Update product_return_prediction/api.py

product_return_prediction/api.py

@@ -126,10 +126,6 @@ async def root():
 
 @app.post("/predict/")
 async def predict(products: ProductRequest):
-    inventory_path: Path = EXTERNAL_DATA_DIR / "inventory.tsv"
-    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")
     inventory = pd.DataFrame(dataset['train'])
@@ -145,9 +141,12 @@ async def predict(products: ProductRequest):
         filtered_inventory, products.total_customer_purchases, products.total_customer_returns
     )
 
-    model = load_model(hf_hub_download(repo_id="molinari135/se4ai-models", filename="svm.pkl"))
-    
+    model_file = hf_hub_download(repo_id="molinari135/se4ai-models", filename="svm.pkl")
+    model = load_model(model_file)
+
+    scaler_file = hf_hub_download(repo_id="molinari135/se4ai-models", filename="scaler.pkl")
     scaled_inventory = apply_scaling(prepared_inventory, scaler_file)
+    
     predictions, probabilities = make_predictions(model, scaled_inventory)
 
     result = [