Update app.py

app.py

@@ -9,45 +9,38 @@ import random
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# --- 1. חילוץ תמונות לתיקייה המדויקת שציינת ---
+# --- 1. הכנת תמונות (סנכרון תיקייה פנימית) ---
 IMAGE_DIR = "extracted_images"
-DISHES_SUBDIR = "Dishes_Images" # התיקייה שציינת
-
 if os.path.exists('images.zip'):
     with zipfile.ZipFile('images.zip', 'r') as zip_ref:
         zip_ref.extractall(IMAGE_DIR)
 
 def find_dish_image(idx):
-    # חיפוש ממוקד בתוך התיקייה Dishes_Images
-    path_to_search = os.path.join(IMAGE_DIR, DISHES_SUBDIR, f"dish_{idx}_*.jpg")
-    files = glob.glob(path_to_search)
-    
-    # גיבוי: אם לא מצא בנתיב המדויק, מחפש בכל מקום בתוך החילוץ
+    pattern = os.path.join(IMAGE_DIR, "Dishes_Images", f"dish_{idx}_*.jpg")
+    files = glob.glob(pattern)
     if not files:
         files = glob.glob(os.path.join(IMAGE_DIR, "**", f"dish_{idx}_*.jpg"), recursive=True)
-    
     if files:
         return f"file/{files[0]}"
     return "https://via.placeholder.com/400x400?text=BiteWise+Dish"
 
-# --- 2. טעינת נתונים (סנכרון ברזל למניעת בלבול מנות) ---
+# --- 2. טעינה וסנכרון ברזל ---
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-def load_and_sync_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))
     df = df.iloc[:min_l].reset_index(drop=True)
     return df, dish_emb[:min_l], user_emb[:min_l]
 
-main_df, dish_embeddings, user_embeddings = load_and_sync_data()
+main_df, dish_embeddings, user_embeddings = load_data()
 NAMES = ["James Miller", "Sarah Johnson", "Michael Brown", "Emily Davis", "Robert Wilson"]
 
-# --- 3. מנוע החיפוש עם הפיצ'רים המלאים מהדאטה ---
+# --- 3. מנוע החיפוש (Discovery) ---
 def run_discovery(query, origin, hobbies, style):
     q_vec = model.encode([str(query)])
     u_dna = f"Origin: {origin}, Hobbies: {hobbies}, Style: {style}"
@@ -58,8 +51,8 @@ def run_discovery(query, origin, hobbies, style):
     final_scores = (dish_sim * 0.7) + (user_sim * 0.3)
     
     res = main_df.copy()
-    res['score'] = final_scores
-    top_candidates = res.sort_values('score', ascending=False).head(10)
+    res['similarity_score'] = final_scores
+    top_candidates = res.sort_values('similarity_score', ascending=False).head(10)
 
     final_results = []
     seen_users = set()
@@ -72,22 +65,22 @@ def run_discovery(query, origin, hobbies, style):
 
     html_output = ""
     for original_idx, row, u_name in final_results:
-        pct = f"{min(99.0, 85 + (row['score'] * 15)):.1f}%"
+        pct = f"{min(99.0, 85 + (row['similarity_score'] * 15)):.1f}%"
         img_url = find_dish_image(original_idx)
         
