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Update recommendation_module.py
Browse files- recommendation_module.py +60 -1
recommendation_module.py
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@@ -132,4 +132,63 @@ def get_recommendation(
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size_label = "large"
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# Build query
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else:
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size_label = "large"
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# Build query text — Sentence Transformer sẽ tìm case tương tự nhất
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query_text = (
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f"{site_type} ecotourism site, {size_label} size, "
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f"{visitor_count} visitors per day, {risk_level} waste risk. "
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f"Needs waste management improvement for sustainability certification."
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)
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# AI similarity search
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model = get_model()
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query_embedding = model.encode(query_text, convert_to_tensor=True)
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case_embeddings = get_case_embeddings()
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similarities = util.cos_sim(query_embedding, case_embeddings)[0]
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# Top 1 match
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best_idx = int(torch.argmax(similarities))
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best_case = CASES[best_idx]
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best_sim = float(similarities[best_idx])
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# All scores sorted
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scored = sorted(
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zip(CASES, similarities.tolist()),
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key=lambda x: -x[1]
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)
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rows = "\n".join([
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f"| {c['name']} | {s:.1%} |"
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for c, s in scored
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])
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output = f"""## 💡 Recommendation for your site
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**Query:** {query_text}
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**Most Similar Certified Site:** {best_case['name']} (similarity: {best_sim:.1%})
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---
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### 📋 3-Phase Action Roadmap
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**Phase 1 — Immediate (within 24 hours)**
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{best_case['rec_immediate']}
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**Phase 2 — Short Term (this month)**
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{best_case['rec_monthly']}
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**Phase 3 — Long Term (6-12 months)**
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{best_case['rec_longterm']}
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---
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### ✅ Proven Result from Similar Site
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{best_case['result']}
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---
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### Similarity Scores (All Reference Sites)
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| Site | Similarity |
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|------|-----------|
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{rows}
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"""
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return output, combined_risk
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