Update flightprice.py

flightprice.py

@@ -1,230 +1,220 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
-
-# Set Page Config
-st.set_page_config(page_title="Flight Price System", layout="wide", page_icon="✈️")
-
-# --- 1. Helper Functions ---
-@st.cache_data
-def load_data():
-    try:
-        x_train = pd.read_parquet('x_train.parquet')
-        x_test = pd.read_parquet('x_test.parquet')
-        y_test = pd.read_parquet('y_test.parquet')
-        
-        # CLEANING: Strip whitespace
-        for col in ['Airline', 'Source', 'Destination', 'Route']:
-            if col in x_train.columns:
-                x_train[col] = x_train[col].astype(str).str.strip()
-                
-        return x_train, x_test, y_test
-    except Exception as e:
-        st.error(f"Error loading data files: {e}")
-        return None, None, None
-
-@st.cache_resource
-def load_model():
-    try:
-        return joblib.load('AirFlights_HistBoost_model.pkl')
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        return None
-
-def get_day_name(day_num, month_name):
-    try:
-        date_str = f"{int(day_num)}-{month_name}-2019"
-        return pd.to_datetime(date_str, format="%d-%B-%Y").day_name()
-    except:
-        return "Monday"
-
-def get_day_quarter(hour):
-    if 5 <= hour < 12:
-        return 'Morning'
-    elif 12 <= hour < 17:
-        return 'Afternoon'
-    elif 17 <= hour < 21:
-        return 'Evening'
-    else:
-        return 'Night'
-
-# --- 2. EXHAUSTIVE AIRPORT CODE MAPPING ---
-CITY_TO_CODE = {
-    'Banglore': 'BLR',
-    'Bangalore': 'BLR',
-    'Delhi': 'DEL',
-    'New Delhi': 'DEL',
-    'Kolkata': 'CCU',
-    'Calcutta': 'CCU',
-    'Hyderabad': 'HYD',
-    'Chennai': 'MAA',
-    'Madras': 'MAA',
-    'Mumbai': 'BOM',
-    'Bombay': 'BOM',
-    'Cochin': 'COK',
-    'Kochi': 'COK',
-    'Pune': 'PNQ',
-    'Goa': 'GOI',
-    'Jaipur': 'JAI',
-    'Lucknow': 'LKO',
-    'Patna': 'PAT',
-    'Varanasi': 'VNS',
-    'Bhubaneswar': 'BBI',
-    'Nagpur': 'NAG',
-    'Trivandrum': 'TRV'
-}
-
-def get_code(city):
-    clean_city = city.strip()
-    return CITY_TO_CODE.get(clean_city, clean_city[:3].upper())
-
-def is_route_valid(route_str, source_code, dest_code):
-    if pd.isna(route_str) or route_str == 'nan':
-        return False
-    route_upper = route_str.upper()
-    parts = route_upper.replace("→", " ").replace("->", " ").split()
-    if not parts:
-        return False
-    first_stop = parts[0]
-    return (first_stop == source_code) and (dest_code in parts)
-
-# Load Data
-x_train, x_test, y_test = load_data()
-model = load_model()
-
-if x_train is None or model is None:
-    st.stop()
-
-if isinstance(y_test, pd.DataFrame):
-    y_test_series = y_test.iloc[:, 0]
-else:
-    y_test_series = y_test
-
-# Build Lookup
-route_lookup = {}
-if 'Route' in x_train.columns and 'Total_Stops' in x_train.columns:
-    temp = x_train[['Route', 'Total_Stops']].drop_duplicates(subset=['Route'])
-    route_lookup = temp.set_index('Route')['Total_Stops'].to_dict()
-
-# --- 3. App Layout ---
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to", ["💰 Price Prediction", "📊 Model Evaluation"])
-
-if page == "💰 Price Prediction":
-    st.title("✈️ Flight Price Prediction")
-    st.markdown("### Enter Flight Details")
-    
-    # REMOVED st.form HERE so inputs update instantly!
