Update app.py

app.py

@@ -4,21 +4,20 @@ import pandas as pd
 import os
 from datasets import load_dataset
 
-# FastAPI endpoint URL
+
 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']).sample(n=15, random_state=42)
 
-# Gradio Interface function
+
 def predict_return(selected_products, 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."
 
-    # Prepare the request data
     models = []
     fabrics = []
     colours = []
@@ -26,18 +25,15 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
     total_value = 0
 
     for selected_product in selected_products:
-        # Split each selected product into model, fabric, and color
         product_info = selected_product.split(" \t\t")
-        model_fabric_colour = product_info[0]  # Questo è del tipo "Model-Fabric-Colour"
+        model_fabric_colour = product_info[0]
         
-        # 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)
         
-        # Get the product details from the inventory
         product_details = inventory[(
             inventory['Item Brand Model'] == model) & 
             (inventory['Item Brand Fabric'] == fabric) & 
@@ -45,11 +41,9 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         ]
         
         if not product_details.empty:
-            # Calculate the product value and add it to the total
             product_value = product_details['Net Sales (FA)'].values[0]
             total_value += product_value
 
-            # Add description to the cart
             description = (
                 f"Model: {model}, Fabric: {fabric}, Colour: {color} \tSales Value: {product_value} USD"
             )
@@ -57,7 +51,6 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         else:
             descriptions.append(f"{model}-{fabric}-{color}: Not Found")
 
-    # Prepare the data to send to the API
     data = {
         "models": models,
         "fabrics": fabrics,
@@ -67,21 +60,17 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
     }
 
     try:
-        # Make the POST request to the FastAPI endpoint
         response = requests.post(API_URL, json=data)
-        response.raise_for_status()  # Raise an error for bad responses
+        response.raise_for_status()
 
-        # Get the predictions and return them
         result = response.json()
         predictions = result.get('predictions', [])
 
         if not predictions:
             return "Error: No predictions found."
 
-        # Format the cart output
         cart_output = "\n".join(descriptions) + f"\n\nTotal Cart Value: {round(total_value, 2)} 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])
 
         return cart_output, formatted_result
@@ -89,7 +78,6 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
     except requests.exceptions.RequestException as e:
         return f"Error: {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" \t\tType: {row['Product Type']} \t\tMaterial: {row['Main Material']} \t\tSales: {round(row['Net Sales (FA)'], 2)} USD"
@@ -97,21 +85,20 @@ checkbox_choices = [
 ]
 
 interface = gr.Interface(
-    fn=predict_return,  # Funzione per la logica di predizione
+    fn=predict_return,
     inputs=[
         gr.CheckboxGroup(
-            choices=checkbox_choices,  # Mostra le informazioni dettagliate accanto ai codici
+            choices=checkbox_choices,
             label="Select Products"
         ),
         gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),
         gr.Slider(0, 10, step=1, label="Total Customer Returns", value=0)
     ],
     outputs=[
-        gr.Textbox(label="Cart Details"),  # Dettagli del carrello
-        gr.Textbox(label="Prediction Results")  # Risultati della predizione
+        gr.Textbox(label="Cart Details"),
+        gr.Textbox(label="Prediction Results")
     ],
-    live=True  # Interattività in tempo reale
+    live=False
 )
 
-# Lancio dell'interfaccia Gradio
 interface.launch()