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Update app.py
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app.py
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import os
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import cv2
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import gradio as gr
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import torch
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from realesrgan.utils import RealESRGANer
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
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if not os.path.exists('RestoreFormer.ckpt'):
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt -P .")
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if not os.path.exists('RestoreFormer++.ckpt'):
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt -P .")
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# torch.hub.download_url_to_file(
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# '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',
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# 'lincoln.jpg')
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# torch.hub.download_url_to_file(
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# 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
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# 'AI-generate.jpg')
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# torch.hub.download_url_to_file(
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# 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
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# 'Blake_Lively.jpg')
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# torch.hub.download_url_to_file(
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# 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
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# '10045.png')
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# background enhancer with RealESRGAN
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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model_path = 'realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=
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os.makedirs('output', exist_ok=True)
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if version == 'RestoreFormer':
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face_enhancer = RestoreFormer(
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model_path=
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elif version == 'RestoreFormer++':
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face_enhancer = RestoreFormer(
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model_path=
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try:
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# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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return None, None