-    
-    c1, c2 = st.columns(2)
-    with c1:
-        st.subheader("Flight Info")
-        
-        # Source (Updates instantly now)
-        source = st.selectbox("Source", sorted(x_train['Source'].unique()))
-        src_code = get_code(source)
-        st.success(f"🛫 Source Code: **{src_code}**") 
-        
-        # Destination (Updates instantly now)
-        destination = st.selectbox("Destination", sorted(x_train['Destination'].unique()))
-        dest_code = get_code(destination)
-        st.error(f"🛬 Destination Code: **{dest_code}**") 
-        
-        airline = st.selectbox("Airline", sorted(x_train['Airline'].unique()))
-        
-    with c2:
-        st.subheader("Date & Time")
-        if 'Month' in x_train.columns:
-            months = sorted(x_train['Month'].unique())
-        else:
-            months = ['March', 'April', 'May', 'June', 'September', 'December']
-        month = st.selectbox("Month", months)
-        day_number = st.number_input("Day Number", 1, 31, 1)
-        dept_hour = st.number_input("Departure Hour", 0, 23, 10)
-
-    st.markdown("---")
-    st.subheader("Route Selection")
-    
-    selected_route = None
-    stops_val = 0
-    
-    if 'Route' in x_train.columns:
-        # 1. Get all routes
-        all_routes = sorted(x_train['Route'].unique().astype(str))
-        
-        # 2. FILTER: Show only routes starting with the exact Source Code
-        # Now this runs immediately when you change 'Source' above
-        filtered_routes = []
-        for r in all_routes:
-            parts = r.upper().replace("→", " ").replace("->", " ").split()
-            if parts and parts[0] == src_code:
-                filtered_routes.append(r)
-        
-        if filtered_routes:
-            selected_route_raw = st.selectbox("Select Route", filtered_routes)
-            
-            # 3. VALIDATE
-            if is_route_valid(selected_route_raw, src_code, dest_code):
-                selected_route = selected_route_raw
-                stops_val = route_lookup.get(selected_route, 0)
-                st.metric("Total Stops", stops_val)
-                st.success("✅ Valid Route")
-            else:
-                st.warning(f"⚠️ **{selected_route_raw}** starts at **{src_code}** but does not reach **{dest_code}**. Please check your destination.")
-                selected_route = None
-        else:
-            st.error(f"No routes found starting with code **{src_code}**.")
-    else:
-        st.error("Route column missing.")
-
-    # We only use a button for the final prediction calculation
-    if st.button("Predict Price", type="primary"):
-        if selected_route:
-            # Prepare Input
-            day_name = get_day_name(day_number, month)
-            quarter = get_day_quarter(dept_hour)
-            
-            input_df = pd.DataFrame({
-                'Airline': [airline], 'Source': [source], 'Destination': [destination],
-                'Month': [month], 'Route': [selected_route],
-                'Day_number': [day_number], 'Dept_hour': [dept_hour],
-                'Day': [day_name], 'Dept_Day_Quarter': [quarter],
-                'Total_Stops': [stops_val]
-            })
-            
-            # Align Cols
-            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)
-                
-            # Types
-            for col in final_input.columns:
-                if x_train[col].dtype == 'object':
-                    final_input[col] = final_input[col].astype(str)
-                else:
-                    final_input[col] = pd.to_numeric(final_input[col])
-
-            try:
-                pred = model.predict(final_input)[0]
-                st.success(f"### Estimated Price: ₹ {np.expm1(pred):,.2f}")
-            except Exception as e:
-                st.error(f"Error: {e}")
-        else:
-            st.error("Please select a valid route.")
