c92bf726343..0000000000000000000000000000000000000000
--- a/appstore/static/megadetector/output.json
+++ /dev/null
@@ -1,94 +0,0 @@
-{
- "images": [
- {
- "file": "nz1.jpg",
- "detections": [
- {
- "category": "1",
- "conf": 0.0119,
- "bbox": [
- 0.5414,
- 0.3274,
- 0.3871,
- 0.467
- ]
- },
- {
- "category": "1",
- "conf": 0.892,
- "bbox": [
- 0.3042,
- 0.1294,
- 0.3585,
- 0.7106
- ]
- }
- ]
- },
- {
- "file": "nz2.jpg",
- "detections": [
- {
- "category": "1",
- "conf": 0.758,
- "bbox": [
- 0.5013,
- 0.4493,
- 0.2909,
- 0.2619
- ]
- }
- ]
- },
- {
- "file": "sea_star_sample_image_800.jpg",
- "detections": [
- {
- "category": "3",
- "conf": 0.00617,
- "bbox": [
- 0,
- 0,
- 0.2962,
- 0.985
- ]
- },
- {
- "category": "1",
- "conf": 0.0181,
- "bbox": [
- 0.001249,
- 0,
- 0.3062,
- 0.9983
- ]
- },
- {
- "category": "1",
- "conf": 0.963,
- "bbox": [
- 0.1512,
- 0.03499,
- 0.6537,
- 0.8866
- ]
- }
- ]
- }
- ],
- "detection_categories": {
- "1": "animal",
- "2": "person",
- "3": "vehicle"
- },
- "info": {
- "detection_completion_time": "2024-08-16 18:11:21",
- "format_version": "1.3",
- "detector": "unknown",
- "detector_metadata": {
- "megadetector_version": "unknown",
- "typical_detection_threshold": 0.5,
- "conservative_detection_threshold": 0.25
- }
- }
-}
\ No newline at end of file
diff --git a/appstore/templates/index.html b/appstore/templates/index.html
deleted file mode 100644
index f301de54c03bbdc459d07d85c721bb139b267fc7..0000000000000000000000000000000000000000
--- a/appstore/templates/index.html
+++ /dev/null
@@ -1,58 +0,0 @@
-<!DOCTYPE html>
-<html>
- <head>
- <title>Home Page</title>
- </head>
- <body>
- <h1>Camera Trapper App Store</h1>
- <button id="animl" onclick="startAniml()">Animl</button>
- <button id="zamba" onclick="window.location.href='/zamba'">Zamba</button>
- <button id="megadetector" onclick="window.location.href='/megadetector'">MegaDetector</button>
- <button id="trapper" onclick="startTrapper()">Trapper</button>
- <div id="loading" style="display: none;">Loading Trapper...</div>
-
- <script>
- function startAniml() {
- fetch('/animl/start', {
- method: 'POST',
- headers: {
- 'Content-Type': 'application/json'
- }
- })
- .then(response => response.json())
- .then(data => {
- if (data.status === 'running') {
- window.location.href = data.url;
- } else {
- console.error('Failed to start Animl:', data.error);
- }
- })
- .catch(error => console.error('Error:', error));
- }
- function startTrapper() {
- const loadingElement = document.getElementById('loading');
- loadingElement.style.display = 'block';
-
- fetch('/trapper/start', {
- method: 'POST',
- headers: {
- 'Content-Type': 'application/json'
- }
- })
- .then(response => response.json())
- .then(data => {
- if (data.status === 'running') {
- window.location.href = data.url;
- } else {
- console.error('Failed to start Trapper:', data.error);
- loadingElement.style.display = 'none';
- }
- })
- .catch(error => {
- console.error('Error:', error);
- loadingElement.style.display = 'none';
- });
- }
- </script>
- </body>
-</html>
\ No newline at end of file
diff --git a/appstore/templates/megadetector.html b/appstore/templates/megadetector.html
deleted file mode 100644
index ba66f88bbd6ea42220b13077ef644a9d6176c3cb..0000000000000000000000000000000000000000
--- a/appstore/templates/megadetector.html
+++ /dev/null
@@ -1,63 +0,0 @@
-<DOCTYPE html>
- <html>
- <head>
- <title>MegaDetector</title>
- <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
- </head>
- <body>
- <h1>MegaDetector</h1>
- <p>MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images).</p>
-
- <form action="/megadetector" method="post" enctype="multipart/form-data">
- <label for="image">Upload an image:</label>
- <input type="file" name="image" id="image" accept="image/*" multiple>
- <br>
- <input type="submit" value="Upload">
- </form>
- <br>
- <form action="/megadetector/run/batch" method="post" enctype="multipart/form-data" id="batchForm">
- <label for="model">Select a model:</label>
- <select name="model" id="model">
- <option value="MDV5A" selected>MDv5a</option>
- <option value="MDV5B">MDv5b</option>
- <option value="MDV4">MDv4</option>
- </select>
- <br>
- <div id="warning" style="color: red; font-weight: bold; display: none;">
- <p>Warning: MDv4 is not recommended, please only select this if you have a really good reason to do so.</p>
- </div>
- <input type="submit" value="Run Batch">
- </form>
-
- <button onclick="window.location.href='/megadetector/report'">View Report</button>
-
- <script>
- document.getElementById('model').addEventListener('change', function() {
- const modelFileInput = document.getElementById('warning');
- if (this.value === 'MDV4') {
- modelFileInput.style.display = 'block';
- } else {
- modelFileInput.style.display = 'none';
- }
- });
-
- document.getElementById('batchForm').addEventListener('submit', function(e) {
- e.preventDefault();
- const formData = new FormData(this);
-
- fetch('/megadetector/run/batch', {
- method: 'POST',
- body: formData
- })
- .then(response => response.json())
- .then(data => {
- console.log(data);
- })
- .catch(error => {
- console.error('Error:', error);
- });
- });
- </script>
- </body>
- </html>
-</DOCTYPE>
\ No newline at end of file
diff --git a/appstore/templates/zamba-result.html b/appstore/templates/zamba-result.html
deleted file mode 100644
index 1287b61b7427ed699fdd3d0a488823938e5b1e16..0000000000000000000000000000000000000000
--- a/appstore/templates/zamba-result.html
+++ /dev/null
@@ -1,25 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
- <head>
- <meta charset="UTF-8">
- <title>Zamba Data Display</title>
- </head>
- <body>
- <h1>Zamba Classification Results</h1>
- <p>Only result of selected most likely class will the display here. You can still download the CSV file to see the result.</p>
- <table border="1">
- <tr>
- <th>Filepath</th>
- <th>Classname</th>
- </tr>
- {% for item in result %}
- <tr>
- <td>{{ item.filepath }}</td>
- <td>{{ item.classname }}</td>
- </tr>
- {% endfor %}
- </table>
- <h2>Download the original CSV file <a href="{{url_for('zamba.download')}}">here</a></h2>
- <button onclick="window.location.href='/zamba'">Back to Zamba</button>
- </body>
-</html>
diff --git a/appstore/templates/zamba-train.html b/appstore/templates/zamba-train.html
deleted file mode 100644
index 884559baf33f2d11df9f03860be407a187a08733..0000000000000000000000000000000000000000
--- a/appstore/templates/zamba-train.html
+++ /dev/null
@@ -1,115 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
- <head>
- <meta charset="UTF-8">
- <title>Zamba Train</title>
- <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
- </head>
- <body>
- <h1>Zamba - Train a model on labeled videos or images</h1>
- <p>For more detailed information, check the <a href="https://zamba.drivendata.org/docs/stable/train-tutorial/" target="_blank">User Tutorial</a>.</p>
-
- <h2>Upload a CSV containing the video labels to use as ground truth during training:</h2>
- <p>There must be columns for both <code>filepath</code> and <code>label</code>.</p>
- <form method="POST" enctype="multipart/form-data">
- <label>CSV file: </label>
- <input type="file" name="label-file" accept=".csv" />
- <br>
- <label>Videos for model training (at least 3 videos per label): </label>
- <input type="file" name="training-video-file" multiple />
- <br>
- <input type="submit" value="Upload" />
- </form>
-
- <h3>Your uploaded label CSV file</h3>
- {% if labelCsv %}
- <ul>
- <li>{{ labelCsv.filename }}</li>
- </ul>
- {% else %}
- <p>No CSV file uploaded.</p>
- {% endif %}
-
- <h3>Your uploaded videos</h3>
- {% if videofiles %}
- <ul>
- {% for video in videofiles %}
- <li>{{ video.filename }}</li>
- {% endfor %}
- </ul>
- {% else %}
- <p>No video files uploaded.</p>
- {% endif %}
-
- <!-- Configuration -->
- <h2>Configuration</h2>
- <p>If you wish to finetune an existing model, please the model here:</p>
- <label for="model">Model:</label>
- <select name="model" method="GET" action="/zamba/train" id="existing-model">
- <option value="time_distributed" selected>time_distributed (default)</option>
- <option value="blank_nonblank">blank_nonblank</option>
- <option value="slowfast">slowfast</option>
- <option value="european">european</option>
- </select>
- <br>
- <label for="dryRun">Dry run:</label>
- <select name="dryRun" method="GET" action="/zamba" id="dry-run">
- <option value="false">false</option>
- <option value="true" selected>true</option>
- </select>
-
- <!-- Submit request -->
- <p>Select the Train model button to confirm training the model. This process may take a while.</p>
- <button onclick="triggerTrain()">Train model</button>
-
- <!-- Display task status/progress when running classification -->
- <div id="trainStatus"></div>
- <div id="taskStatus"></div>
-
- <script>
- function triggerTrain() {
- var selectedModel = document.getElementById('existing-model').value;
- var dryRun = document.getElementById('dry-run').value;
- var labels = '{{ labelCsv.filename }}';
-
- $.ajax({
- url: '/zamba/train/start',
- type: 'POST',
- contentType: 'application/json',
- data: JSON.stringify({ model:selectedModel, dryRun:dryRun, labels:labels }),
- success: function(response) {
- console.log(response);
- document.getElementById('trainStatus').innerHTML = 'Training started: ' + response.message + '<br>Task ID: ' + response.task_id;
- sessionStorage.setItem('taskID', response.task_id); // Store task ID in session storage for future use
- },
- error: function(response) {
- document.getElementById('result').innerHTML = 'Error: ' + response.responseJSON.error;
- }
- });
- }
-
- function checkTaskStatus() {
- var taskId = sessionStorage.getItem('taskID');
- // var dryRun = document.getElementById('dry-run').value;
- if (taskId) {
- fetch(`/task_status/${taskId}`)
- .then(response => response.json())
- .then(data => {
- console.log(data);
- message = data.result ? data.result.message : 'Not avaliable yet';
- output = data.result ? data.result.output : '';
- prettyOutput = output.replace(/\n/g, '<br>')
- document.getElementById('taskStatus').innerHTML = 'Task Status: ' + data.status + '<br>Message: ' + message + '<br>Output: ' + prettyOutput;
-
- if (data.status === 'SUCCESS') {
- clearInterval(interval);
- }
- });
- }
- }
-
- // Get task status every minute and stop when success
- var interval = setInterval(checkTaskStatus, 60000);
- </script>
- </body>
-</html>
diff --git a/appstore/templates/zamba.html b/appstore/templates/zamba.html
deleted file mode 100644
index bfbd0cbcf36d6ca4924e61e49aa8d91fa8d1c2f0..0000000000000000000000000000000000000000
--- a/appstore/templates/zamba.html
+++ /dev/null
@@ -1,108 +0,0 @@
-<!DOCTYPE html>
-<html>
- <head>
- <title>Zamba</title>
- <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
- </head>
- <body>
- <h1>Zamba - Classifying unlabeled videos</h1>
- <p>For more detailed information, check the <a href="https://zamba.drivendata.org/docs/stable/predict-tutorial/">User Tutorial</a>.</p>
-
- <h2>Upload videos/images for processing:</h2>
- <p>Only upload one type (video/image) at a time.</p>
- <form method="POST" enctype="multipart/form-data">
- <input type="file" name="file" multiple />
- <input type="submit" value="Upload" />
- </form>
-
- <h3>Your uploaded videos</h3>
- <ul>
- {% for file in files %}
- <li>{{ file.filename }}</li>
- {% endfor %}
- </ul>
-
- <!-- command prompt setting -->
- <h2>Configuration</h2>
- <label for="mediaType">Media type:</label>
- <select name="mediaType" method="GET" action="/zamba" id="media-type">
- <option value="video" selected>video</option>
- <option value="image">image</option>
- </select>
- <br>
- <label for="dryRun">Dry run:</label>
- <select name="dryRun" method="GET" action="/zamba" id="dry-run">
- <option value="false">false</option>
- <option value="true" selected>true</option>
- </select>
- <br>
- <label for="outputClassname">Output class name only: </label>
- <select name="outputClassname" method="GET" action="/zamba" id="output-classname">
- <option value="false">false</option>
- <option value="true" selected>true</option>
- </select>
-
- <p>Please select a pretrained models that ship with the zamba package (see the <a href="https://zamba.drivendata.org/docs/stable/models/species-detection/" target="_blank">Available Models</a> page for more details).</p>
- <label for="model">Choose a pretrained model:</label>
- <select name="model" method="GET" action="/zamba" id="model-select">
- <option value="time_distributed" selected>time_distributed (default)</option>
- {% for model in models %}
- <option value="{{ model }}">{{ model }}</option>
- {% endfor %}
- </select>
-
- <p>Select the Process file(s) button to confirm processing the uploaded files. This process may take a while. </p>
- <button onclick="triggerProcess()">Process file(s)</button>
-
- <!-- Display task status/progress when running classification -->
- <div id="result"></div>
- <div id="taskStatus"></div>
-
- <button onclick="window.location.href='/zamba/result'">See results</button>
-
- <script>
- function triggerProcess() {
- // get selected model from the dropdown
- var mediaType = document.getElementById('media-type').value;
- var selectedModel = document.getElementById('model-select').value;
- var dryRun = document.getElementById('dry-run').value;
- var outputClassname = document.getElementById('output-classname').value;
-
- // send POST request to server
- $.ajax({
- url: '/zamba/process',
- type: 'POST',
- contentType: 'application/json',
- data: JSON.stringify({ type:mediaType, model:selectedModel, dryRun:dryRun, outputClassname:outputClassname }),
- success: function(response) {
- document.getElementById('result').innerHTML = 'Classification started: ' + response.message + '<br>Task ID: ' + response.task_id;
- sessionStorage.setItem('taskID', response.task_id); // Store task ID in session storage for future use
- },
- error: function(response) {
- document.getElementById('result').innerHTML = 'Error: ' + response.responseJSON.error;
- }
- });
- }
-
- function checkTaskStatus() {
- var taskId = sessionStorage.getItem('taskID');
- console.log(taskId);
- if (taskId) {
- fetch(`/task_status/${taskId}`)
- .then(response => response.json())
- .then(data => {
- console.log(data);
- document.getElementById('taskStatus').innerHTML = 'Task Status: ' + data.status;
-
- if (data.status === 'SUCCESS') {
- clearInterval(interval);
- }
- });
- }
- }
-
- // Get task status every 10 seconds and stop when success
- var interval = setInterval(checkTaskStatus, 10000);
- </script>
- </body>
-</html>
\ No newline at end of file
diff --git a/appstore/utils.py b/appstore/utils.py
index 352952959301a7fe27c9b0393adc12fedd7acb07..5b152054ca566771fae61161f00f27d70dacdb4d 100644
--- a/appstore/utils.py
+++ b/appstore/utils.py
@@ -6,14 +6,31 @@ import json
import os
import git
import pandas as pd
-from sqlalchemy import text
-from appstore import connection, engine
+from sqlalchemy import text, types
+from appstore import db
from appstore.models import AppMetadata
+import logging
+
+# Set up logging at the beginning of your script
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
# variables
trapper_metadata = AppMetadata(
name="Trapper",
- description="Trapper Expert - core web application for camera trap data management",
+ display_name="Trapper",
+ description="TRAPPER is an open-source, Python, Django and Docker-based web application designed to manage camera trapping projects.",
+ full_description="""TRAPPER is an open-source, Python, Django and Docker-based web application designed to manage camera trapping projects.
+ \n• Open-source and free for use in research, academia, or wildlife conservation projects (GPLv3).
+ \n• Offers a spatially-enabled database backend.
+ \n• Capable of handling both pictures and videos.
+ \n• Features a flexible model for AI-based and expert-based classifications.
+ \n• Promotes the Camtrap DP standard and encourages data re-use.
+ \n• Supports collaborative work on a project.""",
+ logo="https://gitlab.com/trapper-project/trapper/-/raw/master/trapper/trapper-project/trapper/apps/common/static/images/logo/logo.png",
+ image="https://gitlab.com/trapper-project/trapper/-/raw/master/trapper/trapper-project/trapper/apps/common/static/images/logo/logo_text.png",
+ link="http://localhost:8000/",
+ tags=["Object Detection", "Filtering"],
repository_url="https://gitlab.com/trapper-project/trapper.git",
docker_compose_file="docker-compose.yml",
start_command="./start.sh -pb dev",
@@ -21,38 +38,156 @@ trapper_metadata = AppMetadata(
)
animl_metadata = AppMetadata(
name="Animl",
- description="Animl - core web application for animal detection",
+ display_name="Animl",
+ description="Animl is an open, extensible, cloud-based platform for managing camera trap data.",
+ full_description="""Animl is an open, extensible, cloud-based platform for managing camera trap data. It provides:
+ \n• Ingest data from a variety of camera trap types (wireless, SD card based, IP, etc.)
+ \n• Systematically store and manage images in a single centralized, cloud-based repository
+ \n• Upload custom object detection and species clasification ML models and configure automated assisted-labeling pipelines
+ \n• Offer frontend web application to view images, review ML-assisted labels, perform manual labeling
+ \n• Offer an API for advanced querying and analysis of camera trap data
+ \n• Offer tools for exporting ML model training data""",
+ logo='https://raw.githubusercontent.com/tnc-ca-geo/animl-frontend/d4b2542251007f7e01bb08956473d5b8f1c84314/public/favicon.ico',
+ image='https://raw.githubusercontent.com/tnc-ca-geo/animl-frontend/main/src/assets/animl-logo.svg',
+ link='/animl',
+ tags=['Object Detection', 'Filtering'],
repository_url="https://github.com/tnc-ca-geo/animl-frontend.git",
docker_image="registry.git.cf.ac.uk/c22097859/c22097859_cmt403_dissertation/animl:latest",
start_command="docker run -p 5173:5173 animl-container animl",
stop_command="docker stop animl-container"
)
+ecosecrets_metadata = AppMetadata(
+ name="EcoSecrets",
+ display_name="EcoSecrets",
+ description='EcoSecrets is a web application enabling users to manage their camera traps data.',
+ full_description="""ecoSecrets is an open-source web application that aims to facilitate biodiversity studies that use autonomous data collection devices such as camera traps. This web-application is in development and offers several features that meet the needs of biodiversity stakeholders:
+ \n• project management: to delimit the studies according to their context
+ \n• management of study sites: to identify spatial scope
+ \n• device management: to specify technical characteristics of the tools used in the field and their availability
+ \n• deployment management: to characterize spatio-temporal limits of data acquisition
+ \n• media management: to standardize and optimize the storage of collected data
+ \n• media processing: to enable the addition of annotations to the raw data""",
+ logo='https://raw.githubusercontent.com/naturalsolutions/ecoSecrets/main/frontend/public/assets/ecosecrets-logo-icon.svg',
+ image='https://raw.githubusercontent.com/naturalsolutions/ecoSecrets/main/frontend/public/assets/ecosecrets_logo_full_light.svg',
+ link='http://localhost:8889/',
+ tags=['Data management', 'Processing'],
+ repository_url="https://github.com/naturalsolutions/ecoSecrets.git",
+ docker_compose_file="docker-compose.yml",
+ start_command="./scripts/docker.sh up -d",
+ stop_command="./scripts/docker.sh down"
+)
+il2bb_metadata = AppMetadata(
+ name="IL2BB",
+ display_name="IL2BB",
+ description='Image Level Label to Bounding Box Pipeline',
+ full_description="""The Image Level Label to Bounding Box (IL2BB) pipeline automates the generation of labeled bounding boxes by leveraging an organization’s previous labeling efforts and Microsoft AI for Earth’s MegaDetector. The output of this pipeline are batches of images with annotation files that can be opened, reviewed, and modified with the Bounding Box Editor and Exporter (BBoxEE) to prepare training data for object detectors.
+ The IL2BB pipeline is especially useful for organizations that are hesitant or not permitted to use or store data on online services.""",
+ logo='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ image='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ link='/il2bb',
+ tags=['Classification'],
+ repository_url="https://github.com/persts/IL2BB",
+ docker_image="registry.git.cf.ac.uk/c22097859/c22097859_cmt403_dissertation/il2bb:latest",
+ start_command="docker run il2bb-container il2bb",
+ stop_command="docker stop il2bb-container"
+)
+cameratraptools_metadata = AppMetadata(
+ name="CameraTrapTools",
+ display_name="Camera Trap Tools",
+ description='Camera Trap Tools is a collection of tools for managing camera trap data.',
+ full_description="Camera Trap Tools is a collection of tools for managing camera trap data.",
+ logo='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ image='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ link='/camera-trap-tools',
+ tags=['Data management'],
+ repository_url="https://github.com/persts/CameraTrapTools",
+ docker_image="registry.git.cf.ac.uk/c22097859/c22097859_cmt403_dissertation/camera-trap-tools:latest",
+ start_command="docker run camera-trap-tools-container camera-trap-tools",
+ stop_command="docker stop camera-trap-tools-container"
+)
+wildcofaceblur_metadata = AppMetadata(
+ name="WildCoFaceBlur",
+ display_name="WildCo-FaceBlur",
+ description='WildCo-FaceBlur is a tool for blurring faces in camera trap images.',
+ full_description="""WildCo-FaceBlur is a tool for blurring faces in camera trap images.""",
+ logo='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ image='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ link='/wildcofaceblur',
+ tags=['Processing'],
+ repository_url="https://github.com/mitch-fen/WildCo-FaceBlur.git",
+ docker_image="registry.git.cf.ac.uk/c22097859/c22097859_cmt403_dissertation/wildco-faceblur:latest",
+ start_command="docker run wildco-faceblur-container wildco-faceblur",
+ stop_command="docker stop wildco-faceblur-container"
+)
+cameratrapworkflow_metadata = AppMetadata(
+ name="CameraTrapWorkflow",
+ display_name="Camera Trap Workflow",
+ description='Camera Trap Workflow is a semi-automatic workflow to process camera trap images in R.',
+ full_description="""Camera Trap Workflow is a semi-automatic workflow to process camera trap images in R. It includes:
+ \n• Extract metadata and rename images
+ \n• Automatic classification
+ \n• Quality check
+ \n• Manual classification
+ \n• Formatting""",
+ logo='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ image='https://github.com/yan-tung-lam/il2bb/blob/main/logo.png?raw=true',
+ link='/camera-trap-workflow',
+ tags=['Data management', 'Processing'],
+ repository_url="https://github.com/hannaboe/camera_trap_workflow",
+ docker_image="registry.git.cf.ac.uk/c22097859/c22097859_cmt403_dissertation/camera-trap-workflow:latest",
+ start_command="docker run -p 3838:3838 --name camera-trap-workflow-container camera-trap-workflow",
+ stop_command="docker stop camera-trap-workflow-container"
+)
+
+zamba_metadata = AppMetadata(
+ name="Zamba",
+ display_name="Zamba",
+ description="Zamba is a tool built in Python that uses machine learning and computer vision to automatically detect and classify animals in camera trap videos.",
+ full_description="""Zamba Cloud uses machine learning to automatically detect and classify animals in camera trap videos. You can use Zamba Cloud to:
+ \n• Classify which species appear in a video
+ \n• Identify blank videos
+ \n• Train a custom model with your own labeled data to identify species in your habitat
+ \n• Estimate the distance between animals in the frame and the camera
+ \n• And more!""",
+ logo="https://drivendata-prod-public.s3.amazonaws.com/images/drivendata-logo.58f94dd407ef.svg",
+ image="https://drivendata-prod-public.s3.amazonaws.com/images/drivendata-logo.58f94dd407ef.svg",
+ link="/zamba",
+ tags=["Classification", "Train Model"],
+ repository_url="https://github.com/drivendataorg/zamba",
+)
+
+megadetector_metadata = AppMetadata(
+ name='MegaDetector',
+ display_name='MegaDetector',
+ description='MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images.',
+ full_description="""Conservation biologists invest a huge amount of time reviewing camera trap images, and a huge fraction of that time is spent reviewing images they aren't interested in. This primarily includes empty images, but for many projects, images of people and vehicles are also "noise", or at least need to be handled separately from animals.
+ Machine learning can accelerate this process, letting biologists spend their time on the images that matter.
+ To this end, this page hosts a model we've trained - called "MegaDetector" - to detect animals, people, and vehicles in camera trap images. It does not identify animals to the species level, it just finds them.""",
+ logo='https://camo.githubusercontent.com/1ee7509ad47f8cbc02dd27f8405b8064ce3507fd454f187c7fb7e22da36ed0a3/68747470733a2f2f692e696d6775722e636f6d2f464354627147482e706e67',
+ image='https://camo.githubusercontent.com/1ee7509ad47f8cbc02dd27f8405b8064ce3507fd454f187c7fb7e22da36ed0a3/68747470733a2f2f692e696d6775722e636f6d2f464354627147482e706e67',
+ link='/megadetector',
+ tags=['Object Detection', 'Filtering'],
+ repository_url="https://github.com/agentmorris/MegaDetector/",
+)
# functions
def create_app_metadata(app_metadata):
- with engine.connect() as connection:
- connection.execute(AppMetadata.__table__.insert().values(
- name=app_metadata.name,
- description=app_metadata.description,
- repository_url=app_metadata.repository_url,
- docker_compose_file=app_metadata.docker_compose_file,
- docker_image=app_metadata.docker_image,
- start_command=app_metadata.start_command,
- stop_command=app_metadata.stop_command
- ))
+ db.session.add(app_metadata)
+ db.session.commit()
def get_app_metadata(app_name):
- with engine.connect() as connection:
- result = connection.execute(text("SELECT * FROM app_metadata WHERE name = :name"), {"name": app_name}).fetchone()
- return {
- "name": result[1],
- "description": result[2],
- "repository_url": result[3],
- "docker_compose_file": result[4],
- "docker_image": result[5],
- "start_command": result[6],
- "stop_command": result[7]
- }
+ result = AppMetadata.query.filter_by(name=app_name).first()
+ if result is None:
+ return None # or raise an exception if you prefer
+ return {
+ "name": result.name,
+ "description": result.description,
+ "repository_url": result.repository_url,
+ "docker_compose_file": result.docker_compose_file,
+ "docker_image": result.docker_image,
+ "start_command": result.start_command,
+ "stop_command": result.stop_command
+ }
def is_container_exist(container_name):
client = docker.from_env()
@@ -77,17 +212,25 @@ def is_container_running_by_name(container_name):
def is_container_running(service_name):
client = docker.from_env()
try:
+ logger.debug(f"Checking if service '{service_name}' is running")
containers = client.containers.list(filters={'label': f'com.docker.compose.service={service_name}'})
- return any(container.status == 'running' for container in containers)
+ logger.debug(f"Found {len(containers)} containers for service '{service_name}'")
+
+ for container in containers:
+ logger.debug(f"Container {container.name} status: {container.status}")
+
+ is_running = any(container.status == 'running' for container in containers)
+ logger.debug(f"Service '{service_name}' running status: {is_running}")
+ return is_running
except Exception as e:
- print(f"Error checking container status: {e}")
+ logger.error(f"Error checking container status for service '{service_name}': {e}")
return False
def is_server_ready(url, timeout=5):
try:
response = requests.get(url, timeout=timeout)
return response.status_code == 200
- except requests.RequestException:
+ except requests.RequestException as e:
return False
# celery tasks
@@ -117,7 +260,6 @@ def train_zamba_task(model, dryRun, labels, data_dir='appstore/static/zamba/trai
def process_zamba_task(type, model, dryRun, outputClassname, data_dir='appstore/static/zamba/media'):
print('Start processing (celery task)...')
command = ['zamba', 'predict', '--data-dir', data_dir, '-y', '--model', model]
- # command.insert(0, 'PREDICT_ON_IMAGES=True') if type == 'image' else command.insert(0, 'PREDICT_ON_IMAGES=False')
command.append('--dry-run') if dryRun == "true" else command.append('--no-dry-run')
command.append('--output-class-names') if outputClassname == "true" else command.append('--no-output-class-names')
@@ -146,7 +288,18 @@ def parseCSV(filePath):
# Read CSV using pandas
csvDataFrame = pd.read_csv(filePath)
csvDataFrame.rename(columns={'0': 'classname'}, inplace=True)
+ csvDataFrame['timestamp'] = pd.Timestamp.now()
# Insert data into the database
- connection.execute(text("CREATE TABLE IF NOT EXISTS zamba_csv (filepath VARCHAR(255), classname VARCHAR(255))"))
- csvDataFrame.to_sql('zamba_csv', con=engine, index=False, if_exists='append')
\ No newline at end of file
+ db.session.add(csvDataFrame)
+ db.session.commit()
+
+ # Convert timestamp to datetime before inserting into database
+ csvDataFrame['timestamp'] = pd.to_datetime(csvDataFrame['timestamp'])
+
+ csvDataFrame.to_sql('zamba_csv', con=db.engine, index=False, if_exists='append', dtype={'timestamp': types.DateTime()})
+
+ # Verify that the data was inserted correctly
+ result = db.session.execute(text("SELECT * FROM zamba_csv LIMIT 5"))
+ for row in result:
+ print(row)
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d5e824a1dd995d56450fde9353db17cd4426cef2
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,40 @@
+version: '3'
+services:
+ api:
+ build:
+ context: .
+ dockerfile: Dockerfile.api
+ ports:
+ - "5001:5001"
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock
+ environment:
+ - FLASK_ENV=development
+ - DOCKER_HOST=unix:///var/run/docker.sock
+ networks:
+ - appstore_network
+
+ frontend:
+ build:
+ context: .
+ dockerfile: Dockerfile.client
+ ports:
+ - "3001:3001"
+ environment:
+ - PORT=3001
+ depends_on:
+ - api
+ networks:
+ - appstore_network
+
+ redis:
+ image: "redis:alpine"
+ ports:
+ - "6379:6379"
+ networks:
+ - appstore_network
+
+networks:
+ appstore_network:
+ name: appstore_network
+ external: true
diff --git a/frontend/.gitignore b/frontend/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..4d29575de80483b005c29bfcac5061cd2f45313e
--- /dev/null
+++ b/frontend/.gitignore
@@ -0,0 +1,23 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# production
+/build
+
+# misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
diff --git a/frontend/README.md b/frontend/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..58beeaccd87e230076cab531b8f418f40b6d1aeb
--- /dev/null
+++ b/frontend/README.md
@@ -0,0 +1,70 @@
+# Getting Started with Create React App
+
+This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
+
+## Available Scripts
+
+In the project directory, you can run:
+
+### `npm start`
+
+Runs the app in the development mode.\
+Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
+
+The page will reload when you make changes.\
+You may also see any lint errors in the console.
+
+### `npm test`
+
+Launches the test runner in the interactive watch mode.\
+See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
+
+### `npm run build`
+
+Builds the app for production to the `build` folder.\
+It correctly bundles React in production mode and optimizes the build for the best performance.
+
+The build is minified and the filenames include the hashes.\
+Your app is ready to be deployed!
+
+See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
+
+### `npm run eject`
+
+**Note: this is a one-way operation. Once you `eject`, you can't go back!**
+
+If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
+
+Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
+
+You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
+
+## Learn More
+
+You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
+
+To learn React, check out the [React documentation](https://reactjs.org/).
+
+### Code Splitting
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
+
+### Analyzing the Bundle Size
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
+
+### Making a Progressive Web App
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
+
+### Advanced Configuration
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
+
+### Deployment
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
+
+### `npm run build` fails to minify
+
+This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..21f45025088803c4bd82e9c894af2c5239d58a9b
--- /dev/null
+++ b/frontend/package-lock.json
@@ -0,0 +1,19085 @@
+{
+ "name": "frontend",
+ "version": "0.1.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "frontend",
+ "version": "0.1.0",
+ "dependencies": {
+ "@radix-ui/react-dialog": "^1.1.1",
+ "@testing-library/jest-dom": "^5.17.0",
+ "@testing-library/react": "^13.4.0",
+ "@testing-library/user-event": "^13.5.0",
+ "chart.js": "^4.4.4",
+ "class-variance-authority": "^0.7.0",
+ "clsx": "^2.1.1",
+ "dayjs": "^1.11.13",
+ "http-proxy-middleware": "^3.0.1",
+ "lucide-react": "^0.445.0",
+ "react": "^18.3.1",
+ "react-chartjs-2": "^5.2.0",
+ "react-dom": "^18.3.1",
+ "react-modal": "^3.16.1",
+ "react-router-dom": "^6.26.1",
+ "react-scripts": "5.0.1",
+ "react-tailwindcss-datepicker": "^1.7.2",
+ "shadcn-ui": "^0.9.1",
+ "tailwind-merge": "^2.5.2",
+ "tailwindcss-animate": "^1.0.7",
+ "web-vitals": "^2.1.4"
+ },
+ "devDependencies": {
+ "daisyui": "^4.12.10",
+ "tailwindcss": "^3.4.10"
+ }
+ },
+ "node_modules/@adobe/css-tools": {
+ "version": "4.4.0",
+ "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.0.tgz",
+ "integrity": "sha512-Ff9+ksdQQB3rMncgqDK78uLznstjyfIf2Arnh22pW8kBpLs6rpKDwgnZT46hin5Hl1WzazzK64DOrhSwYpS7bQ=="
+ },
+ "node_modules/@alloc/quick-lru": {
+ "version": "5.2.0",
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+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ },
+ "node_modules/@ampproject/remapping": {
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+ "dependencies": {
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+ "@jridgewell/trace-mapping": "^0.3.24"
+ },
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+ }
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+ "bin": {
+ "na": "bin/na.mjs",
+ "nci": "bin/nci.mjs",
+ "ni": "bin/ni.mjs",
+ "nlx": "bin/nlx.mjs",
+ "nr": "bin/nr.mjs",
+ "nu": "bin/nu.mjs",
+ "nun": "bin/nun.mjs"
+ }
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diff --git a/frontend/package.json b/frontend/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..82b5f3506fe01bd38964902f218ef5d843aff99b
--- /dev/null
+++ b/frontend/package.json
@@ -0,0 +1,55 @@
+{
+ "name": "frontend",
+ "version": "0.1.0",
+ "private": true,
+ "dependencies": {
+ "@radix-ui/react-dialog": "^1.1.1",
+ "@testing-library/jest-dom": "^5.17.0",
+ "@testing-library/react": "^13.4.0",
+ "@testing-library/user-event": "^13.5.0",
+ "chart.js": "^4.4.4",
+ "class-variance-authority": "^0.7.0",
+ "clsx": "^2.1.1",
+ "dayjs": "^1.11.13",
+ "http-proxy-middleware": "^3.0.1",
+ "lucide-react": "^0.445.0",
+ "react": "^18.3.1",
+ "react-chartjs-2": "^5.2.0",
+ "react-dom": "^18.3.1",
+ "react-modal": "^3.16.1",
+ "react-router-dom": "^6.26.1",
+ "react-scripts": "5.0.1",
+ "react-tailwindcss-datepicker": "^1.7.2",
+ "tailwind-merge": "^2.5.2",
+ "tailwindcss-animate": "^1.0.7",
+ "web-vitals": "^2.1.4"
+ },
+ "scripts": {
+ "start": "export PORT=3001 && react-scripts start",
+ "build": "react-scripts build",
+ "test": "react-scripts test",
+ "eject": "react-scripts eject"
+ },
+ "eslintConfig": {
+ "extends": [
+ "react-app",
+ "react-app/jest"
+ ]
+ },
+ "browserslist": {
+ "production": [
+ ">0.2%",
+ "not dead",
+ "not op_mini all"
+ ],
+ "development": [
+ "last 1 chrome version",
+ "last 1 firefox version",
+ "last 1 safari version"
+ ]
+ },
+ "devDependencies": {
+ "daisyui": "^4.12.10",
+ "tailwindcss": "^3.4.10"
+ }
+}
diff --git a/frontend/public/favicon.ico b/frontend/public/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..a11777cc471a4344702741ab1c8a588998b1311a
Binary files /dev/null and b/frontend/public/favicon.ico differ
diff --git a/frontend/public/index.html b/frontend/public/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..aa069f27cbd9d53394428171c3989fd03db73c76
--- /dev/null
+++ b/frontend/public/index.html
@@ -0,0 +1,43 @@
+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8" />
+ <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
+ <meta name="viewport" content="width=device-width, initial-scale=1" />
+ <meta name="theme-color" content="#000000" />
+ <meta
+ name="description"
+ content="Web site created using create-react-app"
+ />
+ <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+ <!--
+ manifest.json provides metadata used when your web app is installed on a
+ user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
+ -->
+ <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+ <!--
+ Notice the use of %PUBLIC_URL% in the tags above.
+ It will be replaced with the URL of the `public` folder during the build.
+ Only files inside the `public` folder can be referenced from the HTML.
+
+ Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
+ work correctly both with client-side routing and a non-root public URL.
+ Learn how to configure a non-root public URL by running `npm run build`.
+ -->
+ <title>React App</title>
+ </head>
+ <body>
+ <noscript>You need to enable JavaScript to run this app.</noscript>
+ <div id="root"></div>
+ <!--
+ This HTML file is a template.
+ If you open it directly in the browser, you will see an empty page.
+
+ You can add webfonts, meta tags, or analytics to this file.
+ The build step will place the bundled scripts into the <body> tag.
+
+ To begin the development, run `npm start` or `yarn start`.
+ To create a production bundle, use `npm run build` or `yarn build`.
