| """ | |
| Script de chargement pour le modèle logistic regression | |
| """ | |
| import pickle | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| def load_logistic_model(repo_id="Carlito-25/sentiment-model-logistic"): | |
| """Charge le modèle logistic regression depuis Hugging Face""" | |
| model_path = hf_hub_download( | |
| repo_id=repo_id, | |
| filename="model.pkl" | |
| ) | |
| with open(model_path, 'rb') as f: | |
| model = pickle.load(f) | |
| return model | |
| def predict_sentiment(model, text_features): | |
| """Prédiction avec le modèle logistic regression""" | |
| if isinstance(text_features, list): | |
| text_features = np.array(text_features).reshape(1, -1) | |
| prediction = model.predict(text_features) | |
| probabilities = model.predict_proba(text_features) | |
| return { | |
| 'prediction': prediction[0], | |
| 'probabilities': probabilities[0].tolist() | |
| } | |
| # Exemple d'usage | |
| if __name__ == "__main__": | |
| model = load_logistic_model() | |
| # Remplace par tes features réelles | |
| dummy_features = np.random.rand(1, 100) # Adapte selon tes features | |
| result = predict_sentiment(model, dummy_features) | |
| print(result) | |