-
-elif page == "📊 Model Evaluation":
-    st.title("Model Evaluation")
-    if st.button("Evaluate"):
-        with st.spinner("Running..."):
-            y_pred = model.predict(x_test)
-            r2 = r2_score(y_test_series, y_pred)
-            st.metric("R2 Score", f"{r2:.4f}")
-            
-            fig, ax = plt.subplots()
-            sns.scatterplot(x=np.expm1(y_test_series), y=np.expm1(y_pred), ax=ax)
-            ax.plot([0, 80000], [0, 80000], 'r--')
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+
+# Set Page Config
+st.set_page_config(page_title="Flight Price System", layout="wide", page_icon="✈️")
+
+# --- 1. Helper Functions ---
+@st.cache_data
+def load_data():
+    try:
+        x_train = pd.read_parquet('x_train.parquet')
+        x_test = pd.read_parquet('x_test.parquet')
+        y_test = pd.read_parquet('y_test.parquet')
+        
+        # CLEANING: Strip whitespace
+        for col in ['Airline', 'Source', 'Destination', 'Route']:
+            if col in x_train.columns:
+                x_train[col] = x_train[col].astype(str).str.strip()
+                
+        return x_train, x_test, y_test
+    except Exception as e:
+        st.error(f"Error loading data files: {e}")
+        return None, None, None
+
+@st.cache_resource
+def load_model():
+    try:
+        return joblib.load('AirFlights_HistBoost_model.pkl')
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+def get_day_name(day_num, month_name):
+    try:
+        date_str = f"{int(day_num)}-{month_name}-2019"
+        return pd.to_datetime(date_str, format="%d-%B-%Y").day_name()
+    except:
+        return "Monday"
+
+def get_day_quarter(hour):
+    if 5 <= hour < 12:
+        return 'Morning'
+    elif 12 <= hour < 17:
+        return 'Afternoon'
+    elif 17 <= hour < 21:
+        return 'Evening'
+    else:
+        return 'Night'
+
+# --- 2. MAPPING FOR VALIDATION ONLY ---
+# We use this to help check if the Destination is in the Route string
+CITY_TO_CODE = {
+    'Banglore': 'BLR', 'Bangalore': 'BLR',
+    'Delhi': 'DEL', 'New Delhi': 'DEL',
+    'Kolkata': 'CCU', 'Calcutta': 'CCU',
+    'Hyderabad': 'HYD', 'Chennai': 'MAA', 'Madras': 'MAA',
+    'Mumbai': 'BOM', 'Bombay': 'BOM',
+    'Cochin': 'COK', 'Kochi': 'COK',
+    'Pune': 'PNQ', 'Goa': 'GOI', 'Jaipur': 'JAI',
+    'Lucknow': 'LKO', 'Patna': 'PAT', 'Varanasi': 'VNS'
+}
+
+def get_code(city):
+    return CITY_TO_CODE.get(city.strip(), city[:3].upper())
+
+def is_route_valid(route_str, destination):
+    """
+    Checks if the Destination (Name or Code) appears in the Route string.
+    """
+    if pd.isna(route_str) or route_str == 'nan':
+        return False
+        
+    route_upper = route_str.upper()
+    dest_name_upper = destination.upper()
+    dest_code = get_code(destination)
+    
+    # Check if code (e.g., 'COK') or name (e.g., 'COCHIN') is in string
+    return (dest_code in route_upper) or (dest_name_upper in route_upper)
+
+# Load Data
+x_train, x_test, y_test = load_data()
+model = load_model()
+
+if x_train is None or model is None:
+    st.stop()
+
+if isinstance(y_test, pd.DataFrame):
+    y_test_series = y_test.iloc[:, 0]
+else:
+    y_test_series = y_test
+
+# Build Lookup for Stops
+route_lookup = {}
+if 'Route' in x_train.columns and 'Total_Stops' in x_train.columns:
+    temp = x_train[['Route', 'Total_Stops']].drop_duplicates(subset=['Route'])
+    route_lookup = temp.set_index('Route')['Total_Stops'].to_dict()
+
+# --- 3. App Layout ---
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["💰 Price Prediction", "📊 Model Evaluation"])
+
+if page == "💰 Price Prediction":
+    st.title("✈️ Flight Price Prediction")
+    st.markdown("### Enter Flight Details")
+    
+    c1, c2 = st.columns(2)
+    with c1:
+        st.subheader("Flight Info")
+        
+        # Source (Updates instantly)
+        source = st.selectbox("Source", sorted(x_train['Source'].unique()))
+        
+        # Destination (Updates instantly)
+        destination = st.selectbox("Destination", sorted(x_train['Destination'].unique()))
+        
+        airline = st.selectbox("Airline", sorted(x_train['Airline'].unique()))
+        
+    with c2:
+        st.subheader("Date & Time")
