Update product_return_prediction/api.py

product_return_prediction/api.py

@@ -127,8 +127,8 @@ async def root():
 @app.post("/predict/")
 async def predict(products: ProductRequest):
     inventory_path: Path = EXTERNAL_DATA_DIR / "inventory.tsv"
-    model_name: str = "svm.pkl"
-    scaler_name: str = "scaler.pkl"
+    model_path: Path = MODELS_DIR / "svm.pkl"
+    scaler_file: Path = MODELS_DIR / "scaler.pkl"
 
     hf_token = os.getenv("inventory_data")
     dataset = load_dataset("molinari135/armani-inventory", token=hf_token, data_files="inventory.tsv")
@@ -145,14 +145,14 @@ async def predict(products: ProductRequest):
         filtered_inventory, products.total_customer_purchases, products.total_customer_returns
     )
 
-    models_uri = "https://huggingface.co/molinari135/se4ai-models/resolve/main/"
-    model_path = MODELS_DIR / model_name
-    scaler_path = MODELS_DIR / scaler_name
+    # models_uri = "https://huggingface.co/molinari135/se4ai-models/resolve/main/"
+    # model_path = MODELS_DIR / model_name
+    # scaler_path = MODELS_DIR / scaler_name
 
-    headers = f"Authorization: Bearer {hf_token}"
+    # headers = f"Authorization: Bearer {hf_token}"
 
-    download_file(f"{models_uri}{model_name}", model_path, headers)
-    download_file(f"{models_uri}{scaler_name}", scaler_path, headers)
+    # download_file(f"{models_uri}{model_name}", model_path, headers)
+    # download_file(f"{models_uri}{scaler_name}", scaler_path, headers)
 
     model = load_model(model_path)