Update app.py

app.py

@@ -13,13 +13,13 @@ dataset = load_dataset("molinari135/armani-inventory", token=hf_token, data_file
 inventory = pd.DataFrame(dataset['train']).head(50)
 
 # Gradio Interface function
-def predict_return(selected_rows, total_customer_purchases, total_customer_returns):
+def predict_return(selected_indices, total_customer_purchases, total_customer_returns):
     # Input validation for returns (must be <= purchases)
     if total_customer_returns > total_customer_purchases:
         return "Error: Total returns cannot be greater than total purchases."
 
-    # Selezionare i prodotti scelti
-    selected_products = inventory.iloc[selected_rows]
+    # Selezionare i prodotti scelti usando gli indici
+    selected_products = inventory.iloc[selected_indices]
     
     # Preparare i dati per la predizione
     models = selected_products['Item Brand Model'].tolist()
@@ -48,18 +48,15 @@ def predict_return(selected_rows, total_customer_purchases, total_customer_retur
     return cart_details, formatted_result, total_cart
 
 # Gradio interface elements
+checkboxes = [
+    gr.Checkbox(label=f"{row['Item Brand Model']} - {row['Item Brand Fabric']} - {row['Item Brand Colour']}", value=False)
+    for _, row in inventory.iterrows()
+]
+
 interface = gr.Interface(
     fn=predict_return,  # Funzione per la logica di predizione
     inputs=[
-        gr.DataFrame(
-            value=inventory[['Item Brand Model', 'Item Brand Fabric', 'Item Brand Colour', 'Product Type', 'Main Material', 'Net Sales (FA)']],
-            headers=["Model", "Fabric", "Colour", "Type", "Material", "Sales (FA)"],
-            interactive=True,  # Permette la selezione
-            col_count=6,  # Numero di colonne
-            row_count=5,  # Numero di righe visibili
-            checkbox_column='Checkbox',  # Colonna checkbox
-            label="Select Products"
-        ),
+        gr.Column([*checkboxes], label="Select Products"),  # Aggiungi tutte le checkbox
         gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),
         gr.Slider(0, 10, step=1, label="Total Customer Returns", value=0)
     ],