+ -->
+ </body>
+</html>
diff --git a/frontend/public/logo192.png b/frontend/public/logo192.png
new file mode 100644
index 0000000000000000000000000000000000000000..fc44b0a3796c0e0a64c3d858ca038bd4570465d9
Binary files /dev/null and b/frontend/public/logo192.png differ
diff --git a/frontend/public/logo512.png b/frontend/public/logo512.png
new file mode 100644
index 0000000000000000000000000000000000000000..a4e47a6545bc15971f8f63fba70e4013df88a664
Binary files /dev/null and b/frontend/public/logo512.png differ
diff --git a/frontend/public/manifest.json b/frontend/public/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..080d6c77ac21bb2ef88a6992b2b73ad93daaca92
--- /dev/null
+++ b/frontend/public/manifest.json
@@ -0,0 +1,25 @@
+{
+ "short_name": "React App",
+ "name": "Create React App Sample",
+ "icons": [
+ {
+ "src": "favicon.ico",
+ "sizes": "64x64 32x32 24x24 16x16",
+ "type": "image/x-icon"
+ },
+ {
+ "src": "logo192.png",
+ "type": "image/png",
+ "sizes": "192x192"
+ },
+ {
+ "src": "logo512.png",
+ "type": "image/png",
+ "sizes": "512x512"
+ }
+ ],
+ "start_url": ".",
+ "display": "standalone",
+ "theme_color": "#000000",
+ "background_color": "#ffffff"
+}
diff --git a/frontend/public/robots.txt b/frontend/public/robots.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9e57dc4d41b9b46e05112e9f45b7ea6ac0ba15e
--- /dev/null
+++ b/frontend/public/robots.txt
@@ -0,0 +1,3 @@
+# https://www.robotstxt.org/robotstxt.html
+User-agent: *
+Disallow:
diff --git a/frontend/src/App.css b/frontend/src/App.css
new file mode 100644
index 0000000000000000000000000000000000000000..74b5e053450a48a6bdb4d71aad648e7af821975c
--- /dev/null
+++ b/frontend/src/App.css
@@ -0,0 +1,38 @@
+.App {
+ text-align: center;
+}
+
+.App-logo {
+ height: 40vmin;
+ pointer-events: none;
+}
+
+@media (prefers-reduced-motion: no-preference) {
+ .App-logo {
+ animation: App-logo-spin infinite 20s linear;
+ }
+}
+
+.App-header {
+ background-color: #282c34;
+ min-height: 100vh;
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+ justify-content: center;
+ font-size: calc(10px + 2vmin);
+ color: white;
+}
+
+.App-link {
+ color: #61dafb;
+}
+
+@keyframes App-logo-spin {
+ from {
+ transform: rotate(0deg);
+ }
+ to {
+ transform: rotate(360deg);
+ }
+}
diff --git a/frontend/src/App.js b/frontend/src/App.js
new file mode 100644
index 0000000000000000000000000000000000000000..bb6e07b48198d46ac1977a4ab087f83efd088ba5
--- /dev/null
+++ b/frontend/src/App.js
@@ -0,0 +1,62 @@
+import React, { useState, useEffect } from 'react';
+import Home from './components/Home';
+import About from './components/About';
+import Login from './components/Login';
+import PrivateRoute from './components/PrivateRoute';
+import Profile from './components/Profile';
+import Register from './components/Register';
+import DataManagement from './components/DataManagement';
+import MegaDetector from './components/MegaDetector';
+import Zamba from './components/Zamba';
+import ZambaResult from './components/ZambaResult';
+import Il2bb from './components/Il2bb';
+import WildcoFaceBlur from './components/WildcoFaceBlur';
+import Preview from './components/Preview';
+import CameraTrapTools from './components/CameraTrapTools';
+import CameraTrapWorkflow from './components/CameraTrapWorkflow';
+import ImageBrowserPopup from './components/ImageBrowserPopup';
+import { MegaDetectorReport, MegaDetectorDetectionsAnimalReport } from './components/MegaDetectorReport';
+import { BrowserRouter as Router,Routes, Route } from 'react-router-dom';
+import { ThemeContext } from './ThemeContext';
+import { AuthProvider } from './components/AuthContext';
+
+function App() {
+ const [theme, setTheme] = useState('nature');
+
+ useEffect(() => {
+ const html = document.documentElement;
+ html.setAttribute('data-theme', theme);
+ }, [theme]);
+
+ return (
+ <ThemeContext.Provider value={{ theme, setTheme }}>
+ <AuthProvider>
+ <Router>
+ <Routes>
+ <Route path="/" element={<Home />} />
+ <Route path="/about" element={<About />} />
+ <Route path="/login" element={<Login />} />
+ <Route path="/register" element={<Register />} />
+ <Route element={<PrivateRoute />}>
+ <Route path="/profile" element={<Profile />} />
+ <Route path="/datamanagement" element={<DataManagement />} />
+ </Route>
+ <Route path="/preview/:appName" element={<Preview />} />
+ <Route path="/megadetector" element={<MegaDetector />} />
+ <Route path="/zamba" element={<Zamba />} />
+ <Route path="/zamba/result" element={<ZambaResult />} />
+ <Route path="/megadetector/report" element={<MegaDetectorReport />} />
+ <Route path="/megadetector/detections_animal.html" element={<MegaDetectorDetectionsAnimalReport />} />
+ <Route path="/il2bb" element={<Il2bb />} />
+ <Route path="/camera-trap-tools" element={<CameraTrapTools />} />
+ <Route path="/wildcofaceblur" element={<WildcoFaceBlur />} />
+ <Route path="/image-browser-popup" element={<ImageBrowserPopup />} />
+ <Route path="/camera-trap-workflow" element={<CameraTrapWorkflow />} />
+ </Routes>
+ </Router>
+ </AuthProvider>
+ </ThemeContext.Provider>
+ );
+}
+
+export default App;
diff --git a/frontend/src/ThemeContext.js b/frontend/src/ThemeContext.js
new file mode 100644
index 0000000000000000000000000000000000000000..e36b1d674ccc2d461138a4ee7431f6cee942a72f
--- /dev/null
+++ b/frontend/src/ThemeContext.js
@@ -0,0 +1,6 @@
+import React from 'react';
+
+export const ThemeContext = React.createContext({
+ theme: 'nature',
+ setTheme: () => {},
+});
\ No newline at end of file
diff --git a/frontend/src/components/About.jsx b/frontend/src/components/About.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..e041732eedd709e5190b23b62db5c5e6c6026065
--- /dev/null
+++ b/frontend/src/components/About.jsx
@@ -0,0 +1,137 @@
+import React, { useContext } from 'react';
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+
+const About = () => {
+ const { theme, setTheme } = useContext(ThemeContext);
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ {/* Hero */}
+ <div className="hero bg-base-100">
+ <div className="hero-box bg-primary w-10/12 rounded-box h-[50vh] flex items-center justify-center">
+ <div className="hero-content text-center w-7/12">
+ <div className="max-w-full">
+ <h1 className="text-8xl font-bold text-base-100">About Us</h1>
+ <p className="py-6 text-xl text-base-100">
+ Welcome to the CamTrap Hub: Where Wildlife Meets Tech!
+ </p>
+ </div>
+ </div>
+ </div>
+ </div>
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <p className="text-lg text-primary mb-6">
+ Does the journey from setting up camera traps to analyzing wildlife data seem daunting? Say hello to CamTrap Hub—your ultimate ally in the intricate world of wildlife research. With us, managing camera traps becomes not just manageable but downright enjoyable! Our platform brings together a suite of applications meticulously designed to support every phase of camera trap data processing.
+ </p>
+ {/* Timeline - Stages of camera trap data processing */}
+ <h2 className="text-2xl font-bold mb-6 text-primary">This probably is your typical workflow:</h2>
+ <ul className="timeline timeline-snap-icon max-md:timeline-compact timeline-vertical mt-10">
+ <li>
+ <div className="timeline-middle">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 20 20"
+ fill="currentColor"
+ className="h-5 w-5 text-primary">
+ <path
+ fillRule="evenodd"
+ d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z"
+ clipRule="evenodd" />
+ </svg>
+ </div>
+ <div className="timeline-start mb-10 md:text-end">
+ <div className="text-lg font-black text-primary">Data Retrieval</div>
+ <p className="text-base-content">Retrieve images and related data from camera trap. Store the data in a database for further use. These data include project metadata, media files, deployments information, and observation metadata.</p>
+ </div>
+ <hr />
+ </li>
+ <li>
+ <hr />
+ <div className="timeline-middle">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 20 20"
+ fill="currentColor"
+ className="h-5 w-5 text-primary">
+ <path
+ fillRule="evenodd"
+ d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z"
+ clipRule="evenodd" />
+ </svg>
+ </div>
+ <div className="timeline-end mb-10">
+ <div className="text-lg font-black text-primary">Data Review and Organisation</div>
+ <p className="text-base-content">Review raw data from camera traps, files may need to be labelled and organised into folders. The most common workflow is to categorise images into separate folders per locality or time period.</p>
+ </div>
+ <hr />
+ </li>
+ <li>
+ <hr />
+ <div className="timeline-middle">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 20 20"
+ fill="currentColor"
+ className="h-5 w-5 text-primary">
+ <path
+ fillRule="evenodd"
+ d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z"
+ clipRule="evenodd" />
+ </svg>
+ </div>
+ <div className="timeline-start mb-10 md:text-end">
+ <div className="text-lg font-black text-primary">Identification and Annotation</div>
+ <p className="text-base-content">Identify species and annotate images. This stage is often the most time-consuming one. Image annotation such as extraction of metadata from images can be performed completely manually, but is becoming more facilitated by technology especially with the development of AI.</p>
+ </div>
+ <hr />
+ </li>
+ <li>
+ <hr />
+ <div className="timeline-middle">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 20 20"
+ fill="currentColor"
+ className="h-5 w-5 text-primary">
+ <path
+ fillRule="evenodd"
+ d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z"
+ clipRule="evenodd" />
+ </svg>
+ </div>
+ <div className="timeline-end mb-10">
+ <div className="text-lg font-black text-primary">Data Analysis</div>
+ <p className="text-base-content">Analyse the data to extract information about species presence and activity and answer ecological and biological questions.</p>
+ </div>
+ <hr />
+ </li>
+ <li>
+ <hr />
+ <div className="timeline-middle">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 20 20"
+ fill="currentColor"
+ className="h-5 w-5 text-primary">
+ <path
+ fillRule="evenodd"
+ d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z"
+ clipRule="evenodd" />
+ </svg>
+ </div>
+ <div className="timeline-start mb-10 md:text-end">
+ <div className="text-lg font-black text-primary">Data Sharing and Publication</div>
+ <p className="text-base-content">Share and publish the data for scientific research and conservation efforts. Data publication is the process of making biodiversity data open and FAIR, which guarantees that your camera trap data can be found, accessed, integrated and reused.</p>
+ </div>
+ </li>
+ </ul>
+ <p className="text-lg text-primary mt-6">At CamTrap Hub, we understand that not everyone speaks the intricate language of programming. That's why we've curated tools that require zero coding skills, all equipped with intuitive interfaces that make wildlife data analysis as natural as observing the wild itself. Whether you're a veteran researcher or a budding naturalist, our hub turns complex data into compelling insights with just a few clicks. Embrace the simplicity of our all-in-one platform, where your focus shifts from figuring out software to uncovering the secrets of the natural world.</p>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default About;
\ No newline at end of file
diff --git a/frontend/src/components/ApplicationCard.jsx b/frontend/src/components/ApplicationCard.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..b079d0a7c432d187ca0d44b4776581e54d7fa94e
--- /dev/null
+++ b/frontend/src/components/ApplicationCard.jsx
@@ -0,0 +1,46 @@
+import React, { useState } from 'react'
+import Preview from './Preview';
+
+function ApplicationCard({ appInfo }) {
+ const [isPreviewOpen, setIsPreviewOpen] = useState(false);
+ const [imageError, setImageError] = useState(false);
+
+ const handleImageError = () => {
+ setImageError(true);
+ };
+
+ return (
+ <div className="card bg-base-100 w-96 shadow-xl">
+ <figure className="w-full h-48 bg-base-200">
+ {!imageError ? (
+ <img
+ src={appInfo.image}
+ alt={appInfo.name}
+ onError={handleImageError}
+ />
+ ) : (
+ <div className="avatar placeholder">
+ <div className="bg-accent text-neutral-content w-40 rounded-full">
+ <span className="text-3xl text-primary text-center">{appInfo.name}</span>
+ </div>
+ </div>
+ )}
+ </figure>
+ <div className="card-body">
+ <h2 className="card-title">{appInfo.name}</h2>
+ <p>{appInfo.description}</p>
+ <div className="card-actions justify-between items-center">
+ <div>
+ {appInfo.tags.map((tag, index) => (
+ <div key={index} className="badge badge-outline mr-2">{tag}</div>
+ ))}
+ </div>
+ <button className="btn btn-primary" onClick={() => setIsPreviewOpen(true)}>Details</button>
+ </div>
+ </div>
+ {isPreviewOpen && <Preview appInfo={appInfo} onClose={() => setIsPreviewOpen(false)} />}
+ </div>
+ )
+}
+
+export default ApplicationCard;
diff --git a/frontend/src/components/AuthContext.jsx b/frontend/src/components/AuthContext.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..8e88452e3689ad64413e25644acf9cc09c1199a8
--- /dev/null
+++ b/frontend/src/components/AuthContext.jsx
@@ -0,0 +1,41 @@
+import React, { createContext, useState, useEffect, useContext } from 'react';
+
+const AuthContext = createContext(null);
+
+export const AuthProvider = ({ children }) => {
+ const [isLoggedIn, setIsLoggedIn] = useState(null);
+ const [currentUser, setCurrentUser] = useState(null);
+
+ const checkLoginStatus = async () => {
+ try {
+ const response = await fetch('/api/user', {
+ credentials: 'include'
+ });
+
+ if (response.ok) {
+ const userData = await response.json();
+ setIsLoggedIn(true);
+ setCurrentUser(userData);
+ } else {
+ setIsLoggedIn(false);
+ setCurrentUser(null);
+ }
+ } catch (error) {
+ console.error('Error:', error);
+ setIsLoggedIn(false);
+ setCurrentUser(null);
+ }
+ };
+
+ useEffect(() => {
+ checkLoginStatus();
+ }, []);
+
+ return (
+ <AuthContext.Provider value={{ isLoggedIn, currentUser, checkLoginStatus }}>
+ {children}
+ </AuthContext.Provider>
+ );
+};
+
+export const useAuth = () => useContext(AuthContext);
diff --git a/frontend/src/components/CameraTrapTools.jsx b/frontend/src/components/CameraTrapTools.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..e612be0bcc64b3560134170921889b67cbe2a12c
--- /dev/null
+++ b/frontend/src/components/CameraTrapTools.jsx
@@ -0,0 +1,513 @@
+import React, { useState, useContext } from 'react';
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+const CameraTrapTools = () => {
+ const [activeTab, setActiveTab] = useState('imageProcessing');
+ const { theme, setTheme } = useContext(ThemeContext);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Camera Trap Tools</h1>
+ <p className="text-lg mb-8 text-center text-gray-700">A suite of tools for managing time-lapse recordings taken by camera traps (a.k.a. trail cameras).</p>
+ <div role="tablist" className="tabs tabs-boxed">
+ <a role="tab" className={`tab ${activeTab === 'imageProcessing' ? 'tab-active' : ''}`} onClick={() => setActiveTab('imageProcessing')}>Image Processing</a>
+ <a role="tab" className={`tab ${activeTab === 'annotation' ? 'tab-active' : ''}`} onClick={() => setActiveTab('annotation')}>Annotation</a>
+ <a role="tab" className={`tab ${activeTab === 'reports' ? 'tab-active' : ''}`} onClick={() => setActiveTab('reports')}>Reports</a>
+ </div>
+ </div>
+
+ {activeTab === 'imageProcessing' && <CameraTrapToolsProcessing />}
+ {activeTab === 'annotation' && <CameraTrapToolsAnnotation />}
+ {activeTab === 'reports' && <CameraTrapToolsReports />}
+ </div>
+ )
+}
+
+const CameraTrapToolsProcessing = () => {
+ const [success, setSuccess] = useState(false);
+ const [videoSuccess, setVideoSuccess] = useState(false);
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+ const handleOpenFileBrowser = () => {
+ let allowedTypes = ['image'];
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = async () => {
+ try {
+ const formData = new FormData();
+
+ selectedItems.forEach(item => {
+ formData.append('images', item.path);
+ });
+
+ const response = await fetch('/api/camera-trap-tools/autocopy/upload', {
+ method: 'POST',
+ body: formData
+ })
+ const data = await response.json()
+ setUploadSuccess(true);
+ console.log(data);
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleProcess = async () => {
+ const animalDetection = document.querySelector('.detector').checked;
+ try {
+ const response = await fetch('/api/camera-trap-tools/autocopy/process', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify({ animalDetection })
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleDownload = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/autocopy/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapTools_autocopy_result.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleCreateVideo = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-video', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setVideoSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleDownloadVideos = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-video/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapTools_createvideos_result.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Autocopy</h2>
+ <p className="text-sm text-gray-700 text-center">Downloading and automatic renaming of images from SD cards.</p>
+ <h3 className="text-2xl font-semibold mb-4">Upload Images for renaming</h3>
+ <div>
+ <label htmlFor="images" className="block text-sm font-medium text-gray-700 mb-2">
+ Upload image(s):
+ </label>
+ <button onClick={() => handleOpenFileBrowser()} name="images" className="btn btn-primary mr-2">Select Images</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Images/Videos:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ </div>
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Upload Selected Images</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+
+ <label className="label cursor-pointer justify-start">
+ <span className="label-text mr-4">Activate animal detection? </span>
+ <input type="checkbox" className="detector toggle toggle-primary" defaultChecked />
+ </label>
+
+ <button className='btn btn-secondary mt-4' onClick={handleProcess}>Process data</button>
+ {success && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Processing completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>a log file</li>
+ <li>a status HTML file</li>
+ <li>copies of uploaded images that were labeled inside 'RawImages' folder</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleDownload}>Download results</button>
+ </div>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mt-8 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center text-primary">Create video</h2>
+ <p className="text-sm text-gray-700 text-center">Compressing images to MP4 video files.</p>
+ <h3 className="text-2xl font-semibold mb-4">Create video using autocopy results</h3>
+ <p className="text-sm text-gray-700">Note: this will only work if autocopy has been run before.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleCreateVideo}>Create video</button>
+ {videoSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Video creation completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download videos</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the videos here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>videos created from labeled images</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleDownloadVideos}>Download videos</button>
+ </div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ );
+};
+
+const CameraTrapToolsAnnotation = () => {
+ const [annotationSuccess, setAnnotationSuccess] = useState(false);
+
+ const handleCreateAnnotation = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-annotation', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setAnnotationSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleDownloadAnnotation = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-annotation/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapTools_annotation_files.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+ return (
+ <div className="container mx-auto px-4">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 my-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center text-primary">Create annotation</h2>
+ <p className="text-sm text-gray-700 text-center">Create Draft Annotation From Animal Detector Output.</p>
+ <h3 className="text-2xl font-semibold mb-4">Create draft annotation</h3>
+ <p className="text-sm text-gray-700">Note: this will only work if you select animal dectection when running autocopy.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleCreateAnnotation}>Create annotation</button>
+ {annotationSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Annotation creation completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download annotation files</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the files here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>annotation files created based on your animal detector output</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleDownloadAnnotation}>Download annotation files</button>
+ </div>
+ </div>
+ )
+}
+
+const CameraTrapToolsReports = () => {
+ const [annotationReportSuccess, setAnnotationReportSuccess] = useState(false);
+ const [captureReportSuccess, setCaptureReportSuccess] = useState(false);
+ const [captureReportMonth, setCaptureReportMonth] = useState('');
+
+ const handleCreateAnnotationReport = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-annotation/report', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setAnnotationReportSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleDownloadAnnotationReport = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/create-annotation/report/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapTools_annotation_report.csv'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleCreateCaptureReport = async (e) => {
+ e.preventDefault();
+ try {
+ const formData = new FormData(e.target);
+ setCaptureReportMonth(formData.get('capture-report-month'));
+ const response = await fetch('/api/camera-trap-tools/capture-report', {
+ method: 'POST',
+ body: formData
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setCaptureReportSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleDownloadCaptureReport = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-tools/capture-report/download', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify({ 'capture-report-month': captureReportMonth })
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = `CameraTrapTools_capture_report_${captureReportMonth}.csv`
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ return (
+ <div className="container mx-auto px-4">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 my-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center text-primary">Generate reports</h2>
+ <p className="text-sm text-gray-700 text-center">Generate reports for analysis or status of camera data processing.</p>
+ <h3 className="text-2xl font-semibold mb-4">Generate annotation report</h3>
+ <p className="text-sm text-gray-700">This program combines the contents of all video annotation files and dumps them to a single CSV file suitable for analyzing with the statistical software of your choice. Each record (line) of the report represents one annotation and contains the following information:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>site: Camera site</li>
+ <li>file: The name of the annotation file this record came from</li>
+ <li>activity: The name of the activity or event associated with this annotation</li>
+ <li>kind: Kind of animal associated with this annotation, either 'focal' or 'commensal'</li>
+ <li>individual: ID of the animal associated with this annotation</li>
+ <li>startTime: Time the activity began</li>
+ <li>endTime: Time the activity ended</li>
+ <li>user: Name of the user who created the annotation</li>
+ </ul>
+ <button className='btn btn-secondary mt-4 mb-4' onClick={handleCreateAnnotationReport}>Create annotation report</button>
+ {annotationReportSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Annotation report creation completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <h2 className="text-2xl font-semibold mb-4">Download annotation report</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the files here.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleDownloadAnnotationReport}>Download annotation report</button>
+
+ <div className="divider"></div>
+
+ <h3 className="text-2xl font-semibold mb-4">Generate capture report from video</h3>
+ <p className="text-sm text-gray-700">This program generates a CSV file detailing the number of images captured by each camera on each day, whether those images have been compressed into videos, and if an annotation file exists for that camera day.</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>Camera: Camera site and view</li>
+ <li>Date</li>
+ <li>ImageCount: Number of images in the default_image_folder that are associated with this camera day</li>
+ <li>FrameCount: Number of frames in the video associated with this camera day</li>
+ <li>Video: Boolean value indicating if a compressed video exists for this camera day</li>
+ <li>Annotation: Boolean value indicating if an annotation file exists for this camera day</li>
+ </ul>
+ <form onSubmit={handleCreateCaptureReport}>
+ <div className="form-control flex flex-row items-center mt-4">
+ <label htmlFor="capture-report-month" className="block text-base font-medium text-gray-700 mb-2">Capture report month: </label>
+ <input
+ type="text"
+ placeholder="YYYY-MM"
+ name="capture-report-month"
+ className="input input-bordered w-full max-w-xs ml-4"
+ pattern="\d{4}-\d{2}"
+ title="Please enter a date in the format YYYY-MM"
+ required
+ />
+ </div>
+ <button className='btn btn-secondary mt-4 mb-4' type="submit">Create capture report</button>
+ </form>
+ {captureReportSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Capture report creation completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <h2 className="text-2xl font-semibold mb-4">Download capture report</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the files here.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleDownloadCaptureReport}>Download capture report</button>
+ </div>
+ </div>
+ )
+}
+
+export default CameraTrapTools;
\ No newline at end of file
diff --git a/frontend/src/components/CameraTrapWorkflow.jsx b/frontend/src/components/CameraTrapWorkflow.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..a1dc9dcd1ca0c5f284340d6849b1b38652f3be01
--- /dev/null
+++ b/frontend/src/components/CameraTrapWorkflow.jsx
@@ -0,0 +1,461 @@
+import React, { useState, useContext } from 'react';
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+const CameraTrapWorkflow = () => {
+ const { theme, setTheme } = useContext(ThemeContext);
+ const [activeTab, setActiveTab] = useState('extractMetadata');
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Camera Trap Workflow</h1>
+ <p className="text-lg mb-8 text-center text-gray-700">A semi-automatic workflow to process camera trap images in R.</p>
+ <div role="tablist" className="tabs tabs-boxed">
+ <a role="tab" className={`tab ${activeTab === 'extractMetadata' ? 'tab-active' : ''}`} onClick={() => setActiveTab('extractMetadata')}>Extract Metadata</a>
+ <a role="tab" className={`tab ${activeTab === 'classification' ? 'tab-active' : ''}`} onClick={() => setActiveTab('classification')}>Classification</a>
+ <a role="tab" className={`tab ${activeTab === 'qualityCheck' ? 'tab-active' : ''}`} onClick={() => setActiveTab('qualityCheck')}>Quality Check</a>
+ <a role="tab" className={`tab ${activeTab === 'formatting' ? 'tab-active' : ''}`} onClick={() => setActiveTab('formatting')}>Formatting</a>
+ </div>
+ </div>
+ {activeTab === 'extractMetadata' && <ExtractMetadata />}
+ {activeTab === 'classification' && <Classification />}
+ {activeTab === 'qualityCheck' && <QualityCheck />}
+ {activeTab === 'formatting' && <Formatting />}
+ </div>
+ )
+}
+
+const ExtractMetadata = () => {
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+ const [success, setSuccess] = useState(false);
+
+ const handleOpenFileBrowser = () => {
+ let allowedTypes = ['folder'];
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = async () => {
+ try {
+ const formData = new FormData();
+
+ selectedItems.forEach(item => {
+ formData.append('images', item.path);
+ });
+
+ const response = await fetch('/api/camera-trap-workflow/upload', {
+ method: 'POST',
+ body: formData
+ })
+ const data = await response.json()
+ setUploadSuccess(true);
+ console.log(data);
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleProcess = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/extract-metadata/process', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleDownload = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/extract-metadata/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapWorkload_metadata_renamed.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Extract Metadata and Rename Images</h2>
+ <p className="text-sm text-gray-700">The metadata saved in the image files such as date and time when the image was taken or the temperature will be extracted. What kind of metadata is saved varies between camera types and settings. One metadata file per site will be saved in the metadata-folder. Then the images will be renamed with a unique name that consists of the siteID, the date when the image was taken and unique number for images from the same day (for example site1_2021-01-01_0001.JPG). The renamed images will be saved in the renamed_images- folder.</p>
+ <h3 className="text-2xl font-semibold mb-4 mt-4">Upload Images for renaming</h3>
+ <div>
+ <label htmlFor="folders" className="block text-sm font-medium text-gray-700 mb-2">
+ Upload folder of images:
+ </label>
+ <button onClick={() => handleOpenFileBrowser()} name="folders" className="btn btn-primary mr-2">Select Folder</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Foler:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ </div>
+ <div>
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Upload Selected Images</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+ </div>
+
+ <button className='btn btn-secondary mt-4' onClick={handleProcess}>Process data</button>
+ {success && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>You may download the metadata file and renamed images now.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>metadata file(s) per site</li>
+ <li>copies of uploaded images that were reanmed inside 'images_renamed' folder</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleDownload}>Download results</button>
+ </div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ )
+}
+
+const Classification = () => {
+ const [autoSuccess, setAutoSuccess] = useState(false);
+
+ const handleAutoClassification = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/auto-classify', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setAutoSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleAutoDownload = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/auto-classify/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapWorkload_auto_classification.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleManualClassification = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/manual-classify', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ window.open(data.shiny_url, '_blank');
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Auto Classification</h2>
+ <p className="text-sm text-gray-700">Classify images automatically. <b>It will automatically use the reenamed images and metadata file from Extract Metadata step.</b> Once triggered, all images will be classified automatically using the specified model, the output with the class with the highest confidence (guess1) will be saved in the automatic_classification folder which can be downloaded.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleAutoClassification}>Run Classification</button>
+ {autoSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>You may download the output now.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>classification output file(s) per site</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleAutoDownload}>Download results</button>
+ </div>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Manual Classification</h2>
+ <p className="text-sm text-gray-700 text-center">Select images for manual labelling (classification).</p>
+ <button className='btn btn-secondary mt-4' onClick={handleManualClassification}>Launch Manual Classification</button>
+ </div>
+ </div>
+ </div>
+ )
+}
+
+const QualityCheck = () => {
+ const [launchSuccess, setLaunchSuccess] = useState(false);
+ const [output, setOutput] = useState('');
+
+ const handleQualityCheck = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/quality-check', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ if (data.status === 'success') {
+ window.open(data.shiny_url, '_blank');
+ setLaunchSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleQualityCheckDownload = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/quality-check/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapWorkload_quality_check.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ const handleModalEvaluation = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/modal-evaluation', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ console.log(data);
+ setOutput(data.output.replace(/\\n/g, '\n'));
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Quality Check</h2>
+ <p className="text-sm text-gray-700 text-center">Verify the model labels and performance. You can select the type of quality check (random selection of all images, per class or per confidence class) and the number of images you want to check. Then, random images will be selected and presented for manual labelling. A file with the labels will be saved in the quality_check folder.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleQualityCheck}>Launch Quality Check</button>
+ {launchSuccess && (
+ <div role="alert" className="alert alert-info mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Quality check shiny app launched successfully! Please remember to close the app (scroll to the bottom and click 'STOP') before you leave the page.</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>quality check labels file(s) per site</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleQualityCheckDownload}>Download results</button>
+ </div>
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Modal Evaluation</h2>
+ <p className="text-sm text-gray-700 text-center">After finishing the quality check, use this function to calculate overall model accuracy, accuracy per confidence class as well as precision, recall and F1 score for each class.</p>
+ <p className="text-sm text-gray-700 text-center">You may go back to Classification - <b>Manual Classification</b> to correct model labels.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleModalEvaluation}>Modal Evaluation</button>
+ {output && (
+ <div className="mt-4">
+ <h3>Output:</h3>
+ <pre>{output}</pre>
+ </div>
+ )}
+ </div>
+ </div>
+ </div>
+ )
+}
+
+const Formatting = () => {
+ const [success, setSuccess] = useState(false);
+
+ const handleFormatting = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/formatting', {
+ method: 'POST'
+ })
+ const data = await response.json();
+ if (data.status === 'success') {
+ setSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleFormatDownload = async () => {
+ try {
+ const response = await fetch('/api/camera-trap-workflow/formatting/download', {
+ method: 'GET'
+ })
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'CameraTrapWorkload_final_formatting.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-3xl font-semibold mb-4 text-center">Formatting</h2>
+ <p className="text-sm text-gray-700 text-center">Combine your automatic and manual image labels.</p>
+ <button className='btn btn-secondary mt-4' onClick={handleFormatting}>Start formatting</button>
+ {success && (
+ <div role="alert" className="alert alert-info mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Formatted successfully!</span>
+ </div>
+ )}
+
+ <div className="divider"></div>
+
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>formatted labels file(s) per locality</li>
+ </ul>
+ <button className='btn btn-secondary mt-4' onClick={handleFormatDownload}>Download results</button>
+ </div>
+ </div>
+ </div>
+ )
+}
+
+export default CameraTrapWorkflow;
\ No newline at end of file
diff --git a/frontend/src/components/DataManagement.jsx b/frontend/src/components/DataManagement.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..e5222fb58e1f12af666932924318144eef5c02e1
--- /dev/null
+++ b/frontend/src/components/DataManagement.jsx
@@ -0,0 +1,29 @@
+import React, { useState, useContext } from 'react';
+import NavBar from './NavBar';
+import Dashboard from './datamanagement/Dashboard';
+import DataSpace from './datamanagement/DataSpace';
+import { ThemeContext } from '../ThemeContext';
+
+const DataManagement = () => {
+ const [activeTab, setActiveTab] = useState('dataSpace');
+ const { theme, setTheme } = useContext(ThemeContext);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Data Management</h1>
+ <div role="tablist" className="tabs tabs-boxed">
+ <a role="tab" className={`tab ${activeTab === 'dashboard' ? 'tab-active' : ''}`} onClick={() => setActiveTab('dashboard')}>Insights</a>
+ <a role="tab" className={`tab ${activeTab === 'dataSpace' ? 'tab-active' : ''}`} onClick={() => setActiveTab('dataSpace')}>Data Space</a>
+ </div>
+ </div>
+
+ {activeTab === 'dashboard' && <Dashboard />}
+ {activeTab === 'dataSpace' && <DataSpace />}
+
+ </div>
+ )
+}
+
+export default DataManagement;
\ No newline at end of file
diff --git a/frontend/src/components/Home.jsx b/frontend/src/components/Home.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..2cdb584643cfb584f01ba9e0b3c27d1a7f5ad297
--- /dev/null
+++ b/frontend/src/components/Home.jsx
@@ -0,0 +1,117 @@
+import React, {useState, useEffect, useContext} from 'react'
+import NavBar from './NavBar'
+import ApplicationCard from './ApplicationCard'
+import { ThemeContext } from '../ThemeContext';
+
+function Home() {
+ const [searchTerm, setSearchTerm] = useState('');
+ const [purpose, setPurpose] = useState('');
+ const { theme, setTheme } = useContext(ThemeContext);
+ const [allApps, setAllApps] = useState([]);
+ const [filteredApps, setFilteredApps] = useState(allApps);
+
+ useEffect(() => {
+ const fetchApps = async () => {
+ try {
+ const response = await fetch('/api/apps');
+ const data = await response.json();
+ setAllApps(data);
+ setFilteredApps(data);
+ } catch (error) {
+ console.error('Error fetching apps:', error);
+ }
+ };
+
+ fetchApps();
+ }, []);
+
+ const handleSearch = () => {
+ const filtered = allApps.filter(app =>
+ app.name.toLowerCase().includes(searchTerm.toLowerCase()) &&
+ (purpose === '' || app.tags.includes(purpose))
+ );
+ setFilteredApps(filtered);
+ };
+
+ return (
+ <div>
+ <NavBar theme={theme} setTheme={setTheme}/>
+
+ {/* Hero Section */}
+ <div className='relative'>
+ <div className="hero bg-base-100">
+ <div className="hero-box bg-primary w-10/12 rounded-box h-[50vh] flex items-center justify-center">
+ <div className="hero-content text-center w-7/12">
+ <div className="max-w-full">
+ <h1 className="text-8xl font-bold text-base-100">CamTrap Hub</h1>
+ <p className="py-6 text-base-100">
+ Your all-in-one camera trapper app store where you can find and run apps for image classification, object detection, and blank filtering.
+ </p>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ {/* Search Bar */}
+ <div className="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2">
+ <div className="bg-white rounded-full shadow-lg p-4">
+ <div className="flex flex-wrap gap-2">
+ <input
+ type="text"
+ placeholder="Name"
+ className="input flex-grow bg-white"
+ value={searchTerm}
+ onChange={(e) => setSearchTerm(e.target.value)}
+ />
+ <div className="divider divider-horizontal"></div>
+ <select
+ className="select flex-grow bg-white"
+ value={purpose}
+ onChange={(e) => setPurpose(e.target.value)}
+ >
+ <option value="" disabled selected>Purpose</option>
+ <option value="Classification">Image Classification</option>
+ <option value="Object Detection">Object Detection</option>
+ <option value="Filtering">Blank Filtering</option>
+ <option value="Train Model">Train Model</option>
+ <option value="">None</option>
+ </select>
+ <button className="btn btn-primary rounded-full" onClick={handleSearch}>
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 16 16"
+ fill="currentColor"
+ className="h-4 w-4 opacity-70"
+ >
+ <path
+ fillRule="evenodd"
+ d="M9.965 11.026a5 5 0 1 1 1.06-1.06l2.755 2.754a.75.75 0 1 1-1.06 1.06l-2.755-2.754ZM10.5 7a3.5 3.5 0 1 1-7 0 3.5 3.5 0 0 1 7 0Z"
+ clipRule="evenodd"
+ />
+ </svg>
+ </button>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div className='flex flex-col items-center justify-center mt-20 mb-20 pt-8'>
+ <h2 className='text-3xl text-primary font-bold mb-6'>Featured Apps</h2>
+ <div className='flex flex-row gap-4 flex-wrap justify-center'>
+ {filteredApps.map((app, index) => (
+ <ApplicationCard appInfo={app} key={index} />
+ ))}
+ </div>
+ </div>
+
+ {/* Footer */}
+ <footer className="footer footer-center bg-base-300 text-base-content p-4">
+ <aside>
+ <p>Copyright © {new Date().getFullYear()} - All right reserved by Yan Tung Lam</p>
+ </aside>
+ </footer>
+ </div>
+ );
+}
+
+export default Home;
\ No newline at end of file
diff --git a/frontend/src/components/Il2bb.jsx b/frontend/src/components/Il2bb.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..c58d9bdb1ccb0cb54d96ef76ef47791f849485b6
--- /dev/null
+++ b/frontend/src/components/Il2bb.jsx
@@ -0,0 +1,212 @@
+import React, { useState } from 'react';
+import NavBar from './NavBar';
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+const Il2bb = () => {
+ const [theme, setTheme] = useState('nature');
+ const [mappingFile, setMappingFile] = useState(null);
+ const [processing, setProcessing] = useState(false);
+ const [success, setSuccess] = useState(false);
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+ const handleOpenFileBrowser = () => {
+ let allowedTypes = ['image'];
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = () => {
+ const formData = new FormData();
+
+ if (mappingFile) {
+ formData.append('mappingFile', mappingFile);
+ }
+
+ selectedItems.forEach(item => {
+ formData.append('images', item.path);
+ });
+
+ fetch('/api/il2bb/upload', {
+ method: 'POST',
+ body: formData
+ })
+ .then(response => {
+ if (!response.ok) {
+ throw new Error('Network response was not ok');
+ }
+ setUploadSuccess(true);
+ return response.json();
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ });
+ };
+
+ const handleProcessData = async () => {
+ try {
+ const stage1Reponse = await fetch('/api/il2bb/trigger_stage1', {
+ method: 'POST',
+ body: mappingFile
+ })
+ setProcessing(true);
+ const stage1Data = await stage1Reponse.json();
+ console.log('Stage 1 response: ', stage1Data);
+
+ if (stage1Data.status === 'success') {
+ const stage2Response = await fetch('/api/il2bb/trigger_stage2', {
+ method: 'POST',
+ })
+ const stage2Data = await stage2Response.json();
+ console.log('Stage 2 response: ', stage2Data);
+ setProcessing(false);
+ setSuccess(true);
+ }
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleDownload = async () => {
+ try {
+ const response = await fetch('/api/il2bb/download', {
+ method: 'GET'
+ });
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'IL2BB_batch_result.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ };
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Image Level Label to Bounding Box (IL2BB)</h1>
+ <p className="text-lg mb-8 text-center text-gray-700">A pipeline for generating labeled bounding boxes.</p>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4">Upload Data</h2>
+ {/* CSV file */}
+ <div>
+ <h3 className="text-lg font-semibold mb-2">Label Map (CSV file)</h3>
+ <p className="text-sm text-gray-700">The main input to the batching stage is a simple csv file (no header row) with two fields:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>image file name</li>
+ <li>label</li>
+ </ul>
+ <p className="text-sm text-gray-700">*Note: The IL2BB pipeline expects all of the targets in the image to be of the same species. When creating your label map, skip any images that have more than one species in them. Images can have multiple targets of the same species.</p>
