| import gradio as gr |
| import pickle |
| import numpy as np |
|
|
| model = pickle.load(open('random_forest_model.pkl', 'rb')) |
|
|
| SEVERITY_LABELS = {0: 'Aucune', 1: 'Légère', 2: 'Modérée', 3: 'Sévère'} |
|
|
| def predict(age, gender, severity, test_last, test_mean, test_trend, previous_medication, prescribed_medication): |
| features = np.array([[age, gender, severity, test_last, test_mean, |
| test_trend, previous_medication, prescribed_medication]]) |
| prediction = model.predict(features)[0] |
| probabilities = model.predict_proba(features)[0] |
| return SEVERITY_LABELS[prediction], {SEVERITY_LABELS[i]: float(p) for i, p in enumerate(probabilities)} |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=[ |
| gr.Number(label='Age'), |
| gr.Radio([0, 1], label='Gender (0=F, 1=M)'), |
| gr.Slider(0, 3, step=1, label='Current Severity'), |
| gr.Number(label='Last Test Score'), |
| gr.Number(label='Mean Test Score'), |
| gr.Number(label='Test Trend'), |
| gr.Slider(0, 2, step=1, label='Previous Medication'), |
| gr.Slider(0, 2, step=1, label='Prescribed Medication'), |
| ], |
| outputs=[ |
| gr.Text(label='Predicted Future Severity'), |
| gr.Label(label='Class Probabilities'), |
| ], |
| title='Alzheimer Severity Predictor', |
| ) |
|
|
| demo.launch() |
