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GreenFit AI

Find out about sustainability in your favorite sport clothing brands, in just one click!🌱

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For users

Go to our Streamlit demo webapp and get a hands-on experience of GreenFit AI!

Otherwise, head over to our product showcase website and feel free to explore the product from there

For developers

1. Build from source

You can build the app from source by:

  • Cloning this repo:
git clone https://github.com/AstraBert/greenfit-ai
cd greenfit-ai
  • Create a virtual environment and activate it:
python3 -m venv streamlit-app
source streamlit-app/bin/activate
  • Install the required dependencies:
python3 -m pip install -r requirements.txt
  • Build your .streamlit/secrets.toml file like this:
rapid_api_key="RAPID_API_KEY"
openai_api_key="OPENAI_API_KEY"
qdrant_api_key="QDRANT_API_KEY"
qdrant_url="QDRANT_URL"
courier_auth_token="COURIER_AUTH_TOKEN"
supa_key="SUPABASE_ANON_KEY"
supa_url="SUPABASE_URL"
  • Run the application:
python3 -m streamlit run app.py

2. Use the Docker image

There is a Docker image available on the GitHub Container Registry at ghcr.io/astrabert/greenfit-ai.

You can choose two ways to use the Docker image:

  • From the pre-built image:
    • Pull the image: we advise you pull the main tag, as it is on track with the latest modifications to the app
    docker pull ghcr.io/astrabert/beans-ai:main
    • Create a .streamlit/secrets.toml as specified in the previous section
    • Create a .env file and specify the path to .streamlit/secrets.toml under the STREAMLIT_SECRETS_PATH variable
    • Launch docker compose and find the app http://localhost:8501:
    docker compose up -d
  • Build the image yourself (assuming you have cloned the repository and you are inside it):
    • Create a .streamlit/secrets.toml as specified in the previous section
    • Create a .env file and specify the path to .streamlit/secrets.toml under the STREAMLIT_SECRETS_PATH variable
    • Launch docker compose with the build option and find the app http://localhost:8501:
    docker compose -f compose.build.yaml up -d

Find an example of the .env file on .env.example