Update app.py

app.py

@@ -13,7 +13,7 @@ df["model"] = df["model"].str.strip()
 df["features"] = df["features"].astype(str).str.lower()
 
 # Control long responses
-MAX_TOTAL_CHARACTERS = 3000
+MAX_TOTAL_CHARACTERS = 5000
 
 def extract_numbers(text: str) -> List[float]:
     """Extract all numbers from text"""
@@ -160,8 +160,12 @@ def handle_specific_questions(query: str) -> str:
     
     return ""
 
-def format_car_details(car: Dict, show_features: bool = True) -> str:
+def format_car_details(car: Dict, show_features: bool = True, compact: bool = False) -> str:
     """Format car details for display"""
+    if compact:
+        # Compact format for showing many cars
+        return f"🚗 {car['brand'].title()} {car['model']} | ₹{car['price_lakh']}L | {car['mileage_kmpl']} kmpl | {car['engine']}"
+    
     features_text = ""
     if show_features and 'features' in car:
         features = car['features'][:200] + "..." if len(car['features']) > 200 else car['features']
@@ -243,17 +247,45 @@ def answer_question(query: str) -> str:
         return "❌ No matching cars found for your query. Try adjusting your requirements!"
     
     response = ""
-    if len(filtered_df) > 1:
-        response += f"Found {len(filtered_df)} matching cars:\n\n"
-    
-    for _, row in filtered_df.head(5).iterrows():  # Show top 5 results
-        entry = format_car_details(row.to_dict()) + "\n"
-        if len(response + entry) > MAX_TOTAL_CHARACTERS:
-            break
-        response += entry
     
-    if len(filtered_df) > 5:
-        response += f"\n... and {len(filtered_df) - 5} more cars match your criteria."
+    # Check if it's a simple brand query (show all cars from that brand)
+    is_simple_brand_query = (
+        len(brands) == 1 and 
+        not models and 
+        min_price is None and max_price is None and 
+        min_mileage is None and max_mileage is None and 
+        not features and
+        not any(word in query.lower() for word in ['cheap', 'expensive', 'best', 'compare', 'vs'])
+    )
+    
+    if is_simple_brand_query and len(filtered_df) > 3:
+        # Show all cars for simple brand queries in compact format
+        response += f"🏷️ All {brands[0].title()} cars in our database ({len(filtered_df)} models):\n\n"
+        for _, row in filtered_df.iterrows():
+            response += format_car_details(row.to_dict(), show_features=False, compact=True) + "\n"
+        
+        # Add a summary
+        avg_price = filtered_df['price_lakh'].mean()
+        avg_mileage = filtered_df['mileage_kmpl'].mean()
+        price_range = f"₹{filtered_df['price_lakh'].min()}-{filtered_df['price_lakh'].max()}L"
+        response += f"\n📊 Summary: Average price: ₹{avg_price:.1f}L | Average mileage: {avg_mileage:.1f} kmpl | Price range: {price_range}"
+        
+    else:
+        # Regular detailed format for filtered results
+        if len(filtered_df) > 1:
+            response += f"Found {len(filtered_df)} matching cars:\n\n"
+        
+        # Determine how many cars to show in detail
+        max_detailed_cars = 8 if len(filtered_df) <= 10 else 5
+        
+        for _, row in filtered_df.head(max_detailed_cars).iterrows():
+            entry = format_car_details(row.to_dict()) + "\n"
+            if len(response + entry) > MAX_TOTAL_CHARACTERS:
+                break
+            response += entry
+        
+        if len(filtered_df) > max_detailed_cars:
+            response += f"\n... and {len(filtered_df) - max_detailed_cars} more cars match your criteria."
     
     return response.strip()