app.py

import os
import sys

# Force install a Gradio version that is compatible with Spaces
os.system("pip uninstall -y gradio gradio-client")
os.system("pip install gradio==4.13.0")  # This version should be compatible with Spaces

import cv2
import gradio as gr
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
import spaces

# download weights - using try/except to handle potential errors
try:
    if not os.path.exists('realesr-general-x4v3.pth'):
        os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
    if not os.path.exists('GFPGANv1.2.pth'):
        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
    if not os.path.exists('GFPGANv1.3.pth'):
        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
    if not os.path.exists('GFPGANv1.4.pth'):
        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
    if not os.path.exists('RestoreFormer.pth'):
        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
    if not os.path.exists('CodeFormer.pth'):
        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
except Exception as e:
    print(f"Error downloading model weights: {e}")

# Download sample images - using try/except to handle potential errors
try:
    torch.hub.download_url_to_file(
        'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
        'lincoln.jpg')
    torch.hub.download_url_to_file(
        'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
        'AI-generate.jpg')
    torch.hub.download_url_to_file(
        'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
        'Blake_Lively.jpg')
    torch.hub.download_url_to_file(
        'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
        '10045.png')
except Exception as e:
    print(f"Error downloading sample images: {e}")

# background enhancer with RealESRGAN
try:
    model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
    model_path = 'realesr-general-x4v3.pth'
    half = True if torch.cuda.is_available() else False
    upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
    print("Successfully created upsampler")
except Exception as e:
    print(f"Error creating model or upsampler: {e}")
    upsampler = None

os.makedirs('output', exist_ok=True)

# Apply the spaces.GPU decorator to enable ZeroGPU allocation
@spaces.GPU(duration=90)
def inference(img, version, scale):
    try:
        print(f"Processing image: {img}, version: {version}, scale: {scale}")
        if scale > 4:
            scale = 4  # avoid too large scale value
        
        extension = os.path.splitext(os.path.basename(str(img)))[1]
        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
        if img is None:
            print(f"Failed to read image: {img}")
            return None, None
            
        if len(img.shape) == 3 and img.shape[2] == 4:
            img_mode = 'RGBA'
        elif len(img.shape) == 2:  # for gray inputs
            img_mode = None
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        else:
            img_mode = None

        h, w = img.shape[0:2]
        if h > 3500 or w > 3500:
            print('too large size')
            return None, None
        
        if h < 300:
            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)

        if upsampler is None:
            print("Upsampler not initialized")
            return None, None

        if version == 'v1.2':
            face_enhancer = GFPGANer(
            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
        elif version == 'v1.3':
            face_enhancer = GFPGANer(
            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
        elif version == 'v1.4':
            face_enhancer = GFPGANer(
            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
        elif version == 'RestoreFormer':
            face_enhancer = GFPGANer(
            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
        else:
            print(f"Unknown version: {version}")
            return None, None

        print(f"Using {version} for enhancement")
        _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)

        if scale != 2:
            interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
            h, w = img.shape[0:2]
            output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
            
        if img_mode == 'RGBA':  # RGBA images should be saved in png format
            extension = 'png'
        else:
            extension = 'jpg'
        save_path = f'output/out.{extension}'
        
        cv2.imwrite(save_path, output)
        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
        return output, save_path
    
    except Exception as error:
        import traceback
        print(f"Error in inference: {error}")
        print(traceback.format_exc())
        return None, None


title = "GFPGAN: Practical Face Restoration Algorithm"
description = "Gradio demo Fix for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>It can be used to restore your <b>old photos</b> or improve <b>AI-generated faces</b>.<br>To use it, simply upload your image.<br>If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/Nick088Official/GFPGAN-Fix' target='_blank'>Github Repo</a> and recommend it to your friends 😊"

# Simplified article text to avoid URL parsing issues
article = "If you have any question, please email [email protected] or [email protected] (original creators)."

# Create the interface with more robust error handling
try:
    print("Creating Gradio interface...")
    demo = gr.Interface(
        fn=inference,
        inputs=[
            gr.Image(type="filepath", label="Input"),
            gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
            gr.Number(label="Rescaling factor", value=2),
        ],
        outputs=[
            gr.Image(type="numpy", label="Output (The whole image)"),
            gr.File(label="Download the output image")
        ],
        title=title,
        description=description,
        article=article,
        examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
                  ['10045.png', 'v1.4', 2]]
    )
    
    # Launch with the simplest possible configuration
    print("Launching Gradio interface...")
    demo.launch(server_name="0.0.0.0")
    
except Exception as e:
    import traceback
    print(f"Error setting up Gradio interface: {e}")
    print(traceback.format_exc())