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| title: Plutus Learning Recommender | |
| emoji: 📚 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: "4.44.0" | |
| app_file: app.py | |
| pinned: false | |
| # Plutus Learning Material Recommender | |
| Simple content-based recommendation system built on the Plutus Learner RAG corpus. | |
| ## Features | |
| - BM25 ranking (pure Python, zero heavy dependencies) | |
| - Query-based recommendations | |
| - Topic-based recommendations | |
| - Filter by tool (plinth / cardano) | |
| - Runs on Colab CPU and free Hugging Face CPU Spaces | |
| ## Local / Colab usage | |
| ```bash | |
| # In the plutus_recommender folder | |
| python recommender.py "What is the difference between datum and redeemer?" | |
| ``` | |
| Or in a notebook: | |
| ```python | |
| from recommender import PlutusRecommender | |
| rec = PlutusRecommender() | |
| results = rec.recommend("Plutus V3 validators and ScriptContext", k=5) | |
| for r in results: | |
| print(r["title"], r["score"], r["url"]) | |
| ``` | |
| ## Deploy to Hugging Face Space (CPU) | |
| 1. Create a new **Gradio** Space on Hugging Face. | |
| 2. Upload the entire `plutus_recommender/` folder contents: | |
| - `app.py` | |
| - `recommender.py` | |
| - `rag.py` | |
| - `data/` (the index folder) | |
| - `requirements.txt` | |
| - `README.md` (this file) | |
| 3. The Space will start automatically. No GPU needed. | |
| ## Optional: Dense / Hybrid mode | |
| For semantic recommendations install FastEmbed (still CPU): | |
| ```bash | |
| pip install fastembed numpy | |
| ``` | |
| Then in code: | |
| ```python | |
| # First build vectors (one-time, in Colab) | |
| from rag import embed_index | |
| embed_index("data/index") | |
| # Then | |
| rec = PlutusRecommender(mode="hybrid") | |
| ``` | |
| ## Corpus note | |
| The bundled index contains 15 bootstrap summary chunks. | |
| To expand the corpus, use the original `ingest.py` from the RAG bundle against `sources.json`. | |