Create app.py

app.py

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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)