| import os |
| import joblib |
|
|
| def load_all_models(models_dir="models"): |
| """ |
| Load all models and their features from the given directory. |
| """ |
| models = {} |
| features = {} |
| if not os.path.exists(models_dir): |
| raise FileNotFoundError(f"Models directory '{models_dir}' not found.") |
|
|
| for model_file in os.listdir(models_dir): |
| if model_file.endswith(".pkl"): |
| model_name = os.path.splitext(model_file)[0] |
| data = joblib.load(os.path.join(models_dir, model_file)) |
| models[model_name] = data['model'] |
| features[model_name] = data['features'] |
| print(f"Model '{model_name}' loaded successfully with features: {features[model_name]}") |
| return models, features |
|
|
| def predict_with_model(model, input_data): |
| """ |
| Predict using a loaded model. |
| |
| Parameters: |
| - model: The loaded model. |
| - input_data: A dictionary or Pandas DataFrame row containing input features. |
| |
| Returns: |
| - prediction: Model prediction. |
| """ |
| prediction = model.predict([input_data]) |
| return int(prediction[0]) |
