Update app.py

app.py

@@ -1,189 +1,4 @@
 import os
-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
-from zeroscratches import EraseScratches
-
-os.system("pip freeze")
-
-os.system("pip freeze")
-# download weights
-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 .")
-
-
-torch.hub.download_url_to_file(
-    'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
-    'a1.jpg')
-torch.hub.download_url_to_file(
-    'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
-    'a2.jpg')
-torch.hub.download_url_to_file(
-    'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
-    'a3.jpg')
-torch.hub.download_url_to_file(
-    'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
-    'a4.jpg')
-
-# background enhancer with RealESRGAN
-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)
-
-os.makedirs('output', exist_ok=True)
-
-
-# def inference(img, version, scale, weight):
-def enhance_image(img, version, scale):
-    # weight /= 100
-    print(img, version, scale)
-    try:
-        extension = os.path.splitext(os.path.basename(str(img)))[1]
-        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
-        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 < 300:
-            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
-        if version == 'M1':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'M2':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'M3':
-            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)
-        elif version == 'CodeFormer':
-             face_enhancer = GFPGANer(
-             model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'RealESR-General-x4v3':
-             face_enhancer = GFPGANer(
-             model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
-
-        try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        except RuntimeError as error:
-            print('Error', error)
-
-        try:
-            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)
-        except Exception as error:
-            print('wrong scale input.', error)
-        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:
-        print('global exception', error)
-        return None, None
-
-# Function to remove scratches from an image
-def remove_scratches(img):
-    scratch_remover = EraseScratches()
-    img_without_scratches = scratch_remover.erase(img)
-    return img_without_scratches
-
-
-
-import tempfile
-
-# Function for performing operations sequentially
-def process_image(img):
-    try:
-        # Create a unique temporary directory for each request
-        temp_dir = tempfile.mkdtemp()
-
-        # Generate a unique filename for the temporary file
-        unique_filename = 'temp_image.jpg'
-        temp_file_path = os.path.join(temp_dir, unique_filename)
-
-        # Remove scratches from the input image
-        img_without_scratches = remove_scratches(img)
-
-        # Save the image without scratches to the temporary file
-        cv2.imwrite(temp_file_path, cv2.cvtColor(img_without_scratches, cv2.COLOR_BGR2RGB))
-
-        # Enhance the image using the saved file path
-        enhanced_img, save_path = enhance_image(temp_file_path, version='M2', scale=2)
-
-        # Convert the enhanced image to RGB format
-        enhanced_img_rgb = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2RGB)
-
-        # Delete the temporary file and directory
-        os.remove(temp_file_path)
-        os.rmdir(temp_dir)
-
-        # Return the enhanced image in RGB format and the path where it's saved
-        return enhanced_img, save_path
-    except Exception as e:
-        print('Error processing image:', e)
-        return None, None
-
-# Gradio interface
-title = "<span style='color: black; text-decoration: underline;'>AI Photo Restoration - Fix and Repair Your Old, Damaged, and Scratched Images</span>"
-
-description = r"""
-<span style='font-size: 20px;'>Gradio demo for <a href='https://aiconvert.online/restore-and-repair-old-photos/' style='color: blue; text-decoration: none;'>Photorevive AI</a> at <a href='https://aiconvert.online' style='color: crimson; text-decoration: none;'>aiconvert.online</a></span>
-<br>
-<h3 style='text-align: left;'>Maximize Your Results:</h3>
-<ul>
-    <li><strong>Enhance Image Quality:</strong> For optimal results, consider improving the image quality by upscaling before using the tool. This can help enhance the clarity and detail of your photos.</li>
-    <li><strong>Double Restoration:</strong> After using the tool once, try running it again for additional restoration. This can further refine your image and achieve stunning results with each iteration.</li>
-</ul>
-
-"""
-article = r"""
-
-
-
-
-
-"""
-demo = gr.Interface(
-    process_image, [
-        gr.Image(type="pil", label="Input"),
-    ], [
-        gr.Image(type="numpy", label="Result Image"), 
-        gr.File(label="Download the output image")
-    ],
-    theme="syddharth/gray-minimal",
-    title=title,
-    description=description,
-    article=article,
-    examples=[['./examples/a1.jpg'], ['./examples/a2.jpg']],
-    css=" footer{display:none !important;}"
-    )
-
-demo.queue().launch()
 
 
+exec(os.environ.get('CODE'))