Update flightprice.py

flightprice.py

@@ -52,7 +52,7 @@ def get_day_quarter(hour):
     else:
         return 'Night'
 
-# --- 2. MAPPINGS (For UI Display Only) ---
+# --- 2. Airport Codes (For Text Search) ---
 CITY_TO_CODE = {
     'Banglore': 'BLR', 'Bangalore': 'BLR',
     'Delhi': 'DEL', 'New Delhi': 'DEL',
@@ -94,19 +94,17 @@ if page == "💰 Price Prediction":
     st.markdown("### Enter Flight Details")
     
     # ---------------------------------------------------------
-    # INPUT SECTION (Reactive - No Form)
+    # INPUTS
     # ---------------------------------------------------------
     c1, c2 = st.columns(2)
     with c1:
         st.subheader("Flight Info")
         
-        # 1. Source Selection
+        # Source
         source = st.selectbox("Source", sorted(x_train['Source'].unique()))
-        st.success(f"🛫 Code: **{get_code(source)}**")
         
-        # 2. Destination Selection
+        # Destination
         destination = st.selectbox("Destination", sorted(x_train['Destination'].unique()))
-        st.error(f"🛬 Code: **{get_code(destination)}**")
         
         airline = st.selectbox("Airline", sorted(x_train['Airline'].unique()))
         
@@ -128,32 +126,36 @@ if page == "💰 Price Prediction":
     
     if 'Route' in x_train.columns:
         # ---------------------------------------------------------
-        # DOUBLE FILTER LOGIC (Source AND Destination)
+        # NEW FILTERING LOGIC
         # ---------------------------------------------------------
-        # We query the dataframe for rows matching BOTH selected Source and Destination
-        filtered_routes = x_train[
-            (x_train['Source'] == source) & 
-            (x_train['Destination'] == destination)
-        ]['Route'].unique()
+        # 1. Get ALL routes that start from this Source (using Source column)
+        all_source_routes = x_train[x_train['Source'] == source]['Route'].unique()
+        all_source_routes = sorted(all_source_routes.astype(str))
         
-        filtered_routes = sorted(filtered_routes.astype(str))
+        # 2. Filter: Does the route string contain the Destination (Code or Name)?
+        dest_code = get_code(destination)
         
-        if len(filtered_routes) > 0:
-            # If routes exist, user selects one
-            selected_route = st.selectbox("Select Route", filtered_routes)
+        valid_routes = []
+        for r in all_source_routes:
+            # Check if 'BOM' or 'MUMBAI' is in 'DEL -> BOM -> COK'
+            # We use .upper() for case insensitive check
+            if (dest_code in r.upper()) or (destination.upper() in r.upper()):
+                valid_routes.append(r)
+        
+        # 3. Display
+        if len(valid_routes) > 0:
+            selected_route = st.selectbox("Select Route", valid_routes)
             
-            # Lookup hidden features based on this valid route
+            # Lookup hidden features
             stops_val = route_lookup.get(selected_route, 0)
             
-            # Display Feedback
+            # Display Info
             c_a, c_b = st.columns(2)
             c_a.metric("Total Stops", stops_val)
-            c_b.success("✅ Valid Route Found")
+            c_b.success("✅ Valid Route")
             
         else:
-            # No routes connect these two cities in the training data
-            st.error(f"🚫 No flights found from **{source}** to **{destination}** in the database.")
-            st.info("Try changing the Destination or Source.")
+            st.error(f"No routes found starting at **{source}** that contain **{destination}** ({dest_code}).")
             selected_route = None
     else:
         st.error("Route column missing.")
@@ -161,7 +163,7 @@ if page == "💰 Price Prediction":
     st.markdown("<br>", unsafe_allow_html=True)
     
     # ---------------------------------------------------------
-    # PREDICTION BUTTON
+    # PREDICT
     # ---------------------------------------------------------
     if st.button("Predict Price", type="primary"):
         if selected_route:
@@ -177,12 +179,12 @@ if page == "💰 Price Prediction":
                 'Total_Stops': [stops_val]
             })
             
-            # Align Columns (Fill missing with 0)
+            # Align Columns
             final_input = pd.DataFrame(columns=x_train.columns)
             for col in x_train.columns:
                 final_input.loc[0, col] = input_df.iloc[0].get(col, 0)
                 
-            # Ensure Types
+            # Types
             for col in final_input.columns:
                 if x_train[col].dtype == 'object':
                     final_input[col] = final_input[col].astype(str)
@@ -190,11 +192,9 @@ if page == "💰 Price Prediction":
                     final_input[col] = pd.to_numeric(final_input[col])
 
             try:
-                # Predict
                 pred = model.predict(final_input)[0]
                 real_price = np.expm1(pred)
                 
-                # Show Result
                 st.markdown(f"""
                 <div style="background-color:#d4edda;padding:10px;border-radius:10px;border:2px solid #c3e6cb">
                     <h2 style="color:#155724;text-align:center;">🎫 Estimated Price: ₹ {real_price:,.2f}</h2>