Update app.py

app.py

@@ -10,7 +10,7 @@ API_URL = "https://molinari135-product-return-prediction-api.hf.space/predict/"
 # Load the inventory dataset from Hugging Face
 hf_token = os.getenv("inventory_data")
 dataset = load_dataset("molinari135/armani-inventory", token=hf_token, data_files="inventory.tsv")
-inventory = pd.DataFrame(dataset['train']).head(50)
+inventory = pd.DataFrame(dataset['train']).head(20)
 
 # Gradio Interface function
 def predict_return(selected_products, total_customer_purchases, total_customer_returns):
@@ -27,7 +27,12 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
 
     for selected_product in selected_products:
         # Split each selected product into model, fabric, and color
-        model, fabric, color = selected_product.split("-")
+        product_info = selected_product.split(" - ")
+        model_fabric_colour = product_info[0]  # Questo è del tipo "Model-Fabric-Colour"
+        
+        # Dividi il codice prodotto in modello, tessuto e colore
+        model, fabric, color = model_fabric_colour.split("-")
+
         models.append(model)
         fabrics.append(fabric)
         colours.append(color)