app.py

import streamlit as st
import numpy as np

# Load your trained model (or you can directly use the model if it's in the same script)

# Write a function to take user inputs
def user_input_features():
    cylinders = st.sidebar.slider('Cylinders', 3, 8, 4)
    displacement = st.sidebar.number_input('Displacement')
    horsepower = st.sidebar.number_input('Horsepower')
    weight = st.sidebar.number_input('Weight')
    acceleration = st.sidebar.number_input('Acceleration')
    model_year = st.sidebar.slider('Model Year', 70, 82, 76)
    data = {'cylinders': cylinders,
            'displacement': displacement,
            'horsepower': horsepower,
            'weight': weight,
            'acceleration': acceleration,
            'model_year': model_year}
    features = pd.DataFrame(data, index=[0])
    return features

# Main
st.write("""
# Simple MPG Prediction App
This app predicts the **Miles Per Gallon (MPG)** of your car!
""")

# User input features
input_df = user_input_features()

# Display the user input features
st.subheader('User Input features')
st.write(input_df)

# Predict and display the output
st.subheader('Prediction')
prediction = model.predict(input_df)
st.write(prediction)