Update modelConnector.py

modelConnector.py

@@ -5,9 +5,9 @@ from huggingface_hub import hf_hub_download, HfApi
 from model_trainer import train_models  # Assumes model_trainer.py exists with train_models function
 
 # Hugging Face Model & Dataset Information
-MODEL_REPO = "spaces/chagu13/is_click"
+MODEL_REPO = "chagu13/is_click"
 MODEL_FILENAME = "models/rf_model.pkl"
-DATA_REPO = "datasets/chagu13/is_click_data"
+DATA_REPO = "chagu13/is_click_data"
 LOCAL_MODEL_PATH = f"models/{MODEL_FILENAME}"
 
 # Hugging Face API
@@ -23,7 +23,7 @@ class ModelConnector:
     def check_model_exists(self):
         """Check if the model exists on Hugging Face."""
         try:
-            hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
+            hf_hub_download(repo_id=MODEL_REPO, repo_type="space", filename=MODEL_FILENAME)
             return True
         except Exception:
             return False
@@ -31,7 +31,7 @@ class ModelConnector:
     def load_model(self):
         """Download and load the model from Hugging Face."""
         if self.check_model_exists():
-            model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
+            model_path = hf_hub_download(repo_id=MODEL_REPO, repo_type="space", filename=MODEL_FILENAME)
             return joblib.load(model_path)
         return None
 
@@ -39,7 +39,7 @@ class ModelConnector:
         """Train a new model and upload it to Hugging Face."""
         try:
             # Load dataset
-            train_data_path = hf_hub_download(repo_id=DATA_REPO, filename="train_dataset_full.csv")
+            train_data_path = hf_hub_download(repo_id=DATA_REPO, repo_type="dataset", filename="train_dataset_full.csv")
             train_data = pd.read_csv(train_data_path)
 
             X_train = train_data.drop(columns=["is_click"])
@@ -69,7 +69,7 @@ class ModelConnector:
         """Retrain the existing model with new data."""
         try:
             # Load dataset
-            train_data_path = hf_hub_download(repo_id=DATA_REPO, filename="train_dataset_full.csv")
+            train_data_path = hf_hub_download(repo_id=DATA_REPO, repo_type="dataset", filename="train_dataset_full.csv")
             train_data = pd.read_csv(train_data_path)
 
             X_train = train_data.drop(columns=["is_click"])