Update app.py

app.py

@@ -9,27 +9,22 @@ from sklearn.metrics.pairwise import cosine_similarity
 # ==========================================
 # 1. SETUP & DATA LOADING
 # ==========================================
-# We use the EXACT filenames you provided
 csv_path = "cleaned_dataset_10k.csv"
 pkl_path = "final_embeddings_10k.pkl"
 
-# Check if files exist to prevent crashing
 if not os.path.exists(csv_path) or not os.path.exists(pkl_path):
     raise FileNotFoundError(f"Error: Files not found. I see: {os.listdir('.')}")
 
 # Load Data
 df = pd.read_csv(csv_path)
-
-# Normalize column names (fixes 'Restaurant Name' vs 'restaurant_name' issues)
 df.columns = [c.strip().lower().replace(' ', '_') for c in df.columns]
 
-# Helper to find the right column names
+# Helper to find column names
 def get_col(candidates, default):
     for c in candidates:
         if c in df.columns: return c
     return default
 
-# Map your CSV columns to what the app needs
 col_name = get_col(['restaurant_name', 'name', 'place'], 'restaurant_name')
 col_rating = get_col(['rating', 'rating_score', 'stars'], 'rating')
 col_review = get_col(['review', 'review_content', 'review_content_clean'], 'review')
@@ -38,7 +33,6 @@ col_persona = get_col(['reviewer_persona', 'persona', 'type'], 'reviewer_persona
 # Load Embeddings
 with open(pkl_path, 'rb') as f:
     embedding_data = pickle.load(f)
-    # Handle if pickle is a dictionary or direct array
     if isinstance(embedding_data, dict) and 'embeddings' in embedding_data:
         dataset_embeddings = embedding_data['embeddings']
     else:
@@ -47,7 +41,7 @@ with open(pkl_path, 'rb') as f:
 # Load Model
 model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
 
-# Calculate Persona Profiles
+# Calculate Personas
 persona_profiles = {}
 if col_persona in df.columns:
     for persona in df[col_persona].unique():
@@ -64,27 +58,20 @@ else:
 # 2. LOGIC ENGINE
 # ==========================================
 def run_ven_engine(budget, dietary, company, purpose, noise):
-    # 1. Create a search query
     user_context = f"Searching for a {budget} experience, {dietary} friendly. Group: {company}. Occasion: {purpose}. Atmosphere: {noise}."
-    
-    # 2. Encode query
     query_vec = model.encode([user_context])
     
-    # 3. Find closest Persona
     similarities = {p: cosine_similarity(query_vec, v.reshape(1, -1))[0][0] for p, v in persona_profiles.items()}
     closest_persona = max(similarities, key=similarities.get)
     
-    # 4. Filter data
     if col_persona in df.columns:
         persona_df = df[df[col_persona] == closest_persona]
         if persona_df.empty: persona_df = df
     else:
         persona_df = df
     
-    # 5. Get top result
     top_match = persona_df.sort_values(by=col_rating, ascending=False).iloc[0]
     
-    # 6. Format Output
     match_pct = int(similarities[closest_persona] * 100)
     review_text = str(top_match[col_review])[:160] + "..."
     
@@ -119,14 +106,16 @@ with gr.Blocks(css=ven_css, title="VEN Project") as demo:
             in_budget = gr.Dropdown(["Budget-friendly", "Mid-range", "Premium"], label="Budget", value="Mid-range")
             in_diet = gr.Dropdown(["Anything", "Vegetarian", "Vegan", "Meat-lover"], label="Diet", value="Anything")
             in_company = gr.Radio(["Solo", "Date/Couple", "Friends", "Business"], label="With who?", value="Date/Couple")
-            in_purpose = gr.Dropdown(["Casual dinner", "Special occasion", "Quick bite"], label="Occasion", value="Casual dinner")
+            
+            # --- FIX IS HERE: Added "Professional meeting" to the list ---
+            in_purpose = gr.Dropdown(["Casual dinner", "Special occasion", "Quick bite", "Professional meeting"], label="Occasion", value="Casual dinner")
+            
             in_noise = gr.Radio(["Quiet/Intimate", "Moderate/Social", "Lively/Music"], label="Environment", value="Moderate/Social")
             btn = gr.Button("Find My Table", variant="primary")
 
         with gr.Column():
             output_ui = gr.HTML("<h4>Recommendation will appear here...</h4>")
 
-    # --- THIS IS STEP 7: ONE-CLICK STARTERS ---
     gr.Markdown("### 🚀 Quick Starters (One-Click)")
     gr.Examples(
         examples=[