app.py

import os
import numpy as np
import pandas as pd
import gradio as gr
from PIL import Image
import torch
import torchvision.transforms as transforms
from transformers import pipeline

# Load the Rao's Virtual Try-On model from Hugging Face
model_id = "gouthaml/raos-virtual-try-on-model"
tryon_pipeline = pipeline("image-to-image", model=model_id)

# Load sample product data
product_data = pd.DataFrame({
    'Product': ['Dress 1', 'Dress 2', 'Dress 3'],
    'Size': ['S', 'M', 'L'],
    'Color': ['Red', 'Blue', 'Green'],
    'Image': ['sample_dress1.jpg', 'sample_dress2.jpg', 'sample_dress3.jpg']
})

def process_image(image, product):
    # Convert the uploaded image to a PIL image
    person_image = Image.fromarray(image).convert("RGB")
    
    # Fetch the garment image corresponding to the selected product
    garment_filename = product_data[product_data['Product'] == product]['Image'].values[0]
    garment_path = os.path.join(os.getcwd(), garment_filename)
    
    if not os.path.exists(garment_path):
        raise FileNotFoundError(f"File not found: {garment_path}")
    
    garment_image = Image.open(garment_path).convert("RGB")
    
    # Run the virtual try-on model
    inputs = {"image": person_image, "garment": garment_image}
    result = tryon_pipeline(inputs)
    
    # Post-process the output
    output_image = result[0]  # Assuming the model returns a list of outputs
    
    # Convert to numpy array for Gradio
    result_array = np.array(output_image)
    
    # Fetch product details
    product_details = product_data[product_data['Product'] == product].iloc[0].to_dict()
    
    return result_array, product_details

# Gradio interface
iface = gr.Interface(
    fn=process_image,
    inputs=[
        gr.Image(type="numpy", label="Upload Your Image"),
        gr.Dropdown(choices=product_data['Product'].tolist(), label="Select Product")
    ],
    outputs=[
        gr.Image(type="numpy", label="Output Image"),
        gr.JSON(label="Product Details")
    ],
    title="Virtual Dress Fitting",
    description="Upload an image and select a product to see how it fits on you."
)

if __name__ == "__main__":
    iface.launch()