Update app.py

app.py

@@ -191,38 +191,38 @@ def hybrid_search_no_gemini(query: str, match_count: int = 50):
     pairs = [(query, c["judul"] + " " + c["deskripsi"]) for c in candidates]
 
     try:
-            scores = reranker.predict(pairs)
-        except Exception as e:
-            print("Reranker error:", e)
-            return {"results": sorted(candidates, key=lambda x: x.get("similarity", 0), reverse=True)[:10]}
-    
-        # Ambil nilai min dan max dari skor RRF bawaan database
-        db_scores = [c.get("similarity", 0.0) for c in candidates]
-        db_min = min(db_scores) if db_scores else 0.0
-        db_max = max(db_scores) if db_scores else 1.0
-    
-        for c, s in zip(candidates, scores):
-            # 1. Probabilitas Reranker (0 sampai 1)
-            c["rerank_score"] = float(s)
-            rerank_prob = apply_sigmoid(float(s))
-            c["rerank_prob"] = rerank_prob
+        scores = reranker.predict(pairs)
+    except Exception as e:
+        print("Reranker error:", e)
+        return {"results": sorted(candidates, key=lambda x: x.get("similarity", 0), reverse=True)[:10]}
+
+    # Ambil nilai min dan max dari skor RRF bawaan database
+    db_scores = [c.get("similarity", 0.0) for c in candidates]
+    db_min = min(db_scores) if db_scores else 0.0
+    db_max = max(db_scores) if db_scores else 1.0
+
+    for c, s in zip(candidates, scores):
+        # 1. Probabilitas Reranker (0 sampai 1)
+        c["rerank_score"] = float(s)
+        rerank_prob = apply_sigmoid(float(s))
+        c["rerank_prob"] = rerank_prob
+        
+        # 2. Normalisasi Skor Database (RRF) ke 0 sampai 1
+        raw_db_sim = c.get("similarity", 0.0)
+        if db_max > db_min:
+            norm_db_sim = (raw_db_sim - db_min) / (db_max - db_min)
+        else:
+            norm_db_sim = 0.0
             
-            # 2. Normalisasi Skor Database (RRF) ke 0 sampai 1
-            raw_db_sim = c.get("similarity", 0.0)
-            if db_max > db_min:
-                norm_db_sim = (raw_db_sim - db_min) / (db_max - db_min)
-            else:
-                norm_db_sim = 0.0
-                
-            c["norm_db_sim"] = norm_db_sim # Opsional: simpan untuk debugging
-    
-            # 3. Interpolasi yang SEIMBANG
-            c["final_score"] = (0.6 * rerank_prob) + (0.4 * norm_db_sim)
+        c["norm_db_sim"] = norm_db_sim # Opsional: simpan untuk debugging
+
+        # 3. Interpolasi yang SEIMBANG
+        c["final_score"] = (0.6 * rerank_prob) + (0.4 * norm_db_sim)
+
+    # Urutkan berdasarkan final_score
+    candidates = sorted(candidates, key=lambda x: x["final_score"], reverse=True)
     
-        # Urutkan berdasarkan final_score
-        candidates = sorted(candidates, key=lambda x: x["final_score"], reverse=True)
-        
-        return {"results": candidates[:10]}
+    return {"results": candidates[:10]}
 
 # ==========================================
 # CORE APPS