Create app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+import os
+# Import ACGPN-specific modules (adjust based on actual repository structure)
+# Note: You may need to copy relevant ACGPN code into the Space or modify imports
+from models.acgpn import ACGPN  # Hypothetical import; replace with actual model class
+from utils.preprocessing import preprocess_image, parse_human  # Hypothetical preprocessing utilities
+
+# Set device to CPU
+device = torch.device("cpu")
+
+# Load pre-trained ACGPN model
+def load_model():
+    model = ACGPN()  # Initialize model (adjust parameters as per ACGPN docs)
+    checkpoint_path = "checkpoints/acgpn_checkpoint.pth"  # Path to pre-trained weights
+    model.load_state_dict(torch.load(checkpoint_path, map_location=device))
+    model.to(device)
+    model.eval()
+    return model
+
+model = load_model()
+
+# Function to process images and generate try-on
+def virtual_try_on(person_image, cloth_image):
+    try:
+        # Convert Gradio inputs (PIL Images) to numpy arrays
+        person_img = np.array(person_image)
+        cloth_img = np.array(cloth_image)
+
+        # Preprocess images (resize, normalize, etc.)
+        person_processed, person_mask = preprocess_image(person_img, is_person=True)
+        cloth_processed = preprocess_image(cloth_img, is_person=False)
+
+        # Parse human pose and segmentation (using ACGPN utilities)
+        pose_map, parse_map = parse_human(person_processed)
+
+        # Convert to tensors
+        person_tensor = torch.from_numpy(person_processed).float().to(device)
+        cloth_tensor = torch.from_numpy(cloth_processed).float().to(device)
+        pose_tensor = torch.from_numpy(pose_map).float().to(device)
+        parse_tensor = torch.from_numpy(parse_map).float().to(device)
+
+        # Run inference
+        with torch.no_grad():
+            output = model(person_tensor, cloth_tensor, pose_tensor, parse_tensor)
+            output = output.cpu().numpy()
+
+        # Post-process output
+        output_img = (output * 255).astype(np.uint8)
+        output_img = Image.fromarray(output_img)
+
+        return output_img
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=virtual_try_on,
+    inputs=[
+        gr.Image(type="pil", label="Upload Person Image"),
+        gr.Image(type="pil", label="Upload Clothing Image"),
+    ],
+    outputs=gr.Image(type="pil", label="Try-On Result"),
+    title="ACGPN Virtual Try-On",
+    description="Upload a person image and a clothing image to see the virtual try-on result.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)