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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()
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