+ <p className="text-sm text-gray-700">See <a className="link" href="https://github.com/persts/IL2BB/tree/main/UseCase">here</a> for more details.</p>
+ <label htmlFor="mapping-file" className="block text-sm font-medium text-gray-700 mt-4 mb-2">
+ Label Map (CSV file):
+ </label>
+ <input
+ type="file"
+ name="mapping-file"
+ accept=".csv"
+ className="file-input file-input-bordered file-input-primary bg-white w-full max-w-xs"
+ onChange={(e) => setMappingFile(e.target.files[0])}
+ />
+ {mappingFile && (
+ <div className="mt-4">
+ <p className="font-semibold">Uploaded label CSV file:</p>
+ <ul>
+ <li>{mappingFile.name}</li>
+ </ul>
+ </div>
+ )}
+ </div>
+ {/* Images */}
+ <div className="mt-2">
+ <label htmlFor="images" className="block text-sm font-medium text-gray-700 mb-2">
+ Images:
+ </label>
+ <button onClick={() => handleOpenFileBrowser()} name="images" className="btn btn-primary mr-2">Select Images</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Images:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ </div>
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Upload Selected Items</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+ </div>
+
+ <button className='btn btn-primary' onClick={handleProcessData}>Process data</button>
+ {processing && (
+ <div role="alert" className="alert alert-info mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ fill="none"
+ viewBox="0 0 24 24"
+ className="h-6 w-6 shrink-0 stroke-current">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M13 16h-1v-4h-1m1-4h.01M21 12a9 9 0 11-18 0 9 9 0 0118 0z"></path>
+ </svg>
+ <span>You've successfully triggered the processing. It may take a while to finish.</span>
+ </div>
+ )}
+ {success && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Processing completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mt-8 mb-8">
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>a annotation file (il2bb.bbx)</li>
+ <li>a log file</li>
+ <li>copies of the images used in the batch</li>
+ </ul>
+ <button className='btn btn-primary mt-4' onClick={handleDownload}>Download results</button>
+ </div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ );
+};
+
+export default Il2bb;
\ No newline at end of file
diff --git a/frontend/src/components/ImageBrowserPopup.jsx b/frontend/src/components/ImageBrowserPopup.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..9a153abd1c7564917e41aef0ce3e608330d4c64a
--- /dev/null
+++ b/frontend/src/components/ImageBrowserPopup.jsx
@@ -0,0 +1,169 @@
+import React, { useState, useEffect, useMemo, useCallback } from 'react';
+
+const ImageBrowserPopup = ({ onSelect, allowedTypes = ['image', 'video', 'folder'] }) => {
+ const [files, setFiles] = useState([]);
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [error, setError] = useState(null);
+ const [isLoading, setIsLoading] = useState(false);
+ const [fileStructure, setFileStructure] = useState({});
+ const [selectedFolder, setSelectedFolder] = useState(null);
+ const s3Folder = 'uploads/';
+ const fileTypesDict = {'image': ['jpg', 'jpeg', 'png', 'gif'], 'video': ['mp4', 'mov', 'avi', 'mkv'], 'folder': ['folder']};
+
+ useEffect(() => {
+ fetchData();
+ }, []);
+
+ const fetchData = useCallback(async () => {
+ setIsLoading(true);
+ setError(null);
+ try {
+ const response = await fetch('/api/dataSpace/list_files');
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ const data = await response.json();
+ setFiles(data.files);
+ setFileStructure(data.file_structure);
+ } catch (error) {
+ console.error('Error fetching data:', error);
+ setError('Failed to fetch data. Please try again.');
+ } finally {
+ setIsLoading(false);
+ }
+ }, []);
+
+ const handleItemSelect = useCallback((item) => {
+ console.log('Item selected:', item);
+ setSelectedItems(prevSelected => {
+ if (prevSelected.some(i => i.path === item.path)) {
+ return prevSelected.filter(i => i.path !== item.path);
+ } else {
+ return [...prevSelected, item];
+ }
+ });
+ }, []);
+
+ const handleSelectItems = () => {
+ if (selectedItems.length > 0) {
+ console.log('Found selected items:', selectedItems);
+ onSelect(selectedItems);
+ document.getElementById('file_browser_modal').close();
+ }
+ };
+
+ // const isFileTypeAllowed = (file) => {
+ // console.log('Checking file:', file);
+ // console.log('Allowed types:', allowedTypes);
+
+ // if (!file || !file.category) {
+ // console.log('File or file category is undefined');
+ // return false;
+ // }
+
+ // console.log('File category:', file.category);
+
+ // return allowedTypes.some(type => {
+ // console.log('Checking allowed type:', type);
+ // if (type === 'folder') {
+ // return file.category === 'folder';
+ // }
+ // const isAllowed = file.category === type;
+ // console.log(`Is ${file.category} allowed for ${type}:`, isAllowed);
+ // return isAllowed;
+ // });
+ // };
+ const isFileTypeAllowed = useCallback((file) => {
+ if (!file || !file.category) {
+ return false;
+ }
+ return allowedTypes.some(type => {
+ if (type === 'folder') {
+ return file.category === 'folder';
+ }
+ return file.category === type;
+ });
+ }, [allowedTypes]);
+
+ const renderFileStructure = useMemo(() => {
+ const render = (structure, path = '') => {
+ return Object.keys(structure).map((key) => {
+ const currentPath = path ? `${path}/${key}` : key;
+ if (key === 'files') {
+ return structure[key].map((fileInStructure, index) => {
+ const file = files.find(f => f.path === fileInStructure.path);
+ const isAllowed = file ? isFileTypeAllowed(file) : false;
+ return (
+ <li key={index}>
+ <label className="flex items-center">
+ <input
+ type="checkbox"
+ className="checkbox mr-2"
+ checked={selectedItems.some(item => item.path === fileInStructure.path)}
+ onChange={() => handleItemSelect(file || fileInStructure)}
+ disabled={!isAllowed}
+ />
+ {fileInStructure.name} ({file ? file.type : 'Unknown'})
+ </label>
+ </li>
+ );
+ });
+ } else {
+ return (
+ <li key={key}>
+ <details>
+ <summary>
+ <label className="flex items-center">
+ <input
+ type="checkbox"
+ className="checkbox mr-2"
+ checked={selectedItems.some(item => item.path === currentPath)}
+ onChange={() => handleItemSelect({path: currentPath, type: 'folder', name: key})}
+ disabled={!allowedTypes.includes('folder')}
+ />
+ {key}
+ </label>
+ </summary>
+ <ul>{render(structure[key], currentPath)}</ul>
+ </details>
+ </li>
+ );
+ }
+ });
+ };
+ return render;
+ }, [files, selectedItems, allowedTypes, isFileTypeAllowed, handleItemSelect]);
+
+ return (
+ <dialog id="file_browser_modal" className="modal">
+ <div className="modal-box w-11/12 max-w-5xl">
+ <form method="dialog">
+ <button className="btn btn-sm btn-circle btn-ghost absolute right-2 top-2">✕</button>
+ </form>
+ <h3 className="font-bold text-lg mb-2">Select files or folders</h3>
+ <p className="mb-4 text-sm">Use the checkbox to select files or folders. You can select multiple files and folders to upload.</p>
+ {isLoading && <p>Loading data...</p>}
+ {error && <p className="text-red-500">{error}</p>}
+ <div className="file-structure overflow-y-auto max-h-96">
+ <ul className="menu menu-xs bg-base-200 rounded-lg w-full">
+ {renderFileStructure(fileStructure)}
+ </ul>
+ </div>
+ <div className="modal-action">
+ <form method="dialog">
+ <button className="btn btn-secondary mr-2">Cancel</button>
+ </form>
+ <button
+ className="btn btn-primary"
+ onClick={handleSelectItems}
+ disabled={selectedItems.length === 0}
+ >
+ Select ({selectedItems.length})
+ </button>
+ </div>
+ </div>
+ </dialog>
+ );
+};
+
+export default ImageBrowserPopup;
\ No newline at end of file
diff --git a/frontend/src/components/Login.jsx b/frontend/src/components/Login.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..691287a35f8d7d2dbb85423f4e536aeb7a9f6fae
--- /dev/null
+++ b/frontend/src/components/Login.jsx
@@ -0,0 +1,97 @@
+import React, { useState } from 'react';
+import NavBar from './NavBar';
+import { useAuth } from './AuthContext';
+import { useNavigate } from 'react-router-dom';
+
+const Login = () => {
+ const [success, setSuccess] = useState(false);
+ const [alert, setAlert] = useState("");
+ const { isLoggedIn, checkLoginStatus } = useAuth();
+ const navigate = useNavigate();
+
+ const handleLogin = async (e) => {
+ e.preventDefault();
+ const formData = new FormData(e.target);
+ const username = formData.get('username');
+ const password = formData.get('password');
+
+ if (username.length === 0) {
+ setAlert('Username cannot be empty');
+ }
+ else if (password.length === 0) {
+ setAlert('Password cannot be empty');
+ }
+ else {
+ try {
+ const response = await fetch('/api/login', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify({ username, password })
+ });
+ const data = await response.json();
+ if (data.status === 'success') {
+ setSuccess(true);
+ setAlert('');
+ checkLoginStatus();
+ navigate('/');
+ }
+ }
+ catch(error) {
+ setAlert('Error: ' + error.message);
+ }
+ }
+ }
+
+ if (isLoggedIn) {
+ navigate('/');
+ return null;
+ }
+
+ return (
+ <div>
+ <NavBar />
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8 flex flex-col justify-center items-center h-screen">
+ <h2 className="text-2xl font-bold mb-4">Sign in to your account</h2>
+ <div className="card w-96 bg-base-100 shadow-xl">
+ <div className="card-body">
+ <form onSubmit={handleLogin} className="space-y-4">
+ <div className="form-control">
+ <label className="label" htmlFor="username">Username</label>
+ <input type="text" name="username" className="input input-bordered" />
+ <label className="label" htmlFor="password">Password</label>
+ <input type="password" name="password" className="input input-bordered" />
+ </div>
+ {alert &&
+ <div role="alert" className="alert alert-error">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M10 14l2-2m0 0l2-2m-2 2l-2-2m2 2l2 2m7-2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>{alert}</span>
+ </div>
+ }
+ <div className="card-actions justify-center mt-4">
+ <button type="submit" className="btn btn-secondary w-full">Sign in</button>
+ </div>
+ </form>
+ </div>
+ </div>
+ {success && <p className="text-center mt-4">Login successful! Back to <a href="/" className="text-secondary font-bold">Home</a></p>}
+ <p className="text-center mt-4">Don't have an account? <a href="/register" className="text-secondary font-bold">Register</a></p>
+ </div>
+ </div>
+ </div>
+ )
+}
+
+export default Login;
\ No newline at end of file
diff --git a/frontend/src/components/MegaDetector.jsx b/frontend/src/components/MegaDetector.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..cf181567492bf92a4ea325a355bc8bd60e7ea7f1
--- /dev/null
+++ b/frontend/src/components/MegaDetector.jsx
@@ -0,0 +1,159 @@
+import React, { useState, useContext } from 'react';
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+const MegaDetector = () => {
+ const { theme, setTheme } = useContext(ThemeContext);
+ const [model, setModel] = useState('MDV5A');
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+ const handleOpenFileBrowser = (type) => {
+ let allowedTypes;
+ switch (type) {
+ case 'image':
+ allowedTypes = ['image'];
+ break;
+ case 'video':
+ allowedTypes = ['video'];
+ break;
+ case 'folder':
+ allowedTypes = ['folder'];
+ break;
+ default:
+ allowedTypes = ['image', 'video', 'folder'];
+ }
+ console.log('Setting allowed types:', allowedTypes); // Add this log
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = () => {
+ const formData = new FormData();
+
+ // Append each file to the FormData object
+ selectedItems.forEach(item => {
+ formData.append('image', item.path);
+ });
+
+ fetch('/api/megadetector/upload', {
+ method: 'POST',
+ body: formData
+ })
+ .then(response => {
+ if (!response.ok) {
+ throw new Error('Network response was not ok');
+ }
+ setUploadSuccess(true);
+ return response.text();
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ });
+ }
+
+ const handleSubmit = (e) => {
+ e.preventDefault();
+ const formData = new FormData(e.target);
+
+ fetch('/api/megadetector/run/batch', {
+ method: 'POST',
+ body: formData
+ })
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ });
+ };
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">MegaDetector</h1>
+ <p className="text-lg mb-8 text-center text-gray-700">
+ MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images).
+ </p>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4">Upload Images</h2>
+ <button onClick={() => handleOpenFileBrowser('image')} className="btn btn-primary mr-2">Select Images</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Images:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ <br />
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Confirm Upload</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+ </div>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6">
+ <h2 className="text-2xl font-semibold mb-4">Run MegaDetector</h2>
+ <form onSubmit={handleSubmit} className="space-y-4">
+ <div className="flex flex-col">
+ <label htmlFor="model" className="block text-sm font-medium text-gray-700 mb-2">
+ Select a model:
+ </label>
+ <div className="dropdown dropdown-bottom">
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {model ? model : 'Select model'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content menu z-[1] p-2 shadow rounded-box bg-white w-full max-w-xs">
+ <li><a onClick={() => setModel('MDV5A')}>MDv5a</a></li>
+ <li><a onClick={() => setModel('MDV5B')}>MDv5b</a></li>
+ <li><a onClick={() => setModel('MDV4')}>MDv4</a></li>
+ </ul>
+ </div>
+ </div>
+ {model === 'MDV4' && (
+ <div role="alert" className="alert alert-warning">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M12 9v2m0 4h.01m-6.938 4h13.856c1.54 0 2.502-1.667 1.732-3L13.732 4c-.77-1.333-2.694-1.333-3.464 0L3.34 16c-.77 1.333.192 3 1.732 3z"
+ />
+ </svg>
+ <span>Warning: MDv4 is not recommended, please only select this if you have a really good reason to do so.</span>
+ </div>
+ )}
+ <button type="submit" className="btn btn-primary">Run Batch</button>
+ </form>
+ </div>
+
+ <div className="mt-8 text-center">
+ <button className="btn btn-secondary" onClick={() => window.open('/megadetector/report', '_blank')}>
+ View Report
+ </button>
+ </div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ );
+};
+
+export default MegaDetector;
\ No newline at end of file
diff --git a/frontend/src/components/MegaDetectorReport.css b/frontend/src/components/MegaDetectorReport.css
new file mode 100644
index 0000000000000000000000000000000000000000..de98afa49d9288d21a533b1c146ffb6945a54594
--- /dev/null
+++ b/frontend/src/components/MegaDetectorReport.css
@@ -0,0 +1,19 @@
+.report-container img {
+ @apply max-w-full h-auto rounded-lg shadow-md;
+}
+
+.report-container a {
+ @apply link link-primary hover:link-secondary;
+}
+
+.report-container h2 {
+ @apply text-2xl font-semibold mb-4;
+}
+
+.report-container h3 {
+ @apply text-xl font-semibold mb-4;
+}
+
+.report-container p {
+ @apply text-lg mb-4;
+}
\ No newline at end of file
diff --git a/frontend/src/components/MegaDetectorReport.jsx b/frontend/src/components/MegaDetectorReport.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..06ecde5805e67dd55598c903f0bc9997569c380a
--- /dev/null
+++ b/frontend/src/components/MegaDetectorReport.jsx
@@ -0,0 +1,69 @@
+import React, { useState, useEffect, useContext } from 'react';
+import './MegaDetectorReport.css';
+import { ThemeContext } from '../ThemeContext';
+import NavBar from './NavBar';
+
+const MegaDetectorReport = () => {
+ const [htmlContent, setHtmlContent] = useState('');
+ const { theme, setTheme } = useContext(ThemeContext);
+
+ useEffect(() => {
+ fetch('/api/megadetector/report')
+ .then(response => response.json())
+ .then(data => {setHtmlContent(data.html_content);
+ })
+ .catch(error => console.error('Error fetching HTML content:', error));
+ }, []);
+
+ useEffect(() => {
+ if (htmlContent) {
+ const container = document.querySelector('.report-container');
+ const headers = container.querySelectorAll('h1, h2, h3');
+ headers.forEach(header => {
+ header.classList.add('text-primary', 'font-bold');
+ });
+ }
+ }, [htmlContent]);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto p-4 py-8">
+ <h2 className="text-4xl font-bold mb-4 text-center text-primary">MegaDetector Report</h2>
+ <div className="report-container bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <div dangerouslySetInnerHTML={{ __html: htmlContent }} />
+ </div>
+ </div>
+ </div>
+ );
+};
+
+const MegaDetectorDetectionsAnimalReport = () => {
+ const [htmlContent, setHtmlContent] = useState('');
+ const { theme, setTheme } = useContext(ThemeContext);
+
+ useEffect(() => {
+ fetch('/api/megadetector/detections_animal.html')
+ .then(response => response.json())
+ .then(data => {setHtmlContent(data.html_content);
+ })
+ .catch(error => console.error('Error fetching HTML content:', error));
+ }, []);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto p-4 py-8">
+ <h2 className="text-4xl font-bold mb-4 text-center text-primary">MegaDetector Report</h2>
+ <div className="report-container bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <div dangerouslySetInnerHTML={{ __html: htmlContent }} />
+ </div>
+ <div className="mt-8 text-center">
+ <button className="btn btn-secondary" onClick={() => window.history.back()}>Back</button>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export { MegaDetectorReport, MegaDetectorDetectionsAnimalReport };
\ No newline at end of file
diff --git a/frontend/src/components/NavBar.jsx b/frontend/src/components/NavBar.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..2b9b6b7325003fe52ea5529c4d9d3027a314cbb7
--- /dev/null
+++ b/frontend/src/components/NavBar.jsx
@@ -0,0 +1,84 @@
+import React, { useContext } from 'react';
+import { ThemeContext } from '../ThemeContext';
+import { useAuth } from './AuthContext';
+
+function NavBar() {
+ const { theme, setTheme } = useContext(ThemeContext);
+ const { isLoggedIn, currentUser, checkLoginStatus } = useAuth();
+
+ const toogleTheme = () => {
+ const newTheme = theme === 'nature' ? 'night' : 'nature';
+ setTheme(newTheme);
+ };
+
+ const handleLogout = async () => {
+ try {
+ const response = await fetch('/api/logout', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ });
+ await checkLoginStatus();
+ } catch (error) {
+ console.error('Error during logout:', error);
+ }
+ };
+
+ return (
+ <div className='navbar bg-base'>
+ <div className='flex-1'>
+ <a className='btn btn-ghost text-2xl text-primary' onClick={() => window.location.href = '/'}>CamTrap Hub</a>
+ </div>
+ <div className='flex-none'>
+ <label className="swap swap-rotate">
+ {/* this hidden checkbox controls the state */}
+ <input type="checkbox" className="theme-controller" value="synthwave" onChange={toogleTheme} checked={theme === 'night'}/>
+
+ {/* sun icon */}
+ <svg
+ className="swap-off h-8 w-8 fill-current"
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 24 24">
+ <path
+ d="M5.64,17l-.71.71a1,1,0,0,0,0,1.41,1,1,0,0,0,1.41,0l.71-.71A1,1,0,0,0,5.64,17ZM5,12a1,1,0,0,0-1-1H3a1,1,0,0,0,0,2H4A1,1,0,0,0,5,12Zm7-7a1,1,0,0,0,1-1V3a1,1,0,0,0-2,0V4A1,1,0,0,0,12,5ZM5.64,7.05a1,1,0,0,0,.7.29,1,1,0,0,0,.71-.29,1,1,0,0,0,0-1.41l-.71-.71A1,1,0,0,0,4.93,6.34Zm12,.29a1,1,0,0,0,.7-.29l.71-.71a1,1,0,1,0-1.41-1.41L17,5.64a1,1,0,0,0,0,1.41A1,1,0,0,0,17.66,7.34ZM21,11H20a1,1,0,0,0,0,2h1a1,1,0,0,0,0-2Zm-9,8a1,1,0,0,0-1,1v1a1,1,0,0,0,2,0V20A1,1,0,0,0,12,19ZM18.36,17A1,1,0,0,0,17,18.36l.71.71a1,1,0,0,0,1.41,0,1,1,0,0,0,0-1.41ZM12,6.5A5.5,5.5,0,1,0,17.5,12,5.51,5.51,0,0,0,12,6.5Zm0,9A3.5,3.5,0,1,1,15.5,12,3.5,3.5,0,0,1,12,15.5Z" />
+ </svg>
+
+ {/* moon icon */}
+ <svg
+ className="swap-on h-8 w-8 fill-current"
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 24 24">
+ <path
+ d="M21.64,13a1,1,0,0,0-1.05-.14,8.05,8.05,0,0,1-3.37.73A8.15,8.15,0,0,1,9.08,5.49a8.59,8.59,0,0,1,.25-2A1,1,0,0,0,8,2.36,10.14,10.14,0,1,0,22,14.05,1,1,0,0,0,21.64,13Zm-9.5,6.69A8.14,8.14,0,0,1,7.08,5.22v.27A10.15,10.15,0,0,0,17.22,15.63a9.79,9.79,0,0,0,2.1-.22A8.11,8.11,0,0,1,12.14,19.73Z" />
+ </svg>
+ </label>
+ <ul className="menu menu-horizontal px-1 text-primary font-bold">
+ <li><a onClick={() => window.location.href = '/'}>Home</a></li>
+ <li><a onClick={() => window.location.href = '/about'}>About</a></li>
+ <li><a>Contact</a></li>
+ {isLoggedIn ? (
+ <div className="dropdown dropdown-end">
+ <div tabIndex={0} role="button" className="btn btn-ghost btn-circle avatar placeholder">
+ <div className="bg-neutral text-neutral-content w-10 rounded-full">
+ <span>{currentUser.username.charAt(0).toUpperCase()}</span>
+ </div>
+ </div>
+ <ul
+ tabIndex={0}
+ className="menu menu-sm dropdown-content bg-base-100 rounded-box z-[1] mt-3 w-52 p-2 shadow">
+ <li><a onClick={() => window.location.href = '/profile'}>Profile</a></li>
+ <li><a onClick={() => window.location.href = '/datamanagement'}>Data management</a></li>
+ <li><a onClick={handleLogout}>Logout</a></li>
+ </ul>
+ </div>
+ ) : (
+ <li><a onClick={() => window.location.href = '/login'}>Login</a></li>
+ )}
+ </ul>
+ </div>
+ </div>
+ );
+}
+
+export default NavBar;
\ No newline at end of file
diff --git a/frontend/src/components/Preview.jsx b/frontend/src/components/Preview.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..4839b3b6382b5d4a194d47802cd9298c9b34247a
--- /dev/null
+++ b/frontend/src/components/Preview.jsx
@@ -0,0 +1,235 @@
+import React, {useState} from 'react';
+
+function Preview({ appInfo, onClose }) {
+ const [logoError, setLogoError] = useState(false);
+
+ const handleLogoError = () => {
+ setLogoError(true);
+ };
+
+ const onClickAniml = () => {
+ fetch('/api/animl/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ window.open(data.url, '_blank');
+ } else {
+ console.error('Failed to start Animl:', data.error);
+ }
+ })
+ .catch(error => console.error('Error:', error));
+ };
+
+ const onClickTrapper = () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ fetch('/api/trapper/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ window.open(data.url, '_blank');
+ } else {
+ console.error('Failed to start Trapper:', data.error);
+ loadingElement.style.display = 'none';
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ });
+ };
+
+ const onClickEcoSecrets = () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ fetch('/api/ecosecrets/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ console.log('opening', data.url);
+ window.open(data.url, '_blank');
