Update train.py

train.py

@@ -14,7 +14,6 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.compose import make_column_transformer
 from sklearn.pipeline import make_pipeline
 from sklearn.model_selection import train_test_split
-from huggingface_hub import HfApi, Repository
 
 # Ignore all warnings
 warnings.filterwarnings("ignore")
@@ -24,9 +23,6 @@ url = "http://www.ulb.ac.be/di/map/adalpozz/data/creditcard.Rdata"
 dst_path = "./creditcard.Rdata"
 wget.download(url, dst_path)
 
-# Define the directory to save the model (same as dataset directory)
-dataset_dir = os.path.dirname(dst_path)
-
 # Load the dataset
 parsed_res = rdata.parser.parse_file(dst_path)
 res = rdata.conversion.convert(parsed_res)
@@ -76,30 +72,8 @@ y_test_pred = model_pipeline.predict(X_test)
 
 print("Serializing Model")
 
-# Save the model directly to the dataset directory
-saved_model_path = os.path.join(dataset_dir, "model.joblib")
+# Save the model in the current working directory
+saved_model_path = "model.joblib"
 joblib.dump(model_pipeline, saved_model_path)
 
-print(f"Model Serialized and Saved directly to {saved_model_path}")
-
-# Get the Hugging Face API token securely from the secret environment variable
-api_token = os.getenv('FraudDemoWrite')  # Access the secret directly using its name
-
-if not api_token:
-    raise ValueError("Hugging Face API token not found. Ensure 'FraudDemoWrite' is set as a secret in your environment.")
-
-# Initialize Hugging Face API
-api = HfApi()
-
-# Create or use an existing repository on Hugging Face Hub
-repo_name = "kgauvin603/creditcard-fraud-detection"  # Replace with your desired repo name
-repo_url = api.create_repo(repo_id=repo_name, token=api_token, exist_ok=True)
-
-# Initialize the repository
-repo = Repository(local_dir="hf_model_repo", clone_from=repo_url, use_auth_token=api_token)
-
-# Move the model file to the repository directory
-os.rename(saved_model_path, os.path.join("hf_model_repo", "model.joblib"))
-
-# Push the model to the Hugging Face Hub
-repo.push_to_hub(commit_message="Upload trained model")
+print(f"Model Serialized and Saved to {saved_model_path}")