Spaces:
Runtime error
Runtime error
| """ | |
| Hugging Face Space (Gradio) for the Plutus Learning Material Recommender. | |
| CPU-only. No GPU required. Works with the free HF Space hardware. | |
| Deploy by uploading this folder (app.py, recommender.py, rag.py, data/) to a Gradio Space. | |
| """ | |
| import gradio as gr | |
| from recommender import PlutusRecommender | |
| # Load once at startup (tiny corpus → instant) | |
| recommender = PlutusRecommender(mode="bm25") | |
| ALL_TOPICS = recommender.list_topics() | |
| def recommend_ui(query: str, k: int, tool: str, exclude_text: str): | |
| if not query.strip(): | |
| return "Please enter what you want to learn." | |
| exclude_ids = [x.strip() for x in exclude_text.split(",") if x.strip()] if exclude_text else None | |
| results = recommender.recommend(query, k=int(k), tool=tool, exclude_ids=exclude_ids) | |
| if not results: | |
| return "No recommendations found. Try a different query." | |
| lines = [f"**Recommendations for:** _{query}_\n"] | |
| for i, r in enumerate(results, 1): | |
| topics = ", ".join(r["topics"][:4]) if r["topics"] else "—" | |
| lines.append( | |
| f"### {i}. {r['title']} \n" | |
| f"**Score:** {r['score']} | **Tool:** `{r['tool']}` | **Topics:** {topics} \n" | |
| f"[Open source]({r['url']}) \n" | |
| f"{r['snippet']}\n" | |
| ) | |
| return "\n".join(lines) | |
| def topic_recommend(selected_topics: list[str], k: int): | |
| if not selected_topics: | |
| return "Select at least one topic." | |
| results = recommender.recommend_by_topics(selected_topics, k=int(k)) | |
| if not results: | |
| return "No recommendations." | |
| lines = [f"**Recommendations for topics:** {', '.join(selected_topics)}\n"] | |
| for i, r in enumerate(results, 1): | |
| lines.append( | |
| f"### {i}. {r['title']} \n" | |
| f"**Score:** {r['score']} | [Source]({r['url']}) \n" | |
| f"{r['snippet']}\n" | |
| ) | |
| return "\n".join(lines) | |
| with gr.Blocks(title="Plutus Learning Recommender", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown( | |
| """ | |
| # Plutus Learning Material Recommender | |
| Simple content-based recommendations over the Plutus Learner RAG corpus. | |
| Enter what you want to learn (or pick topics) and get ranked learning resources. | |
| **Runs entirely on CPU** — suitable for free Hugging Face Spaces. | |
| """ | |
| ) | |
| with gr.Tab("Query Recommendation"): | |
| query_in = gr.Textbox( | |
| label="What do you want to learn?", | |
| placeholder="e.g. difference between datum and redeemer, Plutus V3 validators, collateral...", | |
| lines=2, | |
| ) | |
| with gr.Row(): | |
| k_slider = gr.Slider(1, 10, value=5, step=1, label="Number of recommendations") | |
| tool_dd = gr.Dropdown( | |
| choices=["all", "plinth", "cardano"], | |
| value="all", | |
| label="Filter by tool", | |
| ) | |
| exclude_in = gr.Textbox( | |
| label="Exclude chunk IDs (comma-separated, optional)", | |
| placeholder="leave empty", | |
| ) | |
| rec_btn = gr.Button("Get Recommendations", variant="primary") | |
| output = gr.Markdown() | |
| rec_btn.click( | |
| recommend_ui, | |
| inputs=[query_in, k_slider, tool_dd, exclude_in], | |
| outputs=output, | |
| ) | |
| with gr.Tab("By Topic"): | |
| topic_choices = gr.CheckboxGroup(choices=ALL_TOPICS, label="Select topics") | |
| topic_k = gr.Slider(1, 10, value=5, step=1, label="Number of recommendations") | |
| topic_btn = gr.Button("Recommend from topics", variant="primary") | |
| topic_output = gr.Markdown() | |
| topic_btn.click(topic_recommend, inputs=[topic_choices, topic_k], outputs=topic_output) | |
| gr.Markdown( | |
| f""" | |
| --- | |
| **Corpus:** {recommender.manifest.get('chunk_count', '?')} chunks | |
| · Mode: BM25 (pure Python) | |
| · Source: Plutus Learner RAG bootstrap notes | |
| This is a lightweight content-based recommender. For denser semantic search, | |
| install `fastembed` and switch the mode to `"hybrid"` in the code. | |
| """ | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |