Update app.py

app.py

@@ -1,122 +1,122 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Jul 29 08:14:05 2024
-
-@author: sanath
-"""
-
-import pickle
-import streamlit as st
-from streamlit_option_menu import option_menu
-import numpy as np
-import pandas as pd
-import pyarrow
-import xgboost as xgb
-from sklearn.preprocessing import LabelEncoder, StandardScaler
-
-# Print versions to ensure everything is correctly installed
-print(f"pyarrow version: {pyarrow.__version__}")
-print(f"xgboost version: {xgb.__version__}")
-
-
-
-house_model = pickle.load(open('C:/Users/sanath/Desktop/multiple disease prediction system/saved_models/HousePricePrediction.pkl','rb'))
-car_model = pickle.load(open('C:/Users/sanath/Desktop/multiple disease prediction system/saved_models/CarPricePrediction.pkl','rb'))
-bike_model = pickle.load(open('C:/Users/sanath/Desktop/multiple disease prediction system/saved_models/bikePrediction_model.pkl','rb'))
-
-
-
-with st.sidebar:
-    
-    selected = option_menu('Multiple Price Prediction System', 
-                           ['House Price Prediction',
-                            'Car Price Prediction',
-                            'Bike Price Prediction'],
-                           
-                           icons = ['house','car-front-fill','bicycle'],
-                           default_index=0)
-    
-
-if selected == 'House Price Prediction':
-    st.title('House Price Prediction')
-    
-    #input
-    medinc = st.text_input('The median income of the block group (in tens of thousands of dollars)')
-    HouseAge = st.text_input('The median age of the houses in the block group (in years)')
-    AveRooms = st.text_input('The average number of rooms per household in the block group')
-    AveBedrms = st.text_input(' the average number of bedrooms per household in a given block group.')
-    Population = st.text_input('The total number of people living in a block group.')
-    AveOccup = st.text_input('Average number of people per household')
-    Latitude = st.text_input('Latitude of the block group')
-    Longitude = st.text_input('Longitude of the block group')
-    
-    #output
-    
-    # creating a button for prediction
-    
-    if st.button('predict house price'):
-    # Convert input data to floats
-      input_data = np.array([[float(medinc), float(HouseAge), float(AveRooms), float(AveBedrms), 
-                            float(Population), float(AveOccup), float(Latitude), float(Longitude)]])
-      price_prediction = house_model.predict(input_data)
-    
-      st.success(f'Predicted house price: ${price_prediction[0]:,.2f}')
-
-        
-
-
-elif selected == 'Car Price Prediction':
-    st.title('Car Price Prediction')
-    
-    name = st.text_input('car name')
-    company = st.text_input('Company name')
-    year = st.text_input('year')
-    kms_driven = st.text_input('kilometers driven')
-    fuel_type = st.text_input('Fuel Type')
-    
-    if st.button('Predict Car Price'):
-        # Handle categorical variables with LabelEncoder
-        label_encoder = LabelEncoder()
-        name_encoded = label_encoder.fit_transform([name])[0]
-        company_encoded = label_encoder.fit_transform([company])[0]
-        fuel_type_encoded = label_encoder.fit_transform([fuel_type])[0]
-        
-        # Create input array
-        inputs = np.array([[name_encoded, company_encoded, int(year), float(kms_driven), fuel_type_encoded]])
-        
-        # Scale the input
-        scaler = StandardScaler()
-        scaled_car = scaler.fit_transform(inputs)
-        
-        # Predict car price
-        car_price_prediction = car_model.predict(scaled_car)
-        st.success(f'Predicted car price: ${car_price_prediction[0]:,.2f}')
-
-elif selected == 'Bike Price Prediction':
-    st.title('Bike Price Prediction')
-    
-    bike_name = st.text_input('Name of the bike')
-    kms_driven = st.text_input('Kilometers driven')
-    owner = st.text_input('Owner')
-    age = st.text_input('Age of the bike')
-    power = st.text_input('Power of the bike')
-    brand = st.text_input('Bike brand')
-    
-    if st.button('Predict Bike Price'):
-        # Handle categorical variables with LabelEncoder
-        label_encoder = LabelEncoder()
-        bike_name_encoded = label_encoder.fit_transform([bike_name])[0]
-        owner_encoded = label_encoder.fit_transform([owner])[0]
-        brand_encoded = label_encoder.fit_transform([brand])[0]
-        
-        # Create input array
-        inputs = np.array([[bike_name_encoded, float(kms_driven), owner_encoded, int(age), float(power), brand_encoded]])
-        
-        # Scale the input
-        scaler = StandardScaler()
-        scaled_bike = scaler.fit_transform(inputs)
-        
-        # Predict bike price
