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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()
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