Update src/fine_tune_helpers.py

src/fine_tune_helpers.py

@@ -1,36 +1,30 @@
-import pandas as pd
+from src.fine_tune_helpers import preprocess_data, train_model, save_model
 import logging
 
-def preprocess_data(dataset_path):
+def fine_tune_model(dataset_path, config):
     try:
-        data = pd.read_csv(dataset_path)
-        logging.info("Data loaded successfully")
+        # Preprocess data
+        data = preprocess_data(dataset_path)
         
-        # Example preprocessing: clean data, handle missing values, etc.
-        data.dropna(inplace=True)
+        # Initialize model and configure hyperparameters
+        model = train_model(data, config)
         
-        return data
-    except Exception as e:
-        logging.error(f"Error during data preprocessing: {e}")
-
-def train_model(data, config):
-    try:
-        # Assuming some model training logic
-        model = "YourModel"  # Placeholder
-        logging.info("Model training started")
-        
-        # Configuration-based training
-        # Use hyperparameters from config
-        learning_rate = config.getfloat("model", "learning_rate")
+        # Save the fine-tuned model
+        save_model(model)
         
-        return model
+        logging.info("Model fine-tuning complete!")
     except Exception as e:
-        logging.error(f"Error during model training: {e}")
+        logging.error(f"Error during model fine-tuning: {e}")
 
-def save_model(model):
-    try:
-        # Save the fine-tuned model
-        model.save("model_path")
-        logging.info("Model saved successfully")
-    except Exception as e:
-        logging.error(f"Error saving model: {e}")
+if __name__ == "__main__":
+    import argparse
+
+    # Set up argument parser
+    parser = argparse.ArgumentParser(description="Fine-tune a model with specified dataset and configuration.")
+    parser.add_argument("dataset_path", type=str, help="Path to the dataset file.")
+    parser.add_argument("--config", type=str, default="configs/model_config.json", help="Path to the configuration file.")
+
+    args = parser.parse_args()
+
+    # Fine-tune the model with provided arguments
+    fine_tune_model(args.dataset_path, args.config)