         html_output += f"""
-        <div style="border: 1px solid #C4A484; border-radius: 4px; padding: 25px; margin-bottom: 30px; background: #FFF9F5; border-left: 10px solid #3E2723; display: flex; gap: 25px;">
-            <img src="{img_url}" style="width: 250px; height: 250px; object-fit: cover; border: 4px solid #FAF9F6; outline: 1px solid #D2B48C;">
+        <div style="border: 1px solid #C4A484; border-radius: 4px; padding: 25px; margin-bottom: 30px; background: #FFF9F5; border-left: 10px solid #3E2723; display: flex; gap: 25px; color: #3E2723; box-shadow: 5px 5px 15px rgba(0,0,0,0.05);">
+            <img src="{img_url}" style="width: 250px; height: 250px; object-fit: cover; border: 1px solid #D2B48C; padding: 4px; background: white;">
             <div style="flex: 1;">
                 <div style="display: flex; justify-content: space-between; align-items: center;">
-                    <h2 style="margin: 0; color: #3E2723; font-family: 'Playfair Display', serif; font-size: 2.2em;">{row['dish_name']}</h2>
+                    <h2 style="margin: 0; color: #3E2723; font-family: 'Playfair Display', serif; font-size: 2em;">{row['dish_name']}</h2>
                     <span style="background: #3E2723; color: white; padding: 2px 12px; font-weight: bold; border-radius: 20px; font-size: 0.85em;">{pct} MATCH</span>
                 </div>
                 <p style="color: #7F4F24; margin: 8px 0; font-family: 'Courier New', monospace; font-weight: bold;">📍 {row['restaurant_name']} | {row.get('cuisine_type', 'Gourmet')}</p>
-                <p style="font-family: 'Playfair Display', serif; font-style: italic; color: #4E342E; margin: 15px 0; font-size: 1.2em; line-height: 1.4;">"{row['taste_review']}"</p>
+                <p style="font-family: 'Playfair Display', serif; font-style: italic; color: #4E342E; margin: 15px 0; font-size: 1.1em; line-height: 1.4;">"{row['taste_review']}"</p>
                 
-                <div style="display: flex; gap: 10px; margin-bottom: 15px; flex-wrap: wrap;">
-                    <span style="background: #EEDDCC; color: #3E2723; padding: 4px 10px; font-size: 0.75em; border: 1px solid #D2B48C; font-family: 'Courier New', monospace;">💰 {row.get('price_range', '$$')}</span>
+                <div style="display: flex; gap: 10px; margin-bottom: 15px;">
+                    <span style="background: #EEDDCC; color: #3E2723; padding: 4px 10px; font-size: 0.75em; border: 1px solid #D2B48C; font-family: 'Courier New', monospace;">💰 PRICE: {row.get('price_range', '$$')}</span>
                     <span style="background: #EEDDCC; color: #3E2723; padding: 4px 10px; font-size: 0.75em; border: 1px solid #D2B48C; font-family: 'Courier New', monospace;">👥 BEST FOR: {row.get('best_for', 'Friends')}</span>
                 </div>
 
@@ -99,14 +92,13 @@ def run_discovery(query, origin, hobbies, style):
         """
     return html_output
 
-def archive_submission():
-    return "### Thank you for your review! \nYour discovery has been successfully shared with the BiteWise archive."
+def archive_msg(): return "### Thank you! \nYour review has been successfully shared with the BiteWise archive."
 
-# --- 4. הממשק המקורי (Vintage Soul) ---
+# --- 4. הממשק המעוצב (3 מסכים) ---
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Playfair+Display:ital,wght@0,700;1,400&display=swap');
-.gradio-container { background-color: #FDFCF8 !important; color: #3E2723; }
-button.primary { background: #3E2723 !important; border-radius: 0px !important; font-family: 'Courier New', monospace; text-transform: uppercase; letter-spacing: 2px; color: white !important; }
+.gradio-container { background-color: #FDFCF8 !important; }
+button.primary { background: #3E2723 !important; color: white !important; border-radius: 0px !important; font-family: 'Courier New', monospace; text-transform: uppercase; letter-spacing: 2px; }
 input, .dropdown { border-radius: 0px !important; border: 1px solid #D2B48C !important; background: white !important; }
 """
 
@@ -136,8 +128,8 @@ with gr.Blocks(css=custom_css) as demo:
                 gr.Textbox(label="DISH")
                 gr.Textbox(label="ESTABLISHMENT")
             gr.Textbox(label="REVIEW", lines=3)
-            submit_btn = gr.Button("SUBMIT & SHARE", variant="primary")
-            status_msg = gr.Markdown()
-            submit_btn.click(archive_submission, None, status_msg)
+            s_btn = gr.Button("SUBMIT & SHARE", variant="primary")
+            s_msg = gr.Markdown()
+            s_btn.click(archive_msg, None, s_msg)
 
 demo.launch(allowed_paths=["."])