Update app.py

app.py

@@ -5,158 +5,182 @@ import os
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# --- 1. SETTINGS & MODEL ---
-# Using the exact SBERT model from our pipeline
+# ==============================================================================
+# 1. CORE ASSETS & MODEL (Directly from Colab Pipeline)
+# ==============================================================================
+# Initializing SBERT for semantic search
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-# File configurations
-MAIN_DB = 'bitewise_clean_dataset.csv'
-DISH_VECTORS = 'BiteWise_Dish_Embeddings.npy'
-USER_DB_PATH = 'BiteWise_User_Database.csv'
-REVIEWS_DB_PATH = 'BiteWise_User_Reviews.csv'
+# Loading static data and pre-computed embeddings
+main_df = pd.read_csv('bitewise_clean_dataset.csv')
+dish_embeddings = np.load('BiteWise_Dish_Embeddings.npy')
 
-# Pre-loading static assets
-df_main = pd.read_csv(MAIN_DB)
-dish_embeddings = np.load(DISH_VECTORS)
+# Persistent storage for user interaction data
+USER_PROFILES_FILE = "BiteWise_User_Database.csv"
+USER_REVIEWS_FILE = "BiteWise_User_Reviews.csv"
 
-# --- 2. THE STYLING (Bespoke Light Brown/Beige Theme) ---
+# ==============================================================================
+# 2. SOPHISTICATED MINIMALIST DESIGN (Earth Tones & High-End Typography)
+# ==============================================================================
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap');
-* { font-family: 'Inter', sans-serif !important; }
-.gradio-container { background-color: #F8F5F2 !important; }
-.main-header { color: #3D2B1F; text-align: center; font-weight: 600; font-size: 2.5em; margin-bottom: 5px; }
+
+/* Global Font & Background */
+* { font-family: 'Inter', sans-serif !important; color: #2C2C2C; }
+.gradio-container { background-color: #FAF9F6 !important; border: none !important; }
+
+/* Header Styling */
+.main-header { 
+    text-align: left; 
+    font-weight: 300; 
+    letter-spacing: -1px; 
+    font-size: 3.2em; 
+    margin-bottom: 0px; 
+    color: #1A1A1A;
+}
+
+/* Button Styling - Deep Earth Tone, No Gradients */
 .action-btn { 
-    background: #5D4037 !important; 
-    color: white !important; 
-    border: none !important; 
-    border-radius: 8px !important; 
+    background-color: #3E2723 !important; 
+    color: #FFFFFF !important; 
+    border-radius: 4px !important; 
+    border: none !important;
+    font-weight: 400 !important;
+    text-transform: uppercase;
+    letter-spacing: 1px;
+    transition: 0.2s ease-in-out;
+}
+.action-btn:hover { background-color: #1B1B1B !important; }
+
+/* Input Fields - Subtle & Sharp */
+input, textarea, .dropdown { 
+    border-radius: 2px !important; 
+    border: 1px solid #E0E0E0 !important; 
+    background-color: #FFFFFF !important;
+}
+
+/* Tab Navigation Styling */
+.tabs { border-bottom: 1px solid #E0E0E0 !important; }
+.tab-nav button.selected { 
+    border-bottom: 2px solid #3E2723 !important; 
+    background: transparent !important;
     font-weight: 600 !important;
-    padding: 10px 20px !important;
 }
-.action-btn:hover { background: #3D2B1F !important; }
-input, textarea, .dropdown, .slider { border-radius: 8px !important; border: 1px solid #D7CCC8 !important; }
 """
 
-# --- 3. CORE LOGIC (Part 4 Pipeline) ---
+# ==============================================================================
+# 3. ENGINE LOGIC (1:1 Transfer from Colab Part 4)
+# ==============================================================================
 
-def create_user_persona(name, age, cuisine, spice, adventure, diet, fav_dish, drink, frequency):
-    """Step 1: Building the user database record."""
+def create_user_profile(name, age, cuisine, spice, adventure, diet, fav_dish, drink, frequency):
+    """Saves user persona into the persistent database exactly as defined in Part 4."""
     if not name:
-        return "⚠️ Error: Full Name is required."
+        return "⚠️ Error: Please provide a name to initialize your profile."
     
-    user_data = {
-        'full_name': name, 'age_group': age, 'fav_cuisine': cuisine,
-        'spice_level': spice, 'adventure_level': adventure, 'dietary_restrictions': diet,
-        'favorite_dish': fav_dish, 'preferred_drink': drink, 'dining_frequency': frequency
+    user_record = {
+        'Name': name, 'Age': age, 'Favorite Cuisine': cuisine,
+        'Spice Level': spice, 'Adventure Level': adventure, 'Dietary Restrictions': diet,
+        'Favorite Dish': fav_dish, 'Preferred Drink': drink, 'Frequency': frequency
     }
     