+ } else {
+ console.error('Failed to start EcoSecrets:', data.error);
+ loadingElement.style.display = 'none';
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ });
+ };
+
+ const onClickIl2bb = async () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ try {
+ const response = await fetch('/api/il2bb/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ const data = await response.json();
+ if (data.status ==='running') {
+ console.log('opening IL2BB URL');
+ window.location.href = appInfo.link;
+ } else {
+ console.error('Failed to start IL2BB:', data.error);
+ loadingElement.style.display = 'none';
+ }
+ }
+ catch (error) {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ }
+ }
+
+ const onClickCameraTrapTools = () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ fetch('/api/camera-trap-tools/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ window.location.href = '/camera-trap-tools';
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ });
+ }
+
+ const onClickWildCoFaceBlur = () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ fetch('/api/wildCoFaceBlur/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ window.location.href = '/wildCoFaceBlur';
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ });
+ }
+
+ const onClickCameraTrapWorkflow = () => {
+ const loadingElement = document.getElementById('loading');
+ loadingElement.style.display = 'block';
+
+ fetch('/api/camera-trap-workflow/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ }
+ })
+ .then(response => response.json())
+ .then(data => {
+ if (data.status === 'running') {
+ window.location.href = '/camera-trap-workflow';
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ loadingElement.style.display = 'none';
+ });
+ }
+
+ return (
+ <div className="fixed inset-0 bg-black bg-opacity-50 flex items-center justify-center z-50">
+ <div className="bg-white p-8 rounded-lg max-w-2xl w-full">
+ <div className="flex flex-row items-center">
+ {!logoError ? (
+ <img src={appInfo.logo} alt={appInfo.name} className="w-32 h-32 object-cover mr-4" onError={handleLogoError}/>
+ ) : (
+ <div className="avatar placeholder mr-4">
+ <div className="bg-accent text-neutral-content w-24 rounded">
+ <span className="text-2xl text-primary text-center">{appInfo.name}</span>
+ </div>
+ </div>
+ )}
+ <div className="flex flex-col">
+ <h2 className="text-2xl font-bold mb-4 text-primary">{appInfo.name}</h2>
+ <div className="rating mb-2">
+ <input type="radio" name="rating-1" className="mask mask-star bg-secondary" />
+ <input type="radio" name="rating-1" className="mask mask-star bg-secondary" />
+ <input type="radio" name="rating-1" className="mask mask-star bg-secondary" />
+ <input type="radio" name="rating-1" className="mask mask-star bg-secondary" defaultChecked />
+ <input type="radio" name="rating-1" className="mask mask-star bg-secondary" />
+ </div>
+ <div className="mb-4">
+ {appInfo.tags.map((tag, index) => (
+ <span key={index} className="badge badge-outline mr-2">{tag}</span>
+ ))}
+ </div>
+ </div>
+ </div>
+ <div className="divider"></div>
+ <h3 className="text-xl text-primary font-bold mb-4">Description</h3>
+ <div className="mb-4">
+ {appInfo.full_description.split('\n').map((line, index) => {
+ return <p key={index}>{line}</p>
+ })}
+ </div>
+ <div className="flex justify-between">
+ <button className="btn btn-primary" onClick={() => {
+ if (appInfo.name === 'Animl') {
+ onClickAniml();
+ } else if (appInfo.name === 'Trapper') {
+ onClickTrapper();
+ } else if (appInfo.name === 'EcoSecrets') {
+ onClickEcoSecrets();
+ } else if (appInfo.name === 'IL2BB') {
+ onClickIl2bb();
+ } else if (appInfo.name === 'Camera Trap Tools') {
+ onClickCameraTrapTools();
+ } else if (appInfo.name === 'WildCo-FaceBlur') {
+ onClickWildCoFaceBlur();
+ } else if (appInfo.name === 'Camera Trap Workflow') {
+ onClickCameraTrapWorkflow();
+ } else {
+ window.location.href = appInfo.link;
+ }
+ }}>Go to {appInfo.name}</button>
+ <span id="loading" style={{ display: 'none' }} className="loading loading-spinner loading-xs"></span>
+ <button className="btn btn-secondary" onClick={onClose}>Close</button>
+ </div>
+ </div>
+ </div>
+ );
+}
+
+export default Preview;
\ No newline at end of file
diff --git a/frontend/src/components/PrivateRoute.jsx b/frontend/src/components/PrivateRoute.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..a2233d7cc75862c8bd67ad496a340802ef2b21d0
--- /dev/null
+++ b/frontend/src/components/PrivateRoute.jsx
@@ -0,0 +1,16 @@
+import React from 'react';
+import { Navigate, Outlet } from 'react-router-dom';
+import { useAuth } from './AuthContext';
+
+function PrivateRoute() {
+ const { isLoggedIn } = useAuth();
+
+ if (isLoggedIn === null) {
+ // Still checking login status
+ return <div>Loading...</div>;
+ }
+
+ return isLoggedIn ? <Outlet /> : <Navigate to="/login" replace />;
+}
+
+export default PrivateRoute;
\ No newline at end of file
diff --git a/frontend/src/components/Profile.jsx b/frontend/src/components/Profile.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..5db69c81b97c164b7a17ff4e6d43dae2eac66e8a
--- /dev/null
+++ b/frontend/src/components/Profile.jsx
@@ -0,0 +1,122 @@
+import React, { useContext, useState, useEffect } from 'react';
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+import { useAuth } from './AuthContext';
+
+const Profile = () => {
+ const { theme, setTheme } = useContext(ThemeContext);
+ const { currentUser, checkLoginStatus } = useAuth();
+ const [profileData, setProfileData] = useState({
+ username: '',
+ email: '',
+ organisation: '',
+ title: ''
+ });
+
+ useEffect(() => {
+ if (currentUser) {
+ setProfileData({
+ username: currentUser.username || '',
+ email: currentUser.email || '',
+ organisation: currentUser.organisation || '',
+ title: currentUser.title || ''
+ });
+ }
+ }, [currentUser]);
+
+ const handleInputChange = (e) => {
+ const { name, value } = e.target;
+ setProfileData(prevData => ({
+ ...prevData,
+ [name]: value
+ }));
+ };
+
+ const handleSubmit = async (e) => {
+ e.preventDefault();
+ console.log('Profile data to update:', profileData);
+ try {
+ const response = await fetch('/api/profile/update', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify(profileData),
+ credentials: 'include'
+ });
+ const data = await response.json();
+ console.log('Profile data to update:', data);
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ // After updating, you might want to refresh the user data
+ await checkLoginStatus();
+ };
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-bold mb-6 text-primary">Profile Settings</h2>
+ <form onSubmit={handleSubmit}>
+ <div className="grid grid-cols-1 md:grid-cols-2 gap-6 mb-6">
+ <label className="form-control w-full max-w-xs">
+ <div className="label">
+ <span className="label-text">Name</span>
+ </div>
+ <input
+ type="text"
+ name="username"
+ value={profileData.username}
+ onChange={handleInputChange}
+ className="input input-bordered w-full max-w-xs"
+ />
+ </label>
+ <label className="form-control w-full max-w-xs">
+ <div className="label">
+ <span className="label-text">Email</span>
+ </div>
+ <input
+ type="email"
+ name="email"
+ value={profileData.email}
+ onChange={handleInputChange}
+ className="input input-bordered w-full max-w-xs"
+ />
+ </label>
+ <label className="form-control w-full max-w-xs">
+ <div className="label">
+ <span className="label-text">Organisation</span>
+ </div>
+ <input
+ type="text"
+ name="organisation"
+ value={profileData.organisation}
+ onChange={handleInputChange}
+ className="input input-bordered w-full max-w-xs"
+ />
+ </label>
+ <label className="form-control w-full max-w-xs">
+ <div className="label">
+ <span className="label-text">Title</span>
+ </div>
+ <input
+ type="text"
+ name="title"
+ value={profileData.title}
+ onChange={handleInputChange}
+ className="input input-bordered w-full max-w-xs"
+ />
+ </label>
+ </div>
+ <button type="submit" className="btn btn-primary">Update</button>
+ </form>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default Profile;
\ No newline at end of file
diff --git a/frontend/src/components/Register.jsx b/frontend/src/components/Register.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..61aa32d1996c33ae067873c22e2b5fa7b1bf3464
--- /dev/null
+++ b/frontend/src/components/Register.jsx
@@ -0,0 +1,60 @@
+import React, { useState } from 'react';
+import NavBar from './NavBar';
+
+const Register = () => {
+ const [success, setSuccess] = useState(false);
+
+ const handleRegister = async (e) => {
+ e.preventDefault();
+ const formData = new FormData(e.target);
+ try {
+ const response = await fetch('/api/register', {
+ method: 'POST',
+ body: formData
+ })
+ const data = await response.json();
+ console.log(data);
+ if (data.status === 'success') {
+ setSuccess(true);
+ }
+ window.location.href = '/login';
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ }
+ }
+
+ return (
+ <div>
+ <NavBar />
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8 flex flex-col justify-center items-center h-screen">
+ <h2 className="text-2xl font-bold mb-4">Sign up for an account</h2>
+ <div className="card w-96 bg-base-100 shadow-xl">
+ <div className="card-body">
+ <form onSubmit={handleRegister} className="space-y-4">
+ <div className="form-control">
+ <label className="label">Email</label>
+ <input type="text" name="email" className="input input-bordered" />
+ <label className="label">Organisation/Company</label>
+ <input type="text" name="organisation" className="input input-bordered" />
+ <label className="label">Username</label>
+ <input type="text" name="username" className="input input-bordered" />
+ <label className="label">Password</label>
+ <input type="password" name="password" className="input input-bordered" />
+ </div>
+ <div className="card-actions justify-center mt-4">
+ <button type="submit" className="btn btn-secondary w-full">Register</button>
+ </div>
+ </form>
+ </div>
+ </div>
+ {success && <p className="text-center mt-4">Account created successfully! Back to <a href="/login" className="text-secondary font-bold">Login</a></p>}
+ <p className="text-center mt-4">Already have an account? <a href="/login" className="text-secondary font-bold">Login here</a></p>
+ </div>
+ </div>
+ </div>
+ )
+}
+
+export default Register;
\ No newline at end of file
diff --git a/frontend/src/components/WildcoFaceBlur.jsx b/frontend/src/components/WildcoFaceBlur.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..616db2787d089659e324df6651f81f3b6f007a33
--- /dev/null
+++ b/frontend/src/components/WildcoFaceBlur.jsx
@@ -0,0 +1,269 @@
+import React, { useState } from 'react';
+import NavBar from './NavBar';
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+const WildcoFaceBlur = () => {
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [megadetectorFile, setMegadetectorFile] = useState(null);
+ const [processing, setProcessing] = useState(false);
+ const [success, setSuccess] = useState(false);
+ const [dateFolders, setDateFolders] = useState('false');
+ const [blurLevel, setBlurLevel] = useState(7);
+ const [confThreshold, setConfThreshold] = useState(0.25);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+ const handleUpload = async () => {
+ const formData = new FormData();
+
+ if (megadetectorFile) {
+ formData.append('megadetector-file', megadetectorFile);
+ }
+
+ selectedItems.forEach(item => {
+ if (item.type === 'folder') {
+ formData.append('folders', item.path);
+ } else {
+ formData.append('files', item.path);
+ }
+ });
+
+ try {
+ const response = await fetch('/api/wildCoFaceBlur/upload', {
+ method: 'POST',
+ body: formData
+ });
+ const data = await response.json();
+ console.log('Upload response: ', data);
+ setUploadSuccess(true);
+ } catch (e) {
+ console.error('Error: ', e);
+ }
+ };
+
+ const handleProcessImages = async () => {
+ try {
+ const response = await fetch('/api/wildCoFaceBlur/trigger_script', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify({
+ dateFolders: dateFolders,
+ blurLevel: blurLevel,
+ confThreshold: confThreshold
+ })
+ })
+ setProcessing(true);
+ const data = await response.json();
+ console.log('Response: ', data);
+ setProcessing(false);
+ setSuccess(true);
+ }
+ catch(error) {
+ console.error('Error: ', error);
+ };
+ }
+
+ const handleDownload = async () => {
+ try {
+ const response = await fetch('/api/wildCoFaceBlur/download', {
+ method: 'GET'
+ });
+ if (response.ok) {
+ const blob = await response.blob();
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'WildCoFaceBlur_output.zip'
+ a.click();
+ window.URL.revokeObjectURL(url);
+ } else {
+ console.error('Download failed');
+ }
+ }
+ catch (error) {
+ console.error('Error: ', error);
+ }
+ };
+
+ const handleOpenFileBrowser = (type) => {
+ let allowedTypes;
+ switch (type) {
+ case 'image':
+ allowedTypes = ['image'];
+ break;
+ case 'video':
+ allowedTypes = ['video'];
+ break;
+ case 'folder':
+ allowedTypes = ['folder'];
+ break;
+ default:
+ allowedTypes = ['image', 'video', 'folder'];
+ }
+ console.log('Setting allowed types:', allowedTypes); // Add this log
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ console.log('Selected items:', selectedItems);
+ };
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar />
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">WildCo-FaceBlur</h1>
+ <p className="text-lg mb-8 text-center text-gray-700">Obscures human identities in camera trap images after processing via <a href="https://github.com/microsoft/CameraTraps/blob/master/megadetector.md" target="_blank">Microsoft MegaDetector</a>.</p>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4">Upload Data</h2>
+ <div>
+ <h3 className="text-lg font-semibold mb-2">MegaDetector output (a .json file)</h3>
+ <p className="text-sm text-gray-700">Check <a className="link" href="https://github.com/microsoft/CameraTraps/blob/master/megadetector.md">here</a> for more details about MegaDetector usage.</p>
+ <label htmlFor="megadetector-file" className="block text-sm font-medium text-gray-700 mt-2 mb-2">
+ MegaDetector output (a .json file)
+ </label>
+ <input
+ type="file"
+ name="megadetector-file"
+ accept=".json"
+ className="file-input file-input-bordered file-input-primary bg-white w-full max-w-xs mb-2"
+ onChange={(e) => setMegadetectorFile(e.target.files[0])}
+ />
+ {megadetectorFile && (
+ <div className="mt-4">
+ <p className="font-semibold">Uploaded MegaDetector output file:</p>
+ <ul>
+ <li>{megadetectorFile.name}</li>
+ </ul>
+ </div>
+ )}
+ </div>
+ <div>
+ <h3 className="text-lg font-semibold mt-4 mb-2">Folder of images:</h3>
+ <p className="text-sm text-gray-700">*Note: Within your imgaes folder, create individaul sub-folders for each of your camera trap sites/deployments.</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>You may optionally include sub-folders for date specific camera checks (e.g. 2021-09-01 to 2021-12-31).</li>
+ <li>Within these site (or check) specific folders, paste the MegaDetector .json file and camera trap photos corresponding to the site.</li>
+ <li>If you wish to upload different images, please go to <a href="/datamanagement" className="link">Data Space</a> and upload your images there.</li>
+ </ul>
+ {/* <button onClick={() => handleOpenFileBrowser('image')} className="btn btn-primary mr-2">Select Images</button>
+ <button onClick={() => handleOpenFileBrowser('video')} className="btn btn-primary mr-2">Select Videos</button> */}
+ <button onClick={() => handleOpenFileBrowser('folder')} className="btn btn-primary mt-2">Select Folder</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Items:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ </div>
+
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Upload Selected Items</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+
+ <h2 className="text-2xl font-semibold mt-4 mb-4">Configuration</h2>
+ <div className="space-y-4 flex flex-col">
+ <div className="dropdown dropdown-bottom">
+ <label htmlFor="media-type" className="block text-sm font-medium text-gray-700 mb-2">
+ Date folders (depending on your uploaded folder structure):
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {dateFolders ? dateFolders : 'Date folders'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setDateFolders('false')}>False</button></li>
+ <li><button onClick={() => setDateFolders('true')}>True</button></li>
+ </ul>
+ </div>
+ <div>
+ <label htmlFor="blur-level" className="block text-sm font-medium text-gray-700 mb-2">
+ Blur level (Lower values are more obscured; recommended: between 3 and 7):
+ </label>
+ <input
+ type="number"
+ id="blur-level"
+ placeholder="Blur level (number)"
+ className="input w-full max-w-xs"
+ defaultValue="7"
+ onChange={(e) => setBlurLevel(e.target.value)}
+ />
+ </div>
+ <div>
+ <label htmlFor="conf" className="block text-sm font-medium text-gray-700 mb-2">
+ The MegaDetector confidence threshold:
+ </label>
+ <input
+ type="number"
+ id="conf"
+ placeholder="Confidence threshold (number)"
+ className="input w-full max-w-xs"
+ defaultValue="0.25"
+ onChange={(e) => setConfThreshold(e.target.value)}
+ />
+ </div>
+ </div>
+ </div>
+
+ <button className='btn btn-primary' onClick={handleProcessImages}>Process images</button>
+ {processing && (
+ <div role="alert" className="alert alert-info mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ fill="none"
+ viewBox="0 0 24 24"
+ className="h-6 w-6 shrink-0 stroke-current">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M13 16h-1v-4h-1m1-4h.01M21 12a9 9 0 11-18 0 9 9 0 0118 0z"></path>
+ </svg>
+ <span>You've successfully triggered the processing. It may take a while to finish.</span>
+ </div>
+ )}
+
+ {success && (
+ <div role="alert" className="alert alert-success mt-4">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ className="h-6 w-6 shrink-0 stroke-current"
+ fill="none"
+ viewBox="0 0 24 24">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M9 12l2 2 4-4m6 2a9 9 0 11-18 0 9 9 0 0118 0z" />
+ </svg>
+ <span>Processing completed! You may download the result now.</span>
+ </div>
+ )}
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mt-8 mb-8">
+ <h2 className="text-2xl font-semibold mb-4">Download results</h2>
+ <p className="text-sm text-gray-700">Once the pipeline has finished processing, you can download the results here.</p>
+ <p className="text-sm text-gray-700">Inside the the .zip file, you will find:</p>
+ <ul className="list-disc list-inside text-sm text-gray-700">
+ <li>copies of the images that have been processed (blured)</li>
+ </ul>
+ <button className='btn btn-primary mt-4' onClick={handleDownload}>Download results</button>
+ </div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ );
+};
+
+export default WildcoFaceBlur;
\ No newline at end of file
diff --git a/frontend/src/components/Zamba.jsx b/frontend/src/components/Zamba.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..6f7ccca918a7889e030404ce8d8b57d0ddec7a49
--- /dev/null
+++ b/frontend/src/components/Zamba.jsx
@@ -0,0 +1,29 @@
+import React, { useState, useContext } from 'react';
+import NavBar from './NavBar';
+import ZambaProcess from './ZambaProcess';
+import ZambaTrain from './ZambaTrain';
+import { ThemeContext } from '../ThemeContext';
+
+const Zamba = () => {
+ const [activeTab, setActiveTab] = useState('classification');
+ const { theme, setTheme } = useContext(ThemeContext);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Zamba</h1>
+ <div role="tablist" className="tabs tabs-boxed">
+ <a role="tab" className={`tab ${activeTab === 'classification' ? 'tab-active' : ''}`} onClick={() => setActiveTab('classification')}>Classification</a>
+ <a role="tab" className={`tab ${activeTab === 'train' ? 'tab-active' : ''}`} onClick={() => setActiveTab('train')}>Train a model</a>
+ </div>
+ </div>
+
+ {activeTab === 'classification' && <ZambaProcess />}
+ {activeTab === 'train' && <ZambaTrain />}
+
+ </div>
+ )
+}
+
+export default Zamba;
\ No newline at end of file
diff --git a/frontend/src/components/ZambaProcess.jsx b/frontend/src/components/ZambaProcess.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..0662ae19cc66c429dd1a1d830dbc4356e4c687e6
--- /dev/null
+++ b/frontend/src/components/ZambaProcess.jsx
@@ -0,0 +1,203 @@
+import React, { useState, useEffect } from 'react'
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+function ZambaProcess() {
+ const [taskId, setTaskId] = useState(null);
+ const [taskStatus, setTaskStatus] = useState(null);
+ const [mediaType, setMediaType] = useState('video');
+ const [dryRun, setDryRun] = useState('false');
+ const [outputClassname, setOutputClassname] = useState('true');
+ const [selectedModel, setSelectedModel] = useState('time_distributed');
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+
+ const handleOpenFileBrowser = () => {
+ let allowedTypes = ['image', 'video'];
+ console.log('Setting allowed types:', allowedTypes); // Add this log
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = () => {
+ const formData = new FormData();
+
+ selectedItems.forEach(item => {
+ formData.append('file', item.path);
+ });
+
+ fetch('/api/zamba/upload', {
+ method: 'POST',
+ body: formData,
+ })
+ .then(response => {
+ if (!response.ok) {
+ throw new Error('Network response was not ok');
+ }
+ setUploadSuccess(true);
+ return response.json();
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ });
+ }
+
+ const triggerProcess = () => {
+ fetch('/api/zamba/process', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify({ type: mediaType, model: selectedModel, dryRun, outputClassname }),
+ })
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ setTaskId(data.task_id);
+ setTaskStatus('Started');
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ setTaskStatus('Error: ' + error.message);
+ });
+ }
+
+ useEffect(() => {
+ let interval;
+ if (taskId) {
+ interval = setInterval(() => {
+ fetch(`/api/task_status/${taskId}`)
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ setTaskStatus(data.status);
+ if (data.status === 'SUCCESS') {
+ clearInterval(interval);
+ }
+ });
+ }, 10000);
+ }
+ return () => clearInterval(interval);
+ }, [taskId]);
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Classifying unlabeled videos</h1>
+ <p className="text-lg mb-8 text-center text-primary">
+ For more detailed information, check the <a href="https://zamba.drivendata.org/docs/stable/predict-tutorial/" className="link">User Tutorial</a>.