-        bike_price_prediction = bike_model.predict(scaled_bike)
-        st.success(f'Predicted bike price: ${bike_price_prediction[0]:,.2f}')
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 29 08:14:05 2024
+
+@author: sanath
+"""
+
+import pickle
+import streamlit as st
+from streamlit_option_menu import option_menu
+import numpy as np
+import pandas as pd
+import pyarrow
+import xgboost as xgb
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+
+# Print versions to ensure everything is correctly installed
+print(f"pyarrow version: {pyarrow.__version__}")
+print(f"xgboost version: {xgb.__version__}")
+
+
+
+house_model = pickle.load(open('HousePricePrediction.pkl','rb'))
+car_model = pickle.load(open('CarPricePrediction.pkl','rb'))
+bike_model = pickle.load(open('bikePrediction_model.pkl','rb'))
+
+
+
+with st.sidebar:
+    
+    selected = option_menu('Multiple Price Prediction System', 
+                           ['House Price Prediction',
+                            'Car Price Prediction',
+                            'Bike Price Prediction'],
+                           
+                           icons = ['house','car-front-fill','bicycle'],
+                           default_index=0)
+    
+
+if selected == 'House Price Prediction':
+    st.title('House Price Prediction')
+    
+    #input
+    medinc = st.text_input('The median income of the block group (in tens of thousands of dollars)')
+    HouseAge = st.text_input('The median age of the houses in the block group (in years)')
+    AveRooms = st.text_input('The average number of rooms per household in the block group')
+    AveBedrms = st.text_input(' the average number of bedrooms per household in a given block group.')
+    Population = st.text_input('The total number of people living in a block group.')
+    AveOccup = st.text_input('Average number of people per household')
+    Latitude = st.text_input('Latitude of the block group')
+    Longitude = st.text_input('Longitude of the block group')
+    
+    #output
+    
+    # creating a button for prediction
+    
+    if st.button('predict house price'):
+    # Convert input data to floats
+      input_data = np.array([[float(medinc), float(HouseAge), float(AveRooms), float(AveBedrms), 
+                            float(Population), float(AveOccup), float(Latitude), float(Longitude)]])
+      price_prediction = house_model.predict(input_data)
+    
+      st.success(f'Predicted house price: ${price_prediction[0]:,.2f}')
+
+        
+
+
+elif selected == 'Car Price Prediction':
+    st.title('Car Price Prediction')
+    
+    name = st.text_input('car name')
+    company = st.text_input('Company name')
+    year = st.text_input('year')
+    kms_driven = st.text_input('kilometers driven')
+    fuel_type = st.text_input('Fuel Type')
+    
+    if st.button('Predict Car Price'):
+        # Handle categorical variables with LabelEncoder
+        label_encoder = LabelEncoder()
+        name_encoded = label_encoder.fit_transform([name])[0]
+        company_encoded = label_encoder.fit_transform([company])[0]
+        fuel_type_encoded = label_encoder.fit_transform([fuel_type])[0]
+        
+        # Create input array
+        inputs = np.array([[name_encoded, company_encoded, int(year), float(kms_driven), fuel_type_encoded]])
+        
+        # Scale the input
+        scaler = StandardScaler()
+        scaled_car = scaler.fit_transform(inputs)
+        
+        # Predict car price
+        car_price_prediction = car_model.predict(scaled_car)
+        st.success(f'Predicted car price: ${car_price_prediction[0]:,.2f}')
+
+elif selected == 'Bike Price Prediction':
+    st.title('Bike Price Prediction')
+    
+    bike_name = st.text_input('Name of the bike')
+    kms_driven = st.text_input('Kilometers driven')
+    owner = st.text_input('Owner')
+    age = st.text_input('Age of the bike')
+    power = st.text_input('Power of the bike')
+    brand = st.text_input('Bike brand')
+    
+    if st.button('Predict Bike Price'):
+        # Handle categorical variables with LabelEncoder
+        label_encoder = LabelEncoder()
+        bike_name_encoded = label_encoder.fit_transform([bike_name])[0]
+        owner_encoded = label_encoder.fit_transform([owner])[0]
+        brand_encoded = label_encoder.fit_transform([brand])[0]
+        
+        # Create input array
+        inputs = np.array([[bike_name_encoded, float(kms_driven), owner_encoded, int(age), float(power), brand_encoded]])
+        
+        # Scale the input
+        scaler = StandardScaler()
+        scaled_bike = scaler.fit_transform(inputs)
+        
+        # Predict bike price
+        bike_price_prediction = bike_model.predict(scaled_bike)
+        st.success(f'Predicted bike price: ${bike_price_prediction[0]:,.2f}')