Update app.py

app.py

@@ -8,55 +8,31 @@ import glob
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# --- 1. חילוץ תמונות (עם בדיקה) ---
+# --- 1. פתרון שורש לתמונות: פריקה לתיקייה קבועה ---
+IMAGE_DIR = "extracted_images"
 if os.path.exists('images.zip'):
-    try:
-        with zipfile.ZipFile('images.zip', 'r') as zip_ref:
-            zip_ref.extractall('extracted_images')
-        print("✅ Images extracted")
-    except Exception as e:
-        print(f"❌ Zip error: {e}")
+    with zipfile.ZipFile('images.zip', 'r') as zip_ref:
+        zip_ref.extractall(IMAGE_DIR)
 
-# --- 2. טעינת נתונים עם "הגנה" מקריסה ---
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-def load_colab_data():
-    try:
-        if not os.path.exists('bitewise_clean_dataset.csv'):
-            print("❌ CSV MISSING")
-            return pd.DataFrame(), None, None
-            
-        df = pd.read_csv('bitewise_clean_dataset.csv').fillna("N/A")
-        dish_emb = np.load('BiteWise_Dish_Embeddings.npy')
-        
-        with open('BiteWise_User_Embeddings.pkl', 'rb') as f:
-            user_emb = pickle.load(f)
-            if isinstance(user_emb, list): user_emb = np.array(user_emb)
-        
-        # סנכרון כפוי
-        min_l = min(len(df), len(dish_emb), len(user_emb))
-        print(f"✅ Data loaded. Syncing to {min_l} rows.")
-        return df.iloc[:min_l], dish_emb[:min_l], user_emb[:min_l]
-    except Exception as e:
-        print(f"❌ Loading error: {e}")
-        return pd.DataFrame(), None, None
-
-main_df, dish_embeddings, user_embeddings = load_colab_data()
+def load_data():
+    df = pd.read_csv('bitewise_clean_dataset.csv').fillna("N/A")
+    dish_emb = np.load('BiteWise_Dish_Embeddings.npy')
+    with open('BiteWise_User_Embeddings.pkl', 'rb') as f:
+        user_emb = pickle.load(f)
+        if isinstance(user_emb, list): user_emb = np.array(user_emb)
+    min_l = min(len(df), len(dish_emb), len(user_emb))
+    return df.iloc[:min_l], dish_emb[:min_l], user_emb[:min_l]
 
-# יצירת רשימות לבחירה (עם ערכי ברירת מחדל אם הטעינה נכשלה)
-origins = list(main_df['user_origin'].unique()) if not main_df.empty else ["Tel Aviv"]
-hobbies = list(main_df['user_hobbies'].unique()) if not main_df.empty else ["Photography"]
-styles = list(main_df['user_fashion_style'].unique()) if not main_df.empty else ["Modern/Minimalist"]
+main_df, dish_embeddings, user_embeddings = load_data()
 
-# --- 3. לוגיקת המלצה ---
-def run_discovery(query, origin, hobbies_val, style):
-    if main_df.empty or dish_embeddings is None:
-        return "<h3>Error: Database not loaded. Check your files.</h3>"
-        
+# --- 2. לוגיקת 3 ממליצים שונים ---
+def run_discovery(query, origin, hobbies, style):
     alpha = 0.7 
     q_vec = model.encode([str(query)])
-    u_dna_str = f"Origin: {origin}, Hobbies: {hobbies_val}, Style: {style}"
-    u_vec = model.encode([u_dna_str])
+    u_dna = f"Origin: {origin}, Hobbies: {hobbies}, Style: {style}"
+    u_vec = model.encode([u_dna])
     
     dish_sim = cosine_similarity(q_vec, dish_embeddings).flatten()
     user_sim = cosine_similarity(u_vec, user_embeddings).flatten()
@@ -64,70 +40,92 @@ def run_discovery(query, origin, hobbies_val, style):
     
     res = main_df.copy()
     res['score'] = final_scores
-    top_matches = res.sort_values('score', ascending=False).drop_duplicates('restaurant_name').head(3)
     
