app.py

import gradio as gr
import onnxruntime as ort
import numpy as np
from PIL import Image
import requests
from torchvision import transforms

# Load ONNX model
ort_session = ort.InferenceSession("model.onnx")  # Ensure model.onnx is in your app folder

# Preprocessing function
def preprocess(image):
    image = image.resize((320, 320)).convert("RGB")
    image_np = np.array(image).astype(np.float32) / 255.0
    image_np = image_np.transpose(2, 0, 1)  # HWC -> CHW
    image_np = np.expand_dims(image_np, axis=0)  # Add batch dimension
    return image_np

# Inference + Postprocessing
def segment_dress(image):
    input_tensor = preprocess(image)
    inputs = {ort_session.get_inputs()[0].name: input_tensor}
    outputs = ort_session.run(None, inputs)
    
    pred = outputs[0][0][0]
    pred = (pred - pred.min()) / (pred.max() - pred.min())
    pred_img = Image.fromarray((pred * 255).astype(np.uint8)).resize(image.size)

    # Apply mask to image
    image_np = np.array(image.convert("RGB"))
    mask = np.array(pred_img).astype(np.float32) / 255.0
    masked = (image_np * mask[..., None]).astype(np.uint8)

    return Image.fromarray(masked)

# Gradio app
gr.Interface(
    fn=segment_dress,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Image(type="pil", label="Segmented Dress"),
    title="Background Removal",
    description="Upload an image and Remove the Background"
).launch()