-    # Save to persistent user storage
-    u_df = pd.DataFrame([user_data])
-    if os.path.exists(USER_DB_PATH):
-        pd.concat([pd.read_csv(USER_DB_PATH), u_df], ignore_index=True).to_csv(USER_DB_PATH, index=False)
+    u_df = pd.DataFrame([user_record])
+    if os.path.exists(USER_PROFILES_FILE):
+        pd.concat([pd.read_csv(USER_PROFILES_FILE), u_df], ignore_index=True).to_csv(USER_PROFILES_FILE, index=False)
     else:
-        u_df.to_csv(USER_DB_PATH, index=False)
-    
-    # Return exactly as phrased in Colab
-    return f"""### 👤 Profile Created Successfully!
-**Welcome, {name}!**
-
-**Your Culinary Context:**
-* **Preferences:** {cuisine} | **Spice:** {spice}/5 | **Adventure:** {adventure}/5
-* **Details:** {diet} | **Favorite:** {fav_dish} | **Vibe:** {drink}
-
-We have updated our records. You can now use the search engine to find dishes tailored to your taste."""
+        u_df.to_csv(USER_PROFILES_FILE, index=False)
+        
+    return f"**User Profile Created.** Welcome, {name}. Your preferences for '{cuisine}' and {diet} restrictions are now active."
 
-def run_search_engine(query):
-    """Step 2: Semantic search logic."""
+def search_engine(query):
+    """Executes semantic search using Cosine Similarity against the pre-computed dish space."""
     try:
         query_embedding = model.encode([query])
-        sims = cosine_similarity(query_embedding, dish_embeddings).flatten()
+        similarities = cosine_similarity(query_embedding, dish_embeddings).flatten()
         
-        # Temporary view for results
-        search_df = df_main.copy()
-        search_df['match_score'] = sims
-        top_3 = search_df.sort_values(by='match_score', ascending=False).head(3)
+        main_df['match_score'] = similarities
+        top_results = main_df.sort_values(by='match_score', ascending=False).head(3)
         
-        output = "### 🍴 Top Matches Found:\n\n"
-        for _, row in top_3.iterrows():
-            output += f"**{row['dish_name']}** at **{row['restaurant_name']}**\n"
-            output += f"Rating: {row['rating']} ⭐ | Review: {row['taste_review']}\n\n"
-            output += "---\n"
-        return output
+        results_output = "### RECOMMENDED SELECTIONS\n\n"
+        for _, row in top_results.iterrows():
+            results_output += f"**{row['dish_name']}** | {row['restaurant_name']}\n"
+            results_output += f"Score: {row['rating']} ⭐  —  *{row['taste_review']}*\n\n"
+            results_output += "---\n\n"
+        return results_output
     except Exception as e:
-        return f"Error: {e}"
+        return f"Engine Error: {e}"
 
-def submit_review(dish, rest, rate, taste, visual, user):
-    """Step 3: Contributing a new review."""
-    if not dish or not rest:
-        return "⚠️ Dish and Restaurant names are required.", dish, rest, rate, taste, visual, user
-    
-    review_data = {
-        'dish_name': dish, 'restaurant_name': rest, 'rating': rate,
-        'taste_review': taste, 'visual_description': visual, 'user_name': user
+def upload_review(dish_name, restaurant_name, rating, taste_review, visual_description, user_name):
+    """Submits new culinary data to the BiteWise ecosystem and resets input fields."""
+    if not dish_name or not restaurant_name:
+        return "⚠️ Validation Error: Dish and Restaurant names are mandatory.", dish_name, restaurant_name, rating, taste_review, visual_description, user_name
+
+    new_review = {
+        'dish_name': dish_name, 'restaurant_name': restaurant_name, 'rating': rating,
+        'taste_review': taste_review, 'visual_description': visual_description, 'user_name': user_name
     }
     
-    r_df = pd.DataFrame([review_data])
-    if os.path.exists(REVIEWS_DB_PATH):
-        pd.concat([pd.read_csv(REVIEWS_DB_PATH), r_df], ignore_index=True).to_csv(REVIEWS_DB_PATH, index=False)
+    r_df = pd.DataFrame([new_review])
+    if os.path.exists(USER_REVIEWS_FILE):
+        pd.concat([pd.read_csv(USER_REVIEWS_FILE), r_df], ignore_index=True).to_csv(USER_REVIEWS_FILE, index=False)
     else:
-        r_df.to_csv(REVIEWS_DB_PATH, index=False)
+        r_df.to_csv(USER_REVIEWS_FILE, index=False)
         
-    return f"✨ Thank you {user}! Your review for '{dish}' has been synced.", "", "", 4.5, "", "", ""
+    return f"Successfully synced review for '{dish_name}'.", "", "", 4.5, "", "", ""
 