+    # בחירת 3 ממליצים שונים (מסננים לפי שם משתמש ב-CSV)
+    top_matches = []
+    used_users = set()
+    
+    sorted_res = res.sort_values('score', ascending=False)
+    
+    for _, row in sorted_res.iterrows():
+        # לוגיקה: אם המשתמש (הממליץ) עוד לא הופיע, נוסיף אותו
+        if row['user_name'] not in used_users:
+            top_matches.append(row)
+            used_users.add(row['user_name'])
+        if len(top_matches) == 3:
+            break
+
     html_output = ""
-    for idx, row in top_matches.iterrows():
+    for row in top_matches:
+        idx = row.name # האינדקס המקורי מה-CSV
         pct = f"{min(98.8, 88 + (row['score'] * 20)):.1f}%"
         
-        # חיפוש תמונה חכם (טיפול ב-IndexError)
-        pattern = f"extracted_images/**/dish_{idx}_*.jpg"
+        # חיפוש תמונה (שורש הבעיה: נתיב מלא)
+        pattern = os.path.join(IMAGE_DIR, "**", f"dish_{idx}_*.jpg")
         files = glob.glob(pattern, recursive=True)
         img_url = f"file/{files[0]}" if files else "https://via.placeholder.com/300?text=BiteWise"
         
         html_output += f"""
-        <div style="border: 2px solid #3E2723; border-radius: 15px; padding: 25px; margin-bottom: 25px; display: flex; gap: 25px; background: white; border-left: 12px solid #3E2723;">
-            <img src="{img_url}" style="width: 280px; height: 280px; object-fit: cover; border-radius: 12px;">
-            <div style="flex: 1;">
-                <div style="display: flex; justify-content: space-between;">
-                    <h2 style="margin: 0; font-family: Serif; color: #3E2723; font-size: 2.2em;">{row['dish_name']}</h2>
-                    <span style="background: #3E2723; color: white; padding: 5px 15px; border-radius: 20px; font-weight: bold; font-size: 0.8em;">{pct} MATCH</span>
-                </div>
-                <p style="color: #7F4F24; font-weight: 600; margin: 5px 0;">📍 {row['restaurant_name']}</p>
-                <div style="margin: 15px 0; display: flex; gap: 10px;">
-                    <span style="background: #F5F1EE; color: #5D4037; padding: 5px 12px; border-radius: 6px; font-weight: bold; font-size: 0.8em;">🍽️ {row['food_vibe']}</span>
-                    <span style="background: #F5F1EE; color: #5D4037; padding: 5px 12px; border-radius: 6px; font-weight: bold; font-size: 0.8em;">👥 {row['ideal_context']}</span>
-                    <span style="background: #F5F1EE; color: #5D4037; padding: 5px 12px; border-radius: 6px; font-weight: bold; font-size: 0.8em;">⭐ {row['rating']}</span>
-                </div>
-                <p style="font-style: italic; color: #4E342E; font-size: 1.15em; border-left: 3px solid #D7CCC8; padding-left: 15px;">"{row['taste_review']}"</p>
-                <div style="background: #FFF9F0; padding: 15px; border-radius: 10px; border: 1px solid #FFE0B2;">
-                    <strong>The Twin Bio:</strong> Your culinary twin from <b>{origin}</b> who shares your <b>{hobbies_val}</b> interests recommends this <b>{style}</b> experience.
+        <div style="border: 1px solid #D7CCC8; border-radius: 12px; padding: 20px; margin-bottom: 30px; background: white; box-shadow: 0 4px 15px rgba(0,0,0,0.05);">
+            <div style="background: #EFEBE9; padding: 10px 15px; border-radius: 8px; margin-bottom: 15px; border-right: 5px solid #3E2723;">
+                <p style="margin:0; color:#3E2723; font-size: 0.9em;">
+                    <b>Culinary Twin:</b> {row['user_name']} from {row['user_origin']} 
+                    (Loves {row['user_hobbies']} | Style: {row['user_fashion_style']})
+                </p>
+            </div>
+            <div style="display: flex; gap: 20px; align-items: start;">
+                <img src="{img_url}" style="width: 220px; height: 220px; object-fit: cover; border-radius: 8px;">
+                <div style="flex: 1;">
+                    <div style="display: flex; justify-content: space-between;">
+                        <h2 style="margin: 0; color: #3E2723; font-family: Serif;">{row['dish_name']}</h2>
+                        <span style="background: #3E2723; color: white; padding: 4px 12px; border-radius: 20px; font-size: 0.8em;">{pct} Match</span>
+                    </div>
+                    <p style="color: #7F4F24; margin: 5px 0; font-weight: 600;">@{row['restaurant_name']}</p>
+                    <p style="font-style: italic; color: #5D4037; margin: 15px 0;">"{row['taste_review']}"</p>
+                    <div style="display: flex; gap: 8px;">
+                        <span style="background: #F5F1EE; color: #7F4F24; padding: 3px 8px; border-radius: 4px; font-size: 0.75em;">{row['food_vibe']}</span>
+                        <span style="background: #F5F1EE; color: #7F4F24; padding: 3px 8px; border-radius: 4px; font-size: 0.75em;">⭐ {row['rating']}</span>
+                    </div>
                 </div>
             </div>
         </div>
         """
     return html_output
 
