app.py

import streamlit as st
import pandas as pd
import joblib

# Load the saved model
loaded_model = joblib.load('diabetes_prediction_model.joblib')

def predict_diabetes(data):
    # Use the loaded model to make predictions
    predicted_class = loaded_model.predict(data)
    return predicted_class

def main():
    st.title("Diabetes Prediction App")

    # Create input form for user to enter values
    st.sidebar.header("User Input Features")

    pregnancies = st.sidebar.slider("Pregnancies", 0, 17, 3)
    glucose = st.sidebar.slider("Glucose", 0, 199, 117)
    blood_pressure = st.sidebar.slider("Blood Pressure", 0, 122, 72)
    skin_thickness = st.sidebar.slider("Skin Thickness", 0, 99, 23)
    insulin = st.sidebar.slider("Insulin", 0, 846, 30)
    bmi = st.sidebar.slider("BMI", 0.0, 67.1, 32.0)
    diabetes_pedigree_function = st.sidebar.slider("Diabetes Pedigree Function", 0.078, 2.42, 0.3725)
    age = st.sidebar.slider("Age", 21, 81, 29)

    # Create a DataFrame from user input
    user_data = pd.DataFrame({
        'Pregnancies': [pregnancies],
        'Glucose': [glucose],
        'BloodPressure': [blood_pressure],
        'SkinThickness': [skin_thickness],
        'Insulin': [insulin],
        'BMI': [bmi],
        'DiabetesPedigreeFunction': [diabetes_pedigree_function],
        'Age': [age]
    })

    # Make prediction
    if st.button("Predict"):
        prediction = predict_diabetes(user_data)
        st.success(f"The individual is {'Diabetic' if prediction[0] == 1 else 'Not Diabetic'}.")

if __name__ == "__main__":
    main()