import streamlit as st
import numpy as np
import pandas as pd
import joblib
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Load the trained model (assuming it's saved as a .pkl file)
model = joblib.load('RandomForestRegressor_model.pkl')

# Example: sample transformers for preprocessing
num_features = ['year', 'km_driven']  # Example numerical features
cat_features = ['seller_type', 'transmission_type', 'fuel_type', 'model']  # Example categorical features

# Preprocessing pipeline
numeric_transformer = StandardScaler()
onehot_transformer = OneHotEncoder()

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, num_features),
        ('cat', onehot_transformer, cat_features)
    ])

# Streamlit app
st.title('Used Car Price Prediction')

# Sidebar form for user input
st.sidebar.header('Enter Car Details')

# Input fields
year = st.sidebar.number_input('Year of Manufacture', min_value=1990, max_value=2023, value=2015)
km_driven = st.sidebar.number_input('Kilometers Driven', min_value=0, max_value=300000, value=50000)

# Dropdown fields
seller_type = st.sidebar.selectbox('Seller Type', ['Dealer', 'Individual'])
transmission_type = st.sidebar.selectbox('Transmission Type', ['Manual', 'Automatic'])
fuel_type = st.sidebar.selectbox('Fuel Type', ['Petrol', 'Diesel', 'CNG', 'LPG'])
model = st.sidebar.text_input('Car Model (encoded)')

# Button to trigger the prediction
if st.sidebar.button('Predict Price'):
    # Create input dataframe
    input_data = pd.DataFrame({
        'year': [year],
        'km_driven': [km_driven],
        'seller_type': [seller_type],
        'transmission_type': [transmission_type],
        'fuel_type': [fuel_type],
        'model': [model]
    })

    # Preprocess the input
    input_data_transformed = preprocessor.transform(input_data)

    # Predict the price
    predicted_price = model.predict(input_data_transformed)

    # Display the result
    st.write(f'The predicted selling price for the car is: ₹ {predicted_price[0]:,.2f}')