| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| import os | |
| from datasets import load_dataset | |
| API_URL = "https://molinari135-product-return-prediction-api.hf.space/predict/" | |
| 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) | |
| def predict_return(selected_products, total_customer_purchases, total_customer_returns): | |
| if total_customer_returns > total_customer_purchases: | |
| return "Error: Total returns cannot be greater than total purchases." | |
| models = [] | |
| fabrics = [] | |
| colours = [] | |
| descriptions = [] | |
| total_value = 0 | |
| for selected_product in selected_products: | |
| product_info = selected_product.split(" \t\t") | |
| model_fabric_colour = product_info[0] | |
| model, fabric, color = model_fabric_colour.split("-") | |
| models.append(model) | |
| fabrics.append(fabric) | |
| colours.append(color) | |
| product_details = inventory[( | |
| inventory['Item Brand Model'] == model) & | |
| (inventory['Item Brand Fabric'] == fabric) & | |
| (inventory['Item Brand Colour'] == color) | |
| ] | |
| if not product_details.empty: | |
| product_value = product_details['Net Sales (FA)'].values[0] | |
| total_value += product_value | |
| description = ( | |
| f"Model: {model}, Fabric: {fabric}, Colour: {color} \tSales Value: {product_value} USD" | |
| ) | |
| descriptions.append(description) | |
| else: | |
| descriptions.append(f"{model}-{fabric}-{color}: Not Found") | |
| data = { | |
| "models": models, | |
| "fabrics": fabrics, | |
| "colours": colours, | |
| "total_customer_purchases": total_customer_purchases, | |
| "total_customer_returns": total_customer_returns | |
| } | |
| try: | |
| response = requests.post(API_URL, json=data) | |
| response.raise_for_status() | |
| result = response.json() | |
| predictions = result.get('predictions', []) | |
| if not predictions: | |
| return "Error: No predictions found." | |
| cart_output = "\n".join(descriptions) + f"\n\nTotal Cart Value: {round(total_value, 2)} USD" | |
| 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"Error: {str(e)}" | |
| 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" | |
| for _, row in inventory.iterrows() | |
| ] | |
| interface = gr.Interface( | |
| fn=predict_return, | |
| inputs=[ | |
| gr.CheckboxGroup( | |
| 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"), | |
| gr.Textbox(label="Prediction Results") | |
| ], | |
| live=False | |
| ) | |
| interface.launch() |