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| title: Ai Academy 2024 Gr8 Recommender Api | |
| emoji: 📉 | |
| colorFrom: green | |
| colorTo: pink | |
| sdk: docker | |
| pinned: false | |
| license: mit | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # ai-academy-2024-group8 | |
| A lightweight backend API for song recommender | |
| Dataset used in this project is public and available from [online](https://www.kaggle.com/datasets/undefinenull/million-song-dataset-spotify-lastfm) | |
| ## What's in here | |
| - `data/`: Contains the trained `model.pkl` and related `model.csv` that has the training set in csv format | |
| - `notebooks/`: Contains any jupyter notebooks used in the project | |
| - `recommendation-api/`: A FastAPI app to serve user recommendations | |
| ## Running service locally | |
| 1. (Optional) Create an activate python venv | |
| 2. Install the requirements `pip install -r requirements.txt` | |
| 3. Start the service `python recommendation-api/server.py` | |
| Then | |
| - `curl http://localhost:7860/users` to fetch list of supported users | |
| - `curl http://localhost:7860/users/<id>` to fetch track history for individual user | |
| - `curl http://localhost:7860/recommend/<id>` to recommend tracks for the specific user | |
| ## Running in Huggingface | |
| Application is built and started on push to master. | |
| Application is available from [here](https://schibsted-ai-academy-2024-gr8-recommender-api.hf.space/docs) | |