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| import gradio as gr | |
| import joblib | |
| import numpy as np | |
| # Load assets | |
| model = joblib.load('diabetes_model.joblib') | |
| scaler = joblib.load('scaler.joblib') | |
| def predict_diabetes(pregnancies, glucose, bp, skin, insulin, bmi, dpf, age): | |
| features = np.array([[pregnancies, glucose, bp, skin, insulin, bmi, dpf, age]]) | |
| features_scaled = scaler.transform(features) | |
| prediction = model.predict(features_scaled)[0] | |
| probability = model.predict_proba(features_scaled)[0] | |
| label = 'Diabetic' if prediction == 1 else 'Non-Diabetic' | |
| conf = probability[1] if prediction == 1 else probability[0] | |
| return f'{label} ({conf:.2%})' | |
| inputs = [ | |
| gr.Number(label='Pregnancies'), | |
| gr.Number(label='Glucose'), | |
| gr.Number(label='Blood Pressure'), | |
| gr.Number(label='Skin Thickness'), | |
| gr.Number(label='Insulin'), | |
| gr.Number(label='BMI'), | |
| gr.Number(label='Diabetes Pedigree Function'), | |
| gr.Number(label='Age') | |
| ] | |
| interface = gr.Interface( | |
| fn=predict_diabetes, | |
| inputs=inputs, | |
| outputs='text', | |
| title='Diabetes Risk Predictor', | |
| description='Optimized Random Forest model for diabetes classification.' | |
| ) | |
| interface.launch() | |