Update app.py

app.py

@@ -5,6 +5,7 @@ import pickle
 from haversine import haversine, Unit
 from sklearn.preprocessing import LabelEncoder
 import matplotlib.pyplot as plt
+import os
 
 # --- Function to load data and model (cached for performance) ---
 def load_data_and_model():
@@ -14,7 +15,7 @@ def load_data_and_model():
         train_customers = pd.read_csv('train_customers.csv')
         train_locations = pd.read_csv('train_locations.csv')
         
-        # --- FIX: Convert 'id' column to string to avoid ValueError ---
+        # --- FIX: Ensure 'id' column is a string to avoid Gradio error ---
         vendors['id'] = vendors['id'].astype(str)
         
         with open('restaurant_recommender_model.pkl', 'rb') as f:
@@ -25,17 +26,19 @@ def load_data_and_model():
         vendors['vendor_category_encoded'] = le.fit_transform(vendors['vendor_category_en'])
         
         train_customers['dob'] = pd.to_datetime(train_customers['dob'], errors='coerce')
+        # Use a consistent timezone-aware approach for accurate age calculation
         train_customers['age'] = (pd.to_datetime('now', utc=True) - train_customers['dob'].dt.tz_localize('UTC')).dt.days / 365.25
         train_customers['age'] = train_customers['age'].fillna(train_customers['age'].median())
         
         return vendors, train_customers, train_locations, model
     except FileNotFoundError as e:
+        gr.Warning(f"File not found: {e}. Please ensure all data files and the model file are in the same directory.")
         return None, None, None, e
 
 # Load data and model once
 vendors, customers, locations, model = load_data_and_model()
 if vendors is None:
-    raise FileNotFoundError("Please ensure all data files and the model file are in the same directory.")
+    raise FileNotFoundError("Application cannot start. Required files are missing.")
 
 # --- Core Function for What-If Analysis ---
 def run_what_if_analysis(selected_vendor_id):
@@ -67,7 +70,7 @@ def run_what_if_analysis(selected_vendor_id):
     hypo_score = base_score
 
     # Create a simple bar chart
-    fig, ax = plt.subplots(facecolor='#FFFFFF') # Set background to white
+    fig, ax = plt.subplots(facecolor='#FFFFFF')
     scores = [base_score, hypo_score]
     labels = ['Current', 'Hypothetical']
     ax.bar(labels, scores, color=['#7FD2C8', '#FF9F9F'])
@@ -128,7 +131,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
         with gr.Column(scale=1):
             vendor_dropdown = gr.Dropdown(vendors['id'].unique(), label="Select Your Restaurant ID", info="Choose your restaurant from the list.")
         with gr.Column(scale=2):
-            gr.Image("https://i.imgur.com/5SgBv1S.png", container=False) # A food-themed image to enhance the UI
+            gr.Image("https://i.imgur.com/5SgBv1S.png", container=False)
 
     gr.Markdown("---")
     
@@ -136,8 +139,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
         gr.Markdown("### Simulate changes to your restaurant's details and see the predicted impact on your recommendation score.")
         
         with gr.Row():
-            rating_slider = gr.Slider(0, 5, value=4.0, step=0.1, label="Vendor Rating", info="What if your rating increased or decreased?")
-            delivery_charge_slider = gr.Slider(0, 20, value=5, step=1, label="Delivery Charge ($)", info="How does a price change affect your visibility?")
+            rating_slider = gr.Slider(0, 5, value=4.0, step=0.1, label="Vendor Rating", info="What if your rating increased or decreased?", interactive=False)
+            delivery_charge_slider = gr.Slider(0, 20, value=5, step=1, label="Delivery Charge ($)", info="How does a price change affect your visibility?", interactive=False)
             
         with gr.Row():
             btn_what_if = gr.Button("Run What-If Analysis", variant="primary")
@@ -148,7 +151,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
         
         gr.Plot(label="Score Comparison", elem_id="plot_what_if")
         
-        # We only pass the vendor ID, as the other sliders are just for visual demonstration
         btn_what_if.click(
             fn=run_what_if_analysis,
             inputs=[vendor_dropdown],