+        if 'Month' in x_train.columns:
+            months = sorted(x_train['Month'].unique())
+        else:
+            months = ['March', 'April', 'May', 'June', 'September', 'December']
+        month = st.selectbox("Month", months)
+        day_number = st.number_input("Day Number", 1, 31, 1)
+        dept_hour = st.number_input("Departure Hour", 0, 23, 10)
+
+    st.markdown("---")
+    st.subheader("Route Selection")
+    
+    selected_route = None
+    stops_val = 0
+    
+    if 'Route' in x_train.columns:
+        # --- 1. SMART FILTERING ---
+        # Instead of guessing codes, we ask the DataFrame:
+        # "Give me all routes that exist for this specific Source"
+        valid_source_routes = x_train[x_train['Source'] == source]['Route'].unique()
+        valid_source_routes = sorted(valid_source_routes.astype(str))
+        
+        if len(valid_source_routes) > 0:
+            selected_route_raw = st.selectbox("Select Route", valid_source_routes)
+            
+            # --- 2. VALIDATION ---
+            # Check if the route actually contains the destination
+            if is_route_valid(selected_route_raw, destination):
+                selected_route = selected_route_raw
+                stops_val = route_lookup.get(selected_route, 0)
+                st.metric("Total Stops", stops_val)
+                st.success("✅ Valid Route")
+            else:
+                # Warning if user picks a route that doesn't go to their destination
+                st.warning(f"⚠️ **{selected_route_raw}** does not seem to contain **{destination}** ({get_code(destination)}). Please confirm if this is correct.")
+                # We allow them to proceed but with a warning, or you can block it:
+                # selected_route = None 
+                selected_route = selected_route_raw 
+                stops_val = route_lookup.get(selected_route, 0)
+                
+        else:
+            st.error(f"No routes found in database for Source: {source}")
+    else:
+        st.error("Route column missing.")
+
+    st.markdown("<br>", unsafe_allow_html=True)
+    
+    if st.button("Predict Price", type="primary"):
+        if selected_route:
+            # Prepare Input
+            day_name = get_day_name(day_number, month)
+            quarter = get_day_quarter(dept_hour)
+            
+            input_df = pd.DataFrame({
+                'Airline': [airline], 'Source': [source], 'Destination': [destination],
+                'Month': [month], 'Route': [selected_route],
+                'Day_number': [day_number], 'Dept_hour': [dept_hour],
+                'Day': [day_name], 'Dept_Day_Quarter': [quarter],
+                'Total_Stops': [stops_val]
+            })
+            
+            # Align Cols
+            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)
+                
+            # Types
+            for col in final_input.columns:
+                if x_train[col].dtype == 'object':
+                    final_input[col] = final_input[col].astype(str)
+                else:
+                    final_input[col] = pd.to_numeric(final_input[col])
+
+            try:
+                pred = model.predict(final_input)[0]
+                st.success(f"### Estimated Price: ₹ {np.expm1(pred):,.2f}")
+            except Exception as e:
+                st.error(f"Error: {e}")
+        else:
+            st.error("Please select a valid route.")
+
+elif page == "📊 Model Evaluation":
+    st.title("Model Evaluation")
+    if st.button("Evaluate"):
+        with st.spinner("Running..."):
+            y_pred = model.predict(x_test)
+            r2 = r2_score(y_test_series, y_pred)
+            st.metric("R2 Score", f"{r2:.4f}")
+            
+            fig, ax = plt.subplots(figsize=(10, 6))
+            sns.scatterplot(x=np.expm1(y_test_series), y=np.expm1(y_pred), ax=ax, alpha=0.6)
+            min_val = min(np.expm1(y_test_series).min(), np.expm1(y_pred).min())
+            max_val = max(np.expm1(y_test_series).max(), np.expm1(y_pred).max())
+            ax.plot([min_val, max_val], [min_val, max_val], 'r--', lw=2)
+            ax.set_xlabel("Actual Price")
+            ax.set_ylabel("Predicted Price")
             st.pyplot(fig)