+ </p>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4 text-gray-700">Upload videos/images for processing</h2>
+ <p className="mb-4">Only upload one type (video/image) at a time.</p>
+ <button onClick={() => handleOpenFileBrowser()} className="btn btn-primary mr-2">Select Images/Videos</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Images/Videos:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ <br />
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Confirm Upload</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+ </div>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6">
+ <h2 className="text-2xl font-semibold mb-4 text-gray-700">Configuration</h2>
+ <div className="space-y-4 flex flex-col">
+ <div className="dropdown dropdown-bottom">
+ <label htmlFor="media-type" className="block text-sm font-medium text-gray-700 mb-2">
+ Media type:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {mediaType ? mediaType : 'Media type'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setMediaType('video')}>Video</button></li>
+ <li><button onClick={() => setMediaType('image')}>Image</button></li>
+ </ul>
+ </div>
+
+ <div className="dropdown dropdown-bottom">
+ <label htmlFor="dry-run" className="block text-sm font-medium text-gray-700 mb-2">
+ Dry run:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {dryRun ? dryRun : 'Dry run'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setDryRun('false')}>False</button></li>
+ <li><button onClick={() => setDryRun('true')}>True</button></li>
+ </ul>
+ </div>
+
+ <div className="dropdown dropdown-bottom">
+ <label htmlFor="output-classname" className="block text-sm font-medium text-gray-700 mb-2">
+ Output class name only:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {outputClassname ? outputClassname : 'Output class name only'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setOutputClassname('false')}>False</button></li>
+ <li><button onClick={() => setOutputClassname('true')}>True</button></li>
+ </ul>
+ </div>
+
+ <div className="dropdown dropdown-bottom">
+ <label htmlFor="model-select" className="block text-sm font-medium text-gray-700 mb-2">
+ Choose a pretrained model:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {selectedModel ? selectedModel : 'Choose a pretrained model'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setSelectedModel('time_distributed')}>time_distributed (default)</button></li>
+ <li><button onClick={() => setSelectedModel('blank_nonblank')}>blank_nonblank</button></li>
+ <li><button onClick={() => setSelectedModel('slowfast')}>slowfast</button></li>
+ <li><button onClick={() => setSelectedModel('european')}>european</button></li>
+ </ul>
+ </div>
+
+ <p className="text-sm text-gray-700">
+ Please select a pretrained model that ships with the zamba package. See the <a href="https://zamba.drivendata.org/docs/stable/models/species-detection/" target="_blank" rel="noopener noreferrer" className="text-primary hover:underline">Available Models</a> page for more details.
+ </p>
+
+ <div>
+ <p className="mb-2">Select the Process file(s) button to confirm processing the uploaded files. This process may take a while.</p>
+ <button onClick={triggerProcess} className="btn btn-primary">Process file(s)</button>
+ </div>
+
+ {taskStatus && (
+ <div className="alert alert-info">
+ <span>Task Status: {taskStatus}</span>
+ </div>
+ )}
+ </div>
+ </div>
+ </div>
+ <div className="pb-8 text-center">
+ <button onClick={() => window.location.href='/zamba/result'} className="btn btn-secondary">
+ See results
+ </button>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ )
+}
+
+export default ZambaProcess;
\ No newline at end of file
diff --git a/frontend/src/components/ZambaResult.jsx b/frontend/src/components/ZambaResult.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..56e79e122a67212b4efc4be48453cb8bd5463dd8
--- /dev/null
+++ b/frontend/src/components/ZambaResult.jsx
@@ -0,0 +1,92 @@
+import React, { useState, useEffect, useContext } from 'react'
+import NavBar from './NavBar';
+import { ThemeContext } from '../ThemeContext';
+
+function ZambaResult() {
+ const { theme, setTheme } = useContext(ThemeContext);
+ const [results, setResults] = useState([]);
+ useEffect(() => {
+ getResult().then(data => {
+ setResults(data);
+ });
+ }, []);
+
+ const getResult = async () => {
+ const response = await fetch('/api/zamba/result');
+ const data = await response.json();
+ console.log(data);
+ return data;
+ }
+
+ const handleDownload = () => {
+ fetch('/api/zamba/download')
+ .then(response => {
+ if (response.ok) {
+ return response.blob();
+ }
+ throw new Error('Network response was not ok.');
+ })
+ .then(blob => {
+ const url = window.URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.style.display = 'none';
+ a.href = url;
+ a.download = 'zamba_predictions.csv';
+ document.body.appendChild(a);
+ a.click();
+ window.URL.revokeObjectURL(url);
+ })
+ .catch(error => console.error('Download error:', error));
+ }
+
+ return (
+ <div className='min-h-screen bg-base'>
+ <NavBar theme={theme} setTheme={setTheme}/>
+ <div className='container mx-auto px-4 py-8'>
+ <h1 className='text-4xl font-bold mb-4 text-center text-primary'>Zamba Classification Results</h1>
+ <p className='text-lg mb-8 text-center text-primary'>Only result of selected most likely class will be displayed here. You can still download the CSV file to see the full result.</p>
+ <h2 className='text-center text-primary'>Download the original CSV file <a href="#" className='link' onClick={handleDownload}>here</a></h2>
+
+ {results.length > 0 ? (
+ <table border="1" className='table table-zebra'>
+ <thead>
+ <tr>
+ <th>Timestamp</th>
+ <th>Filepath</th>
+ <th>Classname</th>
+ </tr>
+ </thead>
+ <tbody>
+ {results.map((item, index) => (
+ <tr key={index}>
+ <td>{new Date(item.timestamp).toLocaleString()}</td>
+ <td>{item.filepath}</td>
+ <td>{item.classname}</td>
+ </tr>
+ ))}
+ </tbody>
+ </table>
+ ) : (
+ <div role="alert" className="alert alert-info">
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ fill="none"
+ viewBox="0 0 24 24"
+ className="h-6 w-6 shrink-0 stroke-current">
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M13 16h-1v-4h-1m1-4h.01M21 12a9 9 0 11-18 0 9 9 0 0118 0z"></path>
+ </svg>
+ <span>No classification results available. Please run the Zamba process first.</span>
+ </div>
+ )}
+
+ <button className='btn btn-primary mt-4' onClick={() => window.location.href='/zamba'}>Back to Zamba</button>
+ </div>
+ </div>
+ )
+}
+
+export default ZambaResult;
\ No newline at end of file
diff --git a/frontend/src/components/ZambaTrain.jsx b/frontend/src/components/ZambaTrain.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..db4653bc26220c72e7a8772efbb1bba853d65b62
--- /dev/null
+++ b/frontend/src/components/ZambaTrain.jsx
@@ -0,0 +1,213 @@
+import React, { useState, useEffect } from 'react'
+import ImageBrowserPopup from './ImageBrowserPopup';
+
+function ZambaTrain() {
+ const [labelCsv, setLabelCsv] = useState(null);
+ const [trainStatus, setTrainStatus] = useState('');
+ const [taskStatus, setTaskStatus] = useState('');
+ const [taskId, setTaskId] = useState(null);
+ const [model, setModel] = useState('time_distributed');
+ const [dryRun, setDryRun] = useState('false');
+ const [selectedItems, setSelectedItems] = useState([]);
+ const [allowedTypes, setAllowedTypes] = useState([]);
+ const [uploadSuccess, setUploadSuccess] = useState(false);
+
+ useEffect(() => {
+ let interval;
+ if (taskId) {
+ interval = setInterval(() => {
+ fetch(`/api/task_status/${taskId}`)
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ const message = data.result ? data.result.message : 'Not available yet';
+ const output = data.result ? data.result.output : '';
+ setTaskStatus(`Task Status: ${data.status}\nMessage: ${message}\nOutput: ${output}`);
+
+ if (data.status === 'SUCCESS') {
+ clearInterval(interval);
+ }
+ })
+ .catch(error => {
+ console.error('Error fetching task status:', error);
+ setTaskStatus(`Error fetching task status: ${error.message}`);
+ });
+ }, 60000);
+ }
+ return () => clearInterval(interval);
+ }, [taskId]);
+
+ const handleOpenFileBrowser = () => {
+ let allowedTypes = ['video'];
+ console.log('Setting allowed types:', allowedTypes); // Add this log
+ setAllowedTypes(allowedTypes);
+ document.getElementById('file_browser_modal').showModal();
+ };
+
+ const handleSelectItems = (items) => {
+ setSelectedItems(items);
+ };
+
+ const handleUpload = () => {
+ const formData = new FormData();
+
+ if (labelCsv) {
+ formData.append('labelCsv', labelCsv);
+ }
+
+ selectedItems.forEach(item => {
+ formData.append('videos', item.path);
+ });
+
+ fetch('/api/zamba/train/upload', {
+ method: 'POST',
+ body: formData,
+ })
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ setUploadSuccess(true);
+ })
+ .catch(error => {
+ console.error('Error uploading files:', error);
+ });
+ }
+
+ function triggerTrain() {
+ const labels = labelCsv ? (labelCsv.filename || labelCsv) : '';
+
+ if (!labels) {
+ setTrainStatus('Error: No label file specified');
+ return;
+ }
+
+ fetch('/api/zamba/train/start', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify({ model, dryRun, labels }),
+ })
+ .then(response => response.json())
+ .then(data => {
+ console.log(data);
+ if (data.error) {
+ setTrainStatus(`Error: ${data.error}`);
+ } else {
+ setTrainStatus(`Training started: ${data.message}\nTask ID: ${data.task_id}`);
+ setTaskId(data.task_id);
+ }
+ })
+ .catch(error => {
+ console.error('Error:', error);
+ setTrainStatus(`Error: ${error.message}`);
+ });
+ }
+
+ return (
+ <div className="min-h-screen bg-base">
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Train a model on labeled videos or images</h1>
+ <p className="text-lg mb-8 text-center text-primary">
+ For more detailed information, check the <a href="https://zamba.drivendata.org/docs/stable/train-tutorial/" className="link">User Tutorial</a>.
+ </p>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4 text-gray-700">Upload a CSV containing the video labels to use as ground truth during training:</h2>
+ <p className="mb-4">There must be columns for both <code>filepath</code> and <code>label</code>.</p>
+ {/* CSV file upload */}
+ <div>
+ <label htmlFor="labelCsv" className="block text-sm font-medium text-gray-700 mb-2">
+ CSV file:
+ </label>
+ <input
+ type="file"
+ name="labelCsv"
+ accept=".csv"
+ className="file-input file-input-bordered file-input-primary bg-white w-full max-w-xs"
+ onChange={(e) => setLabelCsv(e.target.files[0])}
+ />
+ {labelCsv && (
+ <div className="mt-4">
+ <p className="font-semibold">Selected label CSV file:</p>
+ <ul>
+ <li>{labelCsv.name}</li>
+ </ul>
+ </div>
+ )}
+ </div>
+ {/* Videos upload */}
+ <div className='mt-2'>
+ <label htmlFor="videos" className="block text-sm font-medium text-gray-700 mb-2">
+ Videos:
+ </label>
+ <button onClick={() => handleOpenFileBrowser()} name="videos" className="btn btn-primary mr-2">Select Videos</button>
+ {selectedItems.length > 0 && (
+ <div className="mt-4">
+ <h3>Selected Images/Videos:</h3>
+ <ul>
+ {selectedItems.map((item, index) => (
+ <li key={index}>{item.name} ({item.type})</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ </div>
+ <button className='btn btn-primary mt-4' onClick={handleUpload}>Upload Selected Items</button>
+ {uploadSuccess && (
+ <div role="alert" className="alert alert-success mt-4">
+ <span>Uploaded successfully!</span>
+ </div>
+ )}
+ </div>
+
+ <div className="bg-base-200 shadow-md rounded-lg p-6 mb-8">
+ <h2 className="text-2xl font-semibold mb-4 text-gray-700">Configuration</h2>
+ <p className="mb-4">If you wish to finetune an existing model, please select the model here:</p>
+
+ <div className='flex flex-col'>
+ <div className='dropdown dropdown-bottom'>
+ <label htmlFor="model" className="block text-sm font-medium text-gray-700 mb-2">
+ Model:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {model ? model : 'Model'}
+ </div>
+ <ul tabIndex={0} className="dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs">
+ <li><button onClick={() => setModel('time_distributed')}>time_distributed (default)</button></li>
+ <li><button onClick={() => setModel('blank_nonblank')}>blank_nonblank</button></li>
+ <li><button onClick={() => setModel('slowfast')}>slowfast</button></li>
+ <li><button onClick={() => setModel('european')}>european</button></li>
+ </ul>
+ </div>
+
+ <div className='dropdown dropdown-bottom'>
+ <label htmlFor="dryRun" className='block text-sm font-medium text-gray-700 mb-2'>
+ Dry run:
+ </label>
+ <div tabIndex={0} role="button" className="btn m-1 w-full max-w-xs bg-white">
+ {dryRun ? dryRun : 'Dry run'}
+ </div>
+ <ul tabIndex={0} className='dropdown-content z-[1] menu p-2 shadow bg-white rounded-box w-full max-w-xs'>
+ <li><button onClick={() => setDryRun('false')}>false</button></li>
+ <li><button onClick={() => setDryRun('true')}>true</button></li>
+ </ul>
+ </div>
+ </div>
+
+
+ </div>
+ {/* Submit request */}
+ <p>Select the Train model button to confirm training the model. This process may take a while.</p>
+ <button className='btn btn-primary' onClick={triggerTrain}>Train model</button>
+
+ {/* Display task status/progress when running classification */}
+ <div className='mt-4'>{trainStatus}</div>
+ <div className='mt-4'>{taskStatus}</div>
+ </div>
+ <ImageBrowserPopup onSelect={handleSelectItems} allowedTypes={allowedTypes} />
+ </div>
+ )
+}
+
+export default ZambaTrain;
\ No newline at end of file
diff --git a/frontend/src/components/datamanagement/Dashboard.jsx b/frontend/src/components/datamanagement/Dashboard.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..01856f6a3f44c8ecb4cf242215c8317ac9954af4
--- /dev/null
+++ b/frontend/src/components/datamanagement/Dashboard.jsx
@@ -0,0 +1,44 @@
+import React, { useState } from 'react';
+import Datepicker from "react-tailwindcss-datepicker";
+import DoughnutChart from './dashboard/DoughnutChart';
+import LineChart from './dashboard/LineChart';
+
+function Dashboard() {
+ const [dateValue, setDateValue] = useState({
+ startDate: new Date(),
+ endDate: new Date()
+ });
+
+ const handleDatePickerValueChange = (newValue) => {
+ console.log("newValue:", newValue);
+ setDateValue(newValue);
+ }
+
+ return (
+ <div>
+ <div className="container mx-auto px-4 py-8">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Insights</h1>
+ <Datepicker
+ containerClassName="w-72"
+ value={dateValue}
+ theme={"light"}
+ inputClassName="input input-bordered w-72"
+ popoverDirection={"down"}
+ toggleClassName="invisible"
+ onChange={handleDatePickerValueChange}
+ showShortcuts={true}
+ primaryColor={"white"}
+ popoverClassName="z-50"
+ />
+ {/* Charts */}
+
+ <div className="grid lg:grid-cols-2 mt-0 grid-cols-1 gap-6">
+ <DoughnutChart />
+ <LineChart />
+ </div>
+ </div>
+ </div>
+ );
+}
+
+export default Dashboard;
\ No newline at end of file
diff --git a/frontend/src/components/datamanagement/DataSpace.jsx b/frontend/src/components/datamanagement/DataSpace.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..8fabb1a4b444817f1cebfce49a44e3e277f77d63
--- /dev/null
+++ b/frontend/src/components/datamanagement/DataSpace.jsx
@@ -0,0 +1,339 @@
+import React, { useState, useCallback, useEffect } from 'react';
+
+const DataSpace = () => {
+ const [files, setFiles] = useState([]);
+ const [uploadedFiles, setUploadedFiles] = useState([]);
+ const [fileStructure, setFileStructure] = useState({});
+ const [selectedFolder, setSelectedFolder] = useState(null);
+ const s3Folder = 'uploads/'; // Define the S3 folder prefix
+ const [searchQuery, setSearchQuery] = useState('');
+ const [searchResults, setSearchResults] = useState([]);
+ const [fileToDelete, setFileToDelete] = useState(null);
+ const [selectedUploadFolder, setSelectedUploadFolder] = useState('');
+
+ // Handle file upload
+ const handleFileChange = useCallback((event) => {
+ const selectedFiles = event.target.files || event.dataTransfer.files;
+ console.log("Selected files:", selectedFiles);
+ setFiles(Array.from(selectedFiles));
+ }, []);
+
+ useEffect(() => {
+ console.log("Updated files state:", files);
+ console.log("Files length:", files.length);
+ }, [files]);
+
+ const handleDrop = useCallback((event) => {
+ event.preventDefault();
+ handleFileChange(event);
+ }, [handleFileChange]);
+
+ const handleDragOver = useCallback((event) => {
+ event.preventDefault();
+ }, []);
+
+ const handleUpload = async () => {
+ const formData = new FormData();
+ for (let file of files) {
+ formData.append('files', file);
+ }
+ formData.append('folder', selectedUploadFolder !== '' ? `${selectedUploadFolder}/` : selectedUploadFolder);
+
+ try {
+ const response = await fetch('/api/dataSpace/upload', {
+ method: 'POST',
+ body: formData,
+ });
+ const data = await response.json();
+ console.log('Upload response:', data);
+ fetchUploadedFiles();
+ setFiles([]); // Clear the file selection after upload
+ setSelectedUploadFolder(''); // Reset the selected folder
+ } catch (error) {
+ console.error('Upload error:', error);
+ }
+ };
+
+ // Handle S3 file structure display
+ useEffect(() => {
+ fetchFileStructure();
+ }, []);
+
+ const fetchFileStructure = async () => {
+ try {
+ const response = await fetch('/api/dataSpace/list_files');
+ const data = await response.json();
+ setFileStructure(data.file_structure);
+ } catch (error) {
+ console.error('Fetch error:', error);
+ }
+ };
+
+ const fetchUploadedFiles = async () => {
+ try {
+ const response = await fetch('/api/dataSpace/list_files');
+ const data = await response.json();
+ setUploadedFiles(data.files);
+ } catch (error) {
+ console.error('Fetch error:', error);
+ }
+ };
+
+ // Fetch files once on mount
+ useEffect(() => {
+ fetchUploadedFiles();
+ }, []);
+
+ const handleFolderSelect = (folderPath) => {
+ const fullPath = `${s3Folder}${folderPath}`;
+ setSelectedFolder(fullPath);
+ };
+
+ const renderFileStructure = (structure, path = '') => {
+ return Object.keys(structure).map((key) => {
+ const currentPath = path ? `${path}/${key}` : key;
+ if (key === 'files') {
+ return structure[key].map((file, index) => (
+ <li key={index}>
+ <a href={file.url} target="_blank" rel="noopener noreferrer">
+ {file.name}
+ </a>
+ </li>
+ ));
+ } else {
+ return (
+ <li key={key}>
+ <details>
