Update app.py

app.py

@@ -39,7 +39,7 @@ for root, _, files in os.walk("."):
             ALL_IMAGE_PATHS.append(os.path.join(root, f))
 
 def find_any_matching_image(dish_name):
-    """Robust image search logic."""
+    """Robust image search logic based on keyword matching."""
     name_clean = str(dish_name).lower()
     keywords = [kw for kw in re.findall(r'\w+', name_clean) if len(kw) > 2]
     if not keywords: return None
@@ -67,13 +67,19 @@ def get_hybrid_recommendations(user_query, user_name, city, hobbies, style, alph
         return [None, "### ⚠️ Identity required."] * 3
         
     user_profile_text = f"{user_name} from {city} loves {hobbies} and has a {style} style."
+    
+    # NEW: Create embeddings for both query and persona
     query_emb = model.encode([user_query])
     profile_emb = model.encode([user_profile_text])
     
+    # Calculate similarities (The Hybrid Logic)
     content_sim = cosine_similarity(query_emb, dish_embeddings).flatten()
     user_sim = cosine_similarity(profile_emb, user_embeddings).flatten()
+    
+    # Weighting: 65% Craving (Content) + 35% Persona (Demographics)
     raw_scores = (alpha * content_sim) + (1 - alpha) * user_sim
     
+    # Normalizing scores for display (74%-99%)
     min_s, max_s = raw_scores.min(), raw_scores.max()
     scaled_scores = 0.74 + (raw_scores - min_s) * (0.25) / (max_s - min_s) if max_s > min_s else raw_scores
     
@@ -84,14 +90,25 @@ def get_hybrid_recommendations(user_query, user_name, city, hobbies, style, alph
     output = []
     for _, row in top_results.iterrows():
         image_path = find_any_matching_image(row['dish_name'])
+        
+        # NEW: Generate the 'Culinary Twin' profile from the dataset metadata
+        # This provides transparency on WHY this dish was recommended
+        twin_info = f"A {row['user_age']} year old from {row['user_origin']}, with a {row['user_fashion_style']} style."
+        
         res_text = f"""
         <div style='font-family: serif; color: #4a3f35; padding: 15px; background: #fffcf7; border: 1px solid #e0d8c3; margin-bottom: 10px;'>
             <h2 style='margin-bottom: 2px; color: #5d4037; font-weight: normal; letter-spacing: 1px;'>{row['dish_name'].upper()}</h2>
             <p style='font-style: italic; margin-top: 0; color: #8c7b6c;'>{row['restaurant_name']} — {row['food_vibe']}</p>
-            <p style='font-size: 0.8em; letter-spacing: 1.5px;'>MATCH: {row['final_score']:.2%} | ⭐ {row['rating']}</p>
+            
+            <div style='background-color: #f0ece2; padding: 10px; margin: 10px 0; border-radius: 4px; font-size: 0.9em; border-left: 4px solid #5d4037;'>
+                <strong style='color: #5d4037;'>👤 Recommended by your Culinary Twin:</strong><br>
+                {twin_info}
+            </div>
+
+            <p style='font-size: 0.8em; letter-spacing: 1.5px;'>MATCH SCORE: {row['final_score']:.2%} | ⭐ {row['rating']}</p>
             <hr style='border: 0; border-top: 1px solid #f0ece2; margin: 12px 0;'>
-            <p style='line-height: 1.6;'><b>Vision:</b> {row['visual_description']}</p>
-            <p style='line-height: 1.6;'><b>Experience:</b> {row['taste_review']}</p>
+            <p style='line-height: 1.6;'><b>The Vision:</b> {row['visual_description']}</p>
+            <p style='line-height: 1.6;'><b>The Experience:</b> {row['taste_review']}</p>
         </div>
         """
         output.extend([image_path, res_text])