-# --- 4. USER INTERFACE (Gradio) ---
+# ==============================================================================
+# 4. INTERFACE ARCHITECTURE
+# ==============================================================================
 
 with gr.Blocks(css=custom_css) as demo:
     gr.HTML("<h1 class='main-header'>BiteWise</h1>")
     
     with gr.Tabs():
-        # STEP 1: PERSONA
-        with gr.Tab("1. User Profile Setup"):
-            gr.Markdown("### 📋 Create your culinary DNA")
+        # Step 1: User Persona
+        with gr.Tab("01. PERSONA"):
+            gr.Markdown("Capture your culinary DNA to calibrate the engine.")
             with gr.Row():
-                u_name = gr.Textbox(label="Full Name")
-                u_age = gr.Dropdown(choices=["Under 18", "18-25", "26-40", "41-60", "60+"], label="Age Group")
+                u_name = gr.Textbox(label="FULL NAME")
+                u_age = gr.Dropdown(choices=["Under 18", "18-25", "26-40", "41-60", "60+"], label="AGE GROUP")
             with gr.Row():
-                u_cuisine = gr.Textbox(label="Favorite Cuisine Type", placeholder="e.g., Asian, Italian")
-                u_diet = gr.Textbox(label="Dietary Restrictions", placeholder="None, Vegan, etc.")
+                u_cuisine = gr.Textbox(label="FAVORITE CUISINE", placeholder="e.g. Modern Asian")
+                u_diet = gr.Textbox(label="DIETARY RESTRICTIONS", placeholder="e.g. None / Vegan")
             with gr.Row():
-                u_dish = gr.Textbox(label="One dish you love", placeholder="e.g., Truffle Pasta")
-                u_drink = gr.Textbox(label="Preferred Drink", placeholder="e.g., Matcha, Wine")
+                u_fav_dish = gr.Textbox(label="SIGNATURE DISH", placeholder="What do you always order?")
+                u_drink = gr.Textbox(label="DRINK PREFERENCE")
             with gr.Row():
-                u_freq = gr.Dropdown(choices=["Daily", "Weekly", "Rarely"], label="Dining Frequency")
-                u_spice = gr.Slider(1, 5, step=1, label="Spice Tolerance")
-                u_adv = gr.Slider(1, 5, step=1, label="Adventurousness")
+                u_freq = gr.Dropdown(choices=["Daily", "Weekly", "Monthly", "Rarely"], label="DINING FREQUENCY")
+                u_spice = gr.Slider(1, 5, step=1, label="SPICE TOLERANCE")
+                u_adv = gr.Slider(1, 5, step=1, label="ADVENTUROUSNESS")
             
-            btn_p = gr.Button("Create Profile", elem_classes="action-btn")
+            btn_p = gr.Button("INITIALIZE PROFILE", elem_classes="action-btn")
             out_p = gr.Markdown()
-            btn_p.click(create_user_persona, 
-                       inputs=[u_name, u_age, u_cuisine, u_spice, u_adv, u_diet, u_dish, u_drink, u_freq], 
+            btn_p.click(create_user_profile, 
+                       inputs=[u_name, u_age, u_cuisine, u_spice, u_adv, u_diet, u_fav_dish, u_drink, u_freq], 
                        outputs=out_p)
 
-        # STEP 2: SEARCH
-        with gr.Tab("2. Dish Search"):
-            gr.Markdown("### 🔍 Find your next flavor")
-            s_q = gr.Textbox(label="Describe your craving", placeholder="e.g., Something spicy and crunchy")
-            btn_s = gr.Button("Search Engine", elem_classes="action-btn")
+        # Step 2: Discovery
+        with gr.Tab("02. DISCOVERY"):
+            gr.Markdown("Search the engine for specific cravings or textures.")
+            s_q = gr.Textbox(label="CRAVING QUERY", placeholder="e.g. A rich, smoky pasta with earthy notes")
+            btn_s = gr.Button("EXECUTE SEARCH", elem_classes="action-btn")
             out_s = gr.Markdown()
-            btn_s.click(run_search_engine, inputs=s_q, outputs=out_s)
+            btn_s.click(search_engine, inputs=s_q, outputs=out_s)
 
-        # STEP 3: CONTRIBUTION
-        with gr.Tab("3. Upload Review"):
-            gr.Markdown("### ✍️ Share your experience")
+        # Step 3: Contribution
+        with gr.Tab("03. CONTRIBUTION"):
+            gr.Markdown("Sync new data points to the BiteWise database.")
             with gr.Row():
-                in_u = gr.Textbox(label="Reviewer Name")
-                in_d = gr.Textbox(label="Dish Name")
-                in_r = gr.Textbox(label="Restaurant")
+                in_u = gr.Textbox(label="REVIEWER NAME")
+                in_d = gr.Textbox(label="DISH NAME")
+                in_r = gr.Textbox(label="RESTAURANT NAME")
             with gr.Row():
-                in_rate = gr.Slider(1, 5, value=4.5, step=0.1, label="Rating")
-                in_t = gr.Textbox(label="Taste Notes")
-                in_v = gr.Textbox(label="Visual Description")
+                in_rate = gr.Slider(1, 5, value=4.5, step=0.1, label="RATING")
+                in_t = gr.Textbox(label="TASTE NOTES")
+                in_v = gr.Textbox(label="VISUAL PRESENTATION")
             
-            btn_r = gr.Button("Upload Review", elem_classes="action-btn")
+            btn_r = gr.Button("SYNC DATA", elem_classes="action-btn")
             out_r = gr.Markdown()
-            btn_r.click(submit_review, 
+            btn_r.click(upload_review, 
                        inputs=[in_d, in_r, in_rate, in_t, in_v, in_u], 
                        outputs=[out_r, in_d, in_r, in_rate, in_t, in_v, in_u])