Create app.py

app.py

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+import gradio as gr
+import cv2
+import torch
+from gfpgan import GFPGANer
+import numpy as np
+import os
+
+# Initialize GFPGANer (this will be done once when the app starts)
+# It's important to place model loading outside the prediction function if possible
+# to avoid reloading it on every call, which is slow.
+MODEL_PATH = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
+
+# Check if the model file already exists to avoid re-downloading every time
+# This is a simple check; a more robust solution might involve checking file integrity.
+# For Hugging Face Spaces, files in the repo are persistent.
+# If running locally, this helps avoid re-downloads if the script is restarted.
+local_model_path = "GFPGANv1.3.pth"
+if not os.path.exists(local_model_path):
+    print(f"Downloading model to {local_model_path}...")
+    torch.hub.download_url_to_file(MODEL_PATH, local_model_path, progress=True)
+    print("Model download complete.")
+else:
+    print(f"Model {local_model_path} already exists.")
+
+
+restorer = GFPGANer(
+    model_path=local_model_path, # Use local path after download
+    upscale=2,
+    arch='clean',
+    channel_multiplier=2,
+    bg_upsampler=None, # Can be 'realesrgan' if RealESRGAN is installed and background upsampling is desired
+    device='cpu'
+)
+
+def gfpgan_restore_face(input_image_np):
+    """
+    Restores faces in an input image using GFPGAN.
+    input_image_np: A NumPy array representing the input image (BGR format from cv2.imdecode).
+    Returns a NumPy array representing the restored image (BGR format).
+    """
+    if input_image_np is None:
+        raise gr.Error("Error: Could not read input image. Please upload a valid image.")
+
+    # GFPGAN expects BGR images, which cv2.imdecode provides if the image has color.
+    # If the image is grayscale, cv2.imdecode might return a 2D array.
+    # GFPGANer.enhance handles BGR or Grayscale images.
+
+    try:
+        # The enhance method returns: cropped_faces, restored_faces, restored_img
+        # restored_img is the full image with faces pasted back
+        _, _, restored_img = restorer.enhance(
+            input_image_np,
+            has_aligned=False,
+            only_center_face=False
+            # paste_to_img=True was removed as it's default or handled internally in newer versions
+        )
+    except Exception as e:
+        print(f"Error during GFPGAN processing: {e}")
+        raise gr.Error(f"GFPGAN processing failed: {e}. Check server logs for details.")
+
+    if restored_img is None:
+        # This might happen if no faces are detected or an error occurs
+        # Return the original image or an error message
+        print("No faces were detected or restored by GFPGAN.")
+        # raise gr.Error("No faces detected or an error occurred during restoration. Returning original image.")
+        return input_image_np # Or an image indicating no faces found
+
+    return restored_img # Return BGR image
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=gfpgan_restore_face,
+    inputs=gr.Image(type="numpy", label="Upload Input Image"),
+    outputs=gr.Image(type="numpy", label="Restored Output Image"),
+    title="GFPGAN Face Restoration (CPU)",
+    description="Upload an image with faces to restore them using GFPGAN. Runs on CPU, so it might be slow for large images.",
+    allow_flagging="never"
+)
+
+if __name__ == '__main__':
+    # For Hugging Face Spaces, you typically don't need app.launch() in app.py
+    # The Space will run it. For local testing:
+    iface.launch()