Update app.py

app.py

@@ -10,15 +10,15 @@ 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)
 
-# Funzione Gradio per la predizione
+# Gradio Interface function
 def predict_return(selected_products, total_customer_purchases, total_customer_returns):
-    # Verifica che i ritorni non siano maggiori degli acquisti
+    # Input validation for returns (must be <= purchases)
     if total_customer_returns > total_customer_purchases:
-        return "Errore: il numero dei ritorni non può essere maggiore degli acquisti."
+        return "Error: Total returns cannot be greater than total purchases."
 
-    # Dati per la richiesta
+    # Prepare the request data
     models = []
     fabrics = []
     colours = []
@@ -26,13 +26,13 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
     total_value = 0
 
     for selected_product in selected_products:
-        # Dividi ciascun prodotto selezionato in modello, tessuto e colore
+        # Split each selected product into model, fabric, and color
         model, fabric, color = selected_product.split("-")
         models.append(model)
         fabrics.append(fabric)
         colours.append(color)
         
-        # Ottieni i dettagli del prodotto dall'inventario
+        # Get the product details from the inventory
         product_details = inventory[(
             inventory['Item Brand Model'] == model) & 
             (inventory['Item Brand Fabric'] == fabric) & 
@@ -40,22 +40,20 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         ]
         
         if not product_details.empty:
-            # Calcola il valore del prodotto e somma al totale
+            # Calculate the product value and add it to the total
             product_value = product_details['Net Sales (FA)'].values[0]
             total_value += product_value
 
-            # Aggiungi descrizione al carrello
+            # 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)
         else:
             descriptions.append(f"{model}-{fabric}-{color}: Not Found")
 
-    # Dati da inviare all'API
+    # Prepare the data to send to the API
     data = {
         "models": models,
         "fabrics": fabrics,
@@ -65,40 +63,36 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
     }
 
     try:
-        # Richiesta POST all'endpoint FastAPI
+        # Make the POST request to the FastAPI endpoint
         response = requests.post(API_URL, json=data)
-        response.raise_for_status()  # Solleva un'eccezione per risposte errate
+        response.raise_for_status()  # Raise an error for bad responses
 
-        # Ottieni le predizioni e restituiscile
+        # Get the predictions and return them
         result = response.json()
         predictions = result.get('predictions', [])
 
         if not predictions:
-            return "Errore: nessuna predizione trovata."
+            return "Error: No predictions found."
 
-        # Formatta l'output del carrello
+        # Format the cart output
         cart_output = "\n".join(descriptions) + f"\nTotal Cart Value: {total_value} USD"
 
-        # Formatta i risultati delle predizioni
+        # Format the prediction results
         formatted_result = "\n".join([f"Product: {pred['product']} \t Prediction: {pred['prediction']} \t Confidence: {pred['confidence']}%" for pred in predictions])
 
         return cart_output, formatted_result
 
     except requests.exceptions.RequestException as e:
-        return f"Errore: {str(e)}"
-
-# Crea l'interfaccia Gradio con le checkbox a sinistra
-checkbox_choices = [
-    f"{row['Item Brand Model']}-{row['Item Brand Fabric']}-{row['Item Brand Colour']} - "
-    f"Type: {row['Product Type']} - Material: {row['Main Material']} - Sales: {row['Net Sales (FA)']} USD"
-    for _, row in inventory.iterrows()
-]
+        return f"Error: {str(e)}"
 
+# Gradio interface elements
 interface = gr.Interface(
     fn=predict_return,  # Funzione per la logica di predizione
     inputs=[
         gr.CheckboxGroup(
-            choices=checkbox_choices,  # Mostra le informazioni dettagliate accanto ai codici
+            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()], 
             label="Select Products"
         ),
         gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),