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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) | |