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()