Create app.py

app.py

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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 DeepVTO model from Hugging Face
+deepvto_pipeline = pipeline("image-to-image", model="huggingface/deepvto")
+
+# 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")
+    
+    # Convert images to the format required by the model
+    person_image_tensor = transforms.ToTensor()(person_image).unsqueeze(0)
+    garment_image_tensor = transforms.ToTensor()(garment_image).unsqueeze(0)
+    
+    # Run the DeepVTO model
+    with torch.no_grad():
+        output = deepvto_pipeline(person_image_tensor, garment_image_tensor)
+    
+    # Convert the output to a PIL image
+    output_image = transforms.ToPILImage()(output[0])
+    
+    # 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()