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import gradio as gr
import requests
from PIL import Image
import io
import base64
import logging
from app import ModelManager
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def process_image(url: str):
try:
# Initialize model manager (will load models if not already loaded)
model_manager = ModelManager()
# Download image from URL
response = requests.get(url, stream=True)
if response.status_code != 200:
raise ValueError("Could not download image from URL")
# Process image
image = Image.open(response.raw).convert("RGB")
result = model_manager.process_clothes_image(image)
# Convert base64 mask back to image
mask_data = result["mask"].split(",")[1]
mask_bytes = base64.b64decode(mask_data)
mask_image = Image.open(io.BytesIO(mask_bytes))
return image, mask_image, f"Processed image size: {result['size']}"
except Exception as e:
logger.error(f"Error processing image: {str(e)}")
return None, None, f"Error: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=process_image,
inputs=gr.Textbox(label="Image URL", placeholder="Enter the URL of the image"),
outputs=[
gr.Image(label="Original Image"),
gr.Image(label="Segmentation Mask"),
gr.Textbox(label="Processing Info")
],
title="Clothes Segmentation",
description="Enter an image URL to generate a segmentation mask for clothing items.",
examples=[
["https://example.com/path/to/clothing/image.jpg"],
["https://another-example.com/fashion/photo.jpg"]
],
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch(server_port=7861) # Using different port than FastAPI
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