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(20)
+inventory = pd.DataFrame(dataset['train']).head(15)
 
 # Gradio Interface function
 def predict_return(selected_products, total_customer_purchases, total_customer_returns):
@@ -51,7 +51,7 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
 
             # Add description to the cart
             description = (
-                f"Model: {model}, Fabric: {fabric}, Colour: {color}\tSales Value: {product_value} USD"
+                f"Model: {model}, Fabric: {fabric}, Colour: {color} \tSales Value: {product_value} USD"
             )
             descriptions.append(description)
         else:
@@ -79,7 +79,7 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
             return "Error: No predictions found."
 
         # Format the cart output
-        cart_output = "\n".join(descriptions) + f"\nTotal Cart Value: {total_value} USD"
+        cart_output = "\n".join(descriptions) + f"\n\nTotal Cart Value: {total_value} USD"
 
         # Format the prediction results
         formatted_result = "\n".join([f"Product: {pred['product']} \t Prediction: {pred['prediction']} \t Confidence: {pred['confidence']}%" for pred in predictions])