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(50)
 
 # Gradio Interface function
 def predict_return(selected_products, total_customer_purchases, total_customer_returns):
@@ -47,6 +47,8 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
             # Add description to the cart
             description = (
                 f"Model: {model}, Fabric: {fabric}, Colour: {color}, "
+                f"Product Type: {product_details['Product Type'].values[0]}, "
+                f"Material: {product_details['Main Material'].values[0]}, "
                 f"Sales Value: {product_value} USD"
             )
             descriptions.append(description)
@@ -90,9 +92,8 @@ interface = gr.Interface(
     fn=predict_return,  # Funzione per la logica di predizione
     inputs=[
         gr.CheckboxGroup(
-            choices=[
-                f"{row['Item Brand Model']}-{row['Item Brand Fabric']}-{row['Item Brand Colour']} \nProduct type: {row['Product Type']} Product subtype: {row['Product Subtype']} Price: {row['Net Sales (FA)']}"
-                for _, row in inventory.iterrows()], 
+            choices=[f"{row['Item Brand Model']}-{row['Item Brand Fabric']}-{row['Item Brand Colour']}" 
+                     for _, row in inventory.iterrows()], 
             label="Select Products"
         ),
         gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),