| import gradio as gr
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| from sentence_transformers import SentenceTransformer, util
|
| import os
|
|
|
|
|
| with open("data/handshape_book.txt", encoding="utf-8") as f:
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| book_text = f.read()
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|
|
|
|
| chunks = [chunk.strip() for chunk in book_text.split('\n\n') if len(chunk.strip()) > 100]
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|
|
|
|
| model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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| corpus_embeddings = model.encode(chunks, convert_to_tensor=True)
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|
|
|
|
| def ask_question(question):
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| question_embedding = model.encode(question, convert_to_tensor=True)
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| hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=3)[0]
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| results = "\n\n---\n\n".join([chunks[hit['corpus_id']] for hit in hits])
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| return results
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|
|
|
|
| iface = gr.Interface(fn=ask_question,
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| inputs=gr.Textbox(lines=2, placeholder="Ask about handshapes..."),
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| outputs="text",
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| title="ASL Handshape RAG",
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| description="Semantic search over American Sign Language Handshape Dictionary.")
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|
|
| iface.launch()
|
|
|
