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model.py

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+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])