- Python 3.11+
- Flask
-
Install dependencies:
pip install -r requirements.txt
-
Configure environment variables (optional):
cp .env.example .env # Edit .env with your specific paths -
Run the application:
python app.py
-
Access at
http://localhost:5000
# Build the image
docker build -t enc-demo-website .
# Run with included sample data
docker run -p 8080:5000 enc-demo-websiteAccess at http://localhost:8080
The container includes sample data by default, so no configuration is needed for basic usage.
To use your own images and JSON results, mount your data directory and optionally set environment variables:
# Mount external data directory
docker run -p 8080:5000 -v /path/to/your/data:/app/data enc-demo-website
# Or specify custom paths with environment variables
docker run -p 8080:5000 \
-v /path/to/your/images:/custom/images \
-v /path/to/your/results:/custom/results \
-e IMAGE_FOLDER=/custom/images \
-e JSON_FOLDER_GPT4O=/custom/results/gpt-4o \
-e JSON_FOLDER_O4MINI=/custom/results/o4-mini \
enc-demo-websiteIMAGE_FOLDER: Path to raw images directory (default:./data/raw_images)JSON_FOLDER_GPT4O: Path to GPT-4o results directory (default:./data/results/gpt-4o)JSON_FOLDER_O4MINI: Path to O4-mini results directory (default:./data/results/o4-mini)
Images are automatically built and published to ghcr.io via GitHub Actions on pushes to main branch.
# Simple usage with included data
docker run -p 8080:5000 ghcr.io/epfl-timemachine/enc-demo-website:main
# With custom data
docker run -p 8080:5000 -v /path/to/your/data:/app/data ghcr.io/epfl-timemachine/enc-demo-website:main
# With custom data mounted to custom paths with environment variables
docker run -p 8080:5000 \
-v /path/to/your/images:/custom/images \
-v /path/to/your/results:/custom/results \
-e IMAGE_FOLDER=/custom/images \
-e JSON_FOLDER_GPT4O=/custom/results/gpt-4o \
-e JSON_FOLDER_O4MINI=/custom/results/o4-mini \
ghcr.io/epfl-timemachine/enc-demo-website:main