| import joblib | |
| from sklearn.linear_model import LogisticRegression | |
| import numpy as np | |
| def train_and_save_model(): | |
| # Modèle fictif simple | |
| X = np.array([[1, 2], [2, 3], [3, 4], [4, 5]]) | |
| y = np.array([0, 0, 1, 1]) | |
| model = LogisticRegression() | |
| model.fit(X, y) | |
| joblib.dump(model, 'model.joblib') | |
| print("Modèle entraîné et sauvegardé.") | |
| def predict(input_data): | |
| model = joblib.load('model.joblib') | |
| prediction = model.predict([input_data]) | |
| print(f"Prédiction : {prediction[0]}") | |
| return prediction[0] | |
| if __name__ == "__main__": | |
| train_and_save_model() | |
| predict([3, 4]) |