+ <summary onClick={() => handleFolderSelect(currentPath)}>{key}</summary>
+ <ul>{renderFileStructure(structure[key], currentPath)}</ul>
+ </details>
+ </li>
+ );
+ }
+ });
+ };
+
+ const filteredFiles = selectedFolder
+ ? uploadedFiles.filter(file => file.path && file.path.startsWith(selectedFolder))
+ : uploadedFiles;
+
+ const searchFiles = useCallback((query) => {
+ if (!query.trim()) {
+ setSearchResults([]);
+ return;
+ }
+ const lowercasedQuery = query.toLowerCase();
+ const results = uploadedFiles.filter(file =>
+ file.name.toLowerCase().includes(lowercasedQuery)
+ );
+ setSearchResults(results);
+ }, [uploadedFiles]);
+
+ useEffect(() => {
+ searchFiles(searchQuery);
+ }, [searchQuery, searchFiles]);
+
+ const handleSearchChange = (event) => {
+ setSearchQuery(event.target.value);
+ };
+
+ const displayedFiles = searchQuery ? searchResults : filteredFiles;
+
+ // Delete file from S3
+ const openDeleteConfirmation = (file) => {
+ setFileToDelete(file);
+ };
+
+ const closeDeleteConfirmation = () => {
+ setFileToDelete(null);
+ };
+
+ const handleDelete = async () => {
+ if (!fileToDelete) return;
+
+ try {
+ const response = await fetch(`/api/dataSpace/delete?path=${fileToDelete.path}`, {
+ method: 'DELETE',
+ });
+ const data = await response.json();
+ if (response.ok) {
+ console.log('Delete response:', data);
+ // Refresh the file list after successful deletion
+ fetchUploadedFiles();
+ } else {
+ console.error('Delete failed:', data.error);
+ }
+ } catch (error) {
+ console.error('Delete error:', error);
+ }
+ closeDeleteConfirmation();
+ };
+
+ return (
+ <div>
+ <div className="container mx-auto px-4 py-8 flex flex-col items-center justify-center">
+ <h1 className="text-4xl font-bold mb-4 text-center text-primary">Data Space</h1>
+ {/* Upload Files */}
+ <div className="flex items-center justify-center w-full">
+ <label
+ htmlFor="dropzone-file"
+ className="flex flex-col items-center justify-center w-full h-64 border-2 border-gray-300 border-dashed rounded-lg cursor-pointer bg-gray-50 dark:hover:bg-gray-800 dark:bg-gray-700 hover:bg-gray-100 dark:border-gray-600 dark:hover:border-gray-500 dark:hover:bg-gray-600"
+ onDrop={handleDrop}
+ onDragOver={handleDragOver}
+ >
+ <div className="flex flex-col items-center justify-center pt-5 pb-6">
+ <svg className="w-8 h-8 mb-4 text-gray-500 dark:text-gray-400" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 20 16">
+ <path stroke="currentColor" strokeLinecap="round" strokeLinejoin="round" strokeWidth="2" d="M13 13h3a3 3 0 0 0 0-6h-.025A5.56 5.56 0 0 0 16 6.5 5.5 5.5 0 0 0 5.207 5.021C5.137 5.017 5.071 5 5 5a4 4 0 0 0 0 8h2.167M10 15V6m0 0L8 8m2-2 2 2"/>
+ </svg>
+ <p className="mb-2 text-sm text-gray-500 dark:text-gray-400"><span className="font-semibold">Click to upload</span> or drag and drop</p>
+ <p className="text-xs text-gray-500 dark:text-gray-400">SVG, PNG, JPG or GIF (MAX. 800x400px)</p>
+ </div>
+ <input id="dropzone-file" type="file" className="hidden" multiple onChange={handleFileChange}/>
+ </label>
+ </div>
+ {/* Select Upload Destination */}
+ <div className="mb-4">
+ <span className="mr-2">Upload destination:</span>
+ <select
+ value={selectedUploadFolder}
+ onChange={(e) => setSelectedUploadFolder(e.target.value)}
+ className="select select-bordered w-full max-w-xs"
+ >
+ <option value="">Root</option>
+ {Object.keys(fileStructure).map((folder) => (
+ <option key={folder} value={folder}>{folder}</option>
+ ))}
+ </select>
+ </div>
+ {files.length > 0 && (
+ <div className="mt-4">
+ <h2 className="text-2xl font-bold mb-4">Selected Files</h2>
+ <ul>
+ {files.map((file, index) => (
+ <li key={index}>{file.name}</li>
+ ))}
+ </ul>
+ </div>
+ )}
+ <button className="btn btn-secondary mt-4" onClick={handleUpload}>Upload</button>
+
+ {/* S3 File struture display */}
+ <div className="mt-4 w-full">
+ <h2 className="text-2xl font-bold mb-4 text-center">Uploaded Files</h2>
+ <div id="file-list-container" className="flex w-full">
+ {/* Folder Structure Menu */}
+ <div id="file-structure" className="w-64 flex-shrink-0">
+ <ul className="menu menu-xs bg-base-200 rounded-lg w-full h-full">
+ {renderFileStructure(fileStructure)}
+ </ul>
+ </div>
+ <div id="file-list" className="flex-grow overflow-hidden">
+ {/* Action Bar - Breadcrumbs and Search Bar */}
+ <div id="action-bar" className="flex justify-between items-center mb-4">
+ <div className="breadcrumbs text-sm ml-4">
+ <ul>
+ <li>Home</li>
+ <li>Documents</li>
+ <li>Add Document</li>
+ </ul>
+ </div>
+ <label className="input input-bordered flex items-center gap-2">
+ <input
+ type="text"
+ className="grow"
+ placeholder="Search"
+ value={searchQuery}
+ onChange={handleSearchChange}
+ />
+ <svg
+ xmlns="http://www.w3.org/2000/svg"
+ viewBox="0 0 16 16"
+ fill="currentColor"
+ className="h-4 w-4 opacity-70">
+ <path
+ fillRule="evenodd"
+ d="M9.965 11.026a5 5 0 1 1 1.06-1.06l2.755 2.754a.75.75 0 1 1-1.06 1.06l-2.755-2.754ZM10.5 7a3.5 3.5 0 1 1-7 0 3.5 3.5 0 0 1 7 0Z"
+ clipRule="evenodd" />
+ </svg>
+ </label>
+ </div>
+ {/* File List Table */}
+ <div className="overflow-x-auto w-full">
+ <table className="table w-full">
+ <thead>
+ <tr>
+ <th>
+ <label>
+ <input type="checkbox" className="checkbox" />
+ </label>
+ </th>
+ <th>Image</th>
+ <th>Type</th>
+ <th>Last Modified</th>
+ <th></th>
+ </tr>
+ </thead>
+ <tbody>
+ {displayedFiles && displayedFiles.length > 0 ? (
+ displayedFiles.map((file, index) => (
+ <tr key={index}>
+ <th>
+ <label>
+ <input type="checkbox" className="checkbox" />
+ </label>
+ </th>
+ <td>
+ <div className="flex items-center gap-3">
+ <div className="avatar">
+ <div className="mask mask-square h-12 w-12">
+ <img
+ src={file.content}
+ alt={file.name} />
+ </div>
+ </div>
+ <div>{file.name}</div>
+ </div>
+ </td>
+ <td>{file.type}</td>
+ <td>{file.uploadDate}</td>
+ <th>
+ <button
+ onClick={() => openDeleteConfirmation(file)}
+ className="btn btn-ghost btn-xs"
+ >
+ delete
+ </button>
+ </th>
+ </tr>
+ ))
+ ) : (
+ <tr>
+ <td colSpan="5" className="text-center">No files to display</td>
+ </tr>
+ )}
+ </tbody>
+ </table>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ {/* Confirmation Dialog */}
+ <dialog id="delete_confirmation" className={`modal ${fileToDelete ? 'modal-open' : ''}`}>
+ <form method="dialog" className="modal-box">
+ <h3 className="font-bold text-lg">Confirm Deletion</h3>
+ <p className="py-4">Are you sure you want to delete {fileToDelete?.name}?</p>
+ <div className="modal-action">
+ <button className="btn" onClick={closeDeleteConfirmation}>Cancel</button>
+ <button className="btn btn-error" onClick={handleDelete}>Delete</button>
+ </div>
+ </form>
+ <form method="dialog" className="modal-backdrop">
+ <button onClick={closeDeleteConfirmation}>close</button>
+ </form>
+ </dialog>
+ </div>
+ );
+};
+
+export default DataSpace;
\ No newline at end of file
diff --git a/frontend/src/components/datamanagement/dashboard/DoughnutChart.js b/frontend/src/components/datamanagement/dashboard/DoughnutChart.js
new file mode 100644
index 0000000000000000000000000000000000000000..6bdf4ee7a1ba998638507a23dbef3008e638b16c
--- /dev/null
+++ b/frontend/src/components/datamanagement/dashboard/DoughnutChart.js
@@ -0,0 +1,92 @@
+import {
+ Chart as ChartJS,
+ Filler,
+ ArcElement,
+ Tooltip,
+ Legend,
+} from 'chart.js';
+import { Doughnut } from 'react-chartjs-2';
+import TitleCard from './TitleCard';
+import { useState, useEffect } from 'react';
+
+ChartJS.register(ArcElement, Tooltip, Legend,
+ Tooltip,
+ Filler,
+ Legend);
+
+function DoughnutChart(){
+ const [usageData, setUsageData] = useState({ usage_data: [], total_runtime: 0 });
+
+ useEffect(() => {
+ const fetchUsageData = async () => {
+ try {
+ const response = await fetch('/api/apps/usage');
+ const data = await response.json();
+ setUsageData(data);
+ } catch (error) {
+ console.error('Error fetching usage data:', error);
+ }
+ };
+
+ fetchUsageData();
+ }, []);
+
+ const options = {
+ responsive: true,
+ plugins: {
+ legend: {
+ position: 'top',
+ },
+ tooltip: {
+ callbacks: {
+ label: function(context) {
+ let label = context.label || '';
+ if (label) {
+ label += ': ';
+ }
+ const hours = (context.raw).toFixed(2);
+ label += `${hours} hours`;
+ return label;
+ }
+ }
+ }
+ },
+ };
+
+ const labels = usageData.usage_data.map(item => item.app_name)
+
+ const data = {
+ labels,
+ datasets: [
+ {
+ label: 'Runtime (hours)',
+ data: usageData.usage_data.map(item => item.runtime_seconds / 3600),
+ backgroundColor: [
+ 'rgba(255, 99, 132, 0.8)',
+ 'rgba(54, 162, 235, 0.8)',
+ 'rgba(255, 206, 86, 0.8)',
+ 'rgba(75, 192, 192, 0.8)',
+ 'rgba(153, 102, 255, 0.8)',
+ 'rgba(255, 159, 64, 0.8)',
+ ],
+ borderColor: [
+ 'rgba(255, 99, 132, 1)',
+ 'rgba(54, 162, 235, 1)',
+ 'rgba(255, 206, 86, 1)',
+ 'rgba(75, 192, 192, 1)',
+ 'rgba(153, 102, 255, 1)',
+ 'rgba(255, 159, 64, 1)',
+ ],
+ borderWidth: 1,
+ }
+ ],
+ };
+
+ return(
+ <TitleCard title={"Usage Time by App"}>
+ <Doughnut options={options} data={data} />
+ </TitleCard>
+ )
+}
+
+export default DoughnutChart
diff --git a/frontend/src/components/datamanagement/dashboard/LineChart.js b/frontend/src/components/datamanagement/dashboard/LineChart.js
new file mode 100644
index 0000000000000000000000000000000000000000..f28487b75c1f4a2a1d03e253e86c470283faccca
--- /dev/null
+++ b/frontend/src/components/datamanagement/dashboard/LineChart.js
@@ -0,0 +1,69 @@
+import {
+ Chart as ChartJS,
+ CategoryScale,
+ LinearScale,
+ PointElement,
+ LineElement,
+ Title,
+ Tooltip,
+ Filler,
+ Legend,
+} from 'chart.js';
+import { Line } from 'react-chartjs-2';
+import TitleCard from './TitleCard';
+
+ChartJS.register(
+CategoryScale,
+LinearScale,
+PointElement,
+LineElement,
+Title,
+Tooltip,
+Filler,
+Legend
+);
+
+function LineChart(){
+
+const options = {
+ responsive: true,
+ plugins: {
+ legend: {
+ position: 'top',
+ },
+ },
+};
+
+
+const labels = ['January', 'February', 'March', 'April', 'May', 'June', 'July'];
+
+const data = {
+labels,
+datasets: [
+ {
+ fill: false,
+ label: 'Lion',
+ data: labels.map(() => { return Math.random() * 100 + 500 }),
+ borderColor: 'rgb(53, 162, 235)',
+ backgroundColor: 'rgba(53, 162, 235, 0.5)',
+ },
+ {
+ fill: false,
+ label: 'Leopard',
+ data: labels.map(() => { return Math.random() * 100 + 500 }),
+ borderColor: 'rgba(255, 206, 86, 1)',
+ backgroundColor: 'rgba(255, 206, 86, 0.5)',
+ },
+],
+};
+
+
+ return(
+ <TitleCard title={"Activity Pattern"} >
+ <Line data={data} options={options}/>
+ </TitleCard>
+ )
+}
+
+
+export default LineChart
\ No newline at end of file
diff --git a/frontend/src/components/datamanagement/dashboard/TitleCard.js b/frontend/src/components/datamanagement/dashboard/TitleCard.js
new file mode 100644
index 0000000000000000000000000000000000000000..757a140510572e0ccf8c194fc941b14f7d77655c
--- /dev/null
+++ b/frontend/src/components/datamanagement/dashboard/TitleCard.js
@@ -0,0 +1,34 @@
+function Subtitle({styleClass, children}){
+ return(
+ <div className={`text-xl font-semibold ${styleClass}`}>{children}</div>
+ )
+}
+
+
+function TitleCard({title, children, topMargin, TopSideButtons}){
+ return(
+ <div className={"card w-full p-6 bg-base-100 shadow-xl " + (topMargin || "mt-6")}>
+
+ {/* Title for Card */}
+ <Subtitle styleClass={TopSideButtons ? "inline-block" : ""}>
+ {title}
+
+ {/* Top side button, show only if present */}
+ {
+ TopSideButtons && <div className="inline-block float-right">{TopSideButtons}</div>
+ }
+ </Subtitle>
+
+ <div className="divider mt-2"></div>
+
+ {/** Card Body */}
+ <div className='h-full w-full pb-6 bg-base-100'>
+ {children}
+ </div>
+ </div>
+
+ )
+}
+
+
+ export default TitleCard
\ No newline at end of file
diff --git a/frontend/src/index.css b/frontend/src/index.css
new file mode 100644
index 0000000000000000000000000000000000000000..bd6213e1dfe6b0a79ce7d8b37d0d2dc70f0250bb
--- /dev/null
+++ b/frontend/src/index.css
@@ -0,0 +1,3 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
\ No newline at end of file
diff --git a/frontend/src/index.js b/frontend/src/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..53eef8274951b49850c1bb1a8a7adfcd249df9c2
--- /dev/null
+++ b/frontend/src/index.js
@@ -0,0 +1,18 @@
+import React from 'react';
+import ReactDOM from 'react-dom/client';
+import App from './App';
+import reportWebVitals from './reportWebVitals';
+import './index.css';
+import 'tailwindcss/tailwind.css';
+
+const root = ReactDOM.createRoot(document.getElementById('root'));
+root.render(
+ <React.StrictMode>
+ <App />
+ </React.StrictMode>
+);
+
+// If you want to start measuring performance in your app, pass a function
+// to log results (for example: reportWebVitals(console.log))
+// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals
+reportWebVitals();
diff --git a/frontend/src/reportWebVitals.js b/frontend/src/reportWebVitals.js
new file mode 100644
index 0000000000000000000000000000000000000000..5253d3ad9e6be6690549cb255f5952337b02401d
--- /dev/null
+++ b/frontend/src/reportWebVitals.js
@@ -0,0 +1,13 @@
+const reportWebVitals = onPerfEntry => {
+ if (onPerfEntry && onPerfEntry instanceof Function) {
+ import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {
+ getCLS(onPerfEntry);
+ getFID(onPerfEntry);
+ getFCP(onPerfEntry);
+ getLCP(onPerfEntry);
+ getTTFB(onPerfEntry);
+ });
+ }
+};
+
+export default reportWebVitals;
diff --git a/frontend/src/setupProxy.js b/frontend/src/setupProxy.js
new file mode 100644
index 0000000000000000000000000000000000000000..78e0fa83cd7d9fc7c0ec966d7b2bd92f563e6fa5
--- /dev/null
+++ b/frontend/src/setupProxy.js
@@ -0,0 +1,12 @@
+const { createProxyMiddleware } = require('http-proxy-middleware');
+
+module.exports = function(app) {
+ app.use(
+ '/api', // This is the path you want to proxy
+ createProxyMiddleware({
+ target: 'http://api:5001', // The target server - For Docker
+ // target: 'http://127.0.0.1:5001', // For local testing
+ changeOrigin: true,
+ })
+ );
+};
\ No newline at end of file
diff --git a/frontend/src/setupTests.js b/frontend/src/setupTests.js
new file mode 100644
index 0000000000000000000000000000000000000000..8f2609b7b3e0e3897ab3bcaad13caf6876e48699
--- /dev/null
+++ b/frontend/src/setupTests.js
@@ -0,0 +1,5 @@
+// jest-dom adds custom jest matchers for asserting on DOM nodes.
+// allows you to do things like:
+// expect(element).toHaveTextContent(/react/i)
+// learn more: https://github.com/testing-library/jest-dom
+import '@testing-library/jest-dom';
diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..4503562e1bb100578f7a819aeb735890eba9f692
--- /dev/null
+++ b/frontend/tailwind.config.js
@@ -0,0 +1,43 @@
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+ content: ["./src/**/*.{html,js,jsx}", "./node_modules/react-tailwindcss-datepicker/dist/index.esm.js"],
+ theme: {
+ extend: {},
+ },
+ plugins: [
+ require('daisyui')
+ ],
+ daisyui: {
+ themes: [
+ {
+ nature: {
+ "primary": "#283106",
+ "secondary": "#A3B18A",
+ "accent": "#B7B7A4",
+ "neutral": "#3A3A3A",
+ "base-100": "#F4F4F4",
+ "info": "#A3B18A",
+ "success": "#4CAF50",
+ "warning": "#FFC107",
+ "error": "#F44336",
+ }
+ },
+ {
+ night: {
+ "primary": "#A3B18A",
+ "secondary": "#5A6B47",
+ "accent": "#8A8E7D",
+ "neutral": "#D1D1D1",
+ "base-100": "#1E2419",
+ "base-200": "#252D1F",
+ "base-300": "#2C3625",
+ "info": "#73A6B1",
+ "success": "#4CAF50",
+ "warning": "#FFC107",
+ "error": "#F44336",
+ }
+ }
+ ],
+ },
+}
+
diff --git a/requirements.txt b/requirements.txt
index 4e36fc88663e96b55df1f8bc3a53cedaba36d196..c3f5549617af52651366585a895e8ff0633a99da 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,123 +1,26 @@
-absl-py==2.1.0
-aiohttp==3.9.5
-aiosignal==1.3.1
-amqp==5.2.0
-appdirs==1.4.4
-async-timeout==4.0.3
-attrs==23.2.0
-av==12.3.0
-beautifulsoup4==4.12.3
-billiard==4.2.0
-blinker==1.8.2
-boto3==1.34.149
-botocore==1.34.149
-bounded-pool-executor==0.0.3
-bs4==0.0.2
-celery==5.4.0
-certifi==2024.7.4
-charset-normalizer==3.3.2
-click==8.1.7
-click-didyoumean==0.3.1
-click-plugins==1.1.1
-click-repl==0.3.0
-cloudpathlib==0.18.1
-et-xmlfile==1.1.0
-ffmpeg-python==0.2.0
-filelock==3.15.4
Flask==3.0.3
-flower==2.0.1
-frozenlist==1.4.1
-fsspec==2024.6.1
-future==1.0.0
-fvcore==0.1.5.post20221221
-gitdb==4.0.11
-GitPython==3.1.43
-greenlet==3.0.3
-grpcio==1.65.1
-huggingface-hub==0.24.3
-humanize==4.10.0
-idna==3.7
-importlib_metadata==7.1.0
-iopath==0.1.10
-itsdangerous==2.2.0
+Flask-Cors==4.0.1
+Flask-Login==0.6.3
+Flask-Bcrypt==1.0.1
+Flask-SQLAlchemy==3.1.1
+Werkzeug==3.0.3
Jinja2==3.1.4
-jmespath==1.0.1
-joblib==1.4.2
-kombu==5.3.7
-lightning-utilities==0.11.6
-loader==2017.9.11
-Logbook==1.7.0.post0
-loguru==0.7.2
-Markdown==3.6
-markdown-it-py==3.0.0
-MarkupSafe==2.1.5
-mdurl==0.1.2
-mpmath==1.3.0
-multidict==6.0.5
-mysql-connector-python==9.0.0
-networkx==3.2.1
-numpy==1.26.0
-opencv-python-headless==4.10.0.84
-openpyxl==3.1.5
-packaging==24.1
-pandas==2.1.4
-pandas-path==0.3.0
-parameterized==0.9.0
-pendulum==3.0.0
-pillow==10.4.0
-portalocker==2.10.1
-pqdm==0.2.0
-prometheus_client==0.20.0
-prompt_toolkit==3.0.47
-protobuf==4.25.4
-pydantic==1.10.17
-Pygments==2.18.0
+SQLAlchemy==2.0.34
PyMySQL==1.1.1
-python-dateutil==2.9.0.post0
-python-dotenv==1.0.1
-pytorch-lightning==2.3.3
-pytorchvideo==0.1.5
-pytz==2024.1
-PyYAML==6.0.1
+celery==5.4.0
redis==5.0.8
+boto3==1.34.149
requests==2.32.3
-rich==13.7.1
-s3transfer==0.10.2
-safetensors==0.4.3
-scikit-learn==1.5.1
+numpy==1.26.0
+pandas==2.1.4
scipy==1.13.1
-shellingham==1.5.4
-six==1.16.0
-smmap==5.0.1
-soupsieve==2.5
-SQLAlchemy==1.4.36
-sympy==1.13.1
-tabulate==0.9.0
-tensorboard==2.17.0
-tensorboard-data-server==0.7.2
-termcc==2019.8.2
-termcolor==2.4.0
-terminaltables==3.1.10
-thop==0.0.31.post2005241907
-threadpoolctl==3.5.0
-time-machine==2.14.2
-timm==1.0.8
+Pillow==10.4.0
+opencv-python-headless==4.10.0.84
torch==2.2.2
-torchinfo==1.8.0
-torchmetrics==1.4.0.post0
torchvision==0.17.2
-tornado==6.4.1
+zamba @ git+https://github.com/drivendataorg/zamba.git@v2.4.1
tqdm==4.66.4
-typer==0.12.3
-typing_extensions==4.12.2
-tzdata==2024.1
-urllib3==1.26.19
-vine==5.1.0
-wcwidth==0.2.13
-Werkzeug==3.0.3
-worker==2018.4.1
-WTForms==3.1.2
-yacs==0.1.8
-yarl==1.9.4
-zamba @ https://github.com/drivendataorg/zamba/releases/latest/download/zamba.tar.gz
-zipp==3.19.1
+GitPython==3.1.43
+python-dotenv==1.0.1
+docker==7.1.0
+megadetector==5.0.16
\ No newline at end of file