-# --- 4. הממשק ---
-with gr.Blocks() as demo:
-    gr.HTML("<h1 style='text-align: center; font-family: Serif; color: #3E2723; font-size: 3.5em;'>BiteWise</h1>")
+# --- 3. ממשק חדש (ללא כתום) ---
+custom_css = """
+.gradio-container { background-color: #FAF9F6 !important; }
+button.primary { background: #3E2723 !important; border: none !important; }
+button.primary:hover { background: #5D4037 !important; }
+.tabs { border-bottom: 2px solid #D7CCC8 !important; }
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML("<h1 style='text-align: center; color: #3E2723; font-family: Serif; font-size: 3em;'>BiteWise</h1>")
     
     with gr.Tabs() as tabs:
         with gr.Tab("01. YOUR DNA", id=0):
             with gr.Row():
-                u_name = gr.Textbox(label="User Name", placeholder="What's your name?")
-                u_origin = gr.Dropdown(origins, label="Origin", value=origins[0])
+                u_name = gr.Textbox(label="Your Name")
+                u_origin = gr.Dropdown(list(main_df['user_origin'].unique()), label="Origin", value="Tel Aviv")
             with gr.Row():
-                u_hobbies_input = gr.Dropdown(hobbies, label="Hobbies", value=hobbies[0])
-                u_style = gr.Dropdown(styles, label="Style", value=styles[0])
-            btn1 = gr.Button("SYNC MY DNA", variant="primary")
+                u_hobbies = gr.Dropdown(list(main_df['user_hobbies'].unique()), label="Hobbies")
+                u_style = gr.Dropdown(list(main_df['user_fashion_style'].unique()), label="Style")
+            btn1 = gr.Button("SYNC DNA", variant="primary")
             btn1.click(lambda: gr.update(selected=1), None, tabs)
 
         with gr.Tab("02. DISCOVERY", id=1):
-            with gr.Row():
-                q_in = gr.Textbox(label="What are you craving?", placeholder="e.g. Pasta", scale=4)
-                btn2 = gr.Button("FIND", variant="primary", scale=1)
+            q_in = gr.Textbox(label="What are you craving?", placeholder="e.g. Pasta")
+            btn2 = gr.Button("FIND MY MATCHES", variant="primary")
             out_html = gr.HTML()
-            btn2.click(run_discovery, [q_in, u_origin, u_hobbies_input, u_style], out_html)
+            btn2.click(run_discovery, [q_in, u_origin, u_hobbies, u_style], out_html)
 
         with gr.Tab("03. SHARE", id=2):
-            gr.Markdown("### 📸 Share your culinary discovery with the community")
-            s_dish = gr.Textbox(label="Dish Name")
-            s_rest = gr.Textbox(label="Restaurant")
-            s_rev = gr.Textbox(label="Your Review", lines=3)
-            s_btn = gr.Button("UPLOAD TO COMMUNITY")
-            s_msg = gr.Markdown()
-            s_btn.click(lambda: "### ✅ Thank you! Your review has been added to our database.", None, s_msg)
+            gr.Markdown("### Share a Review")
+            gr.Textbox(label="Dish")
+            gr.Textbox(label="Restaurant")
+            gr.Textbox(label="Review", lines=3)
+            gr.Button("UPLOAD", variant="primary")
 
-# הפעלה
-demo.launch(allowed_paths=["."])
+# הוספת allowed_paths לתיקיית התמונות שפרקנו
+demo.launch(allowed_paths=[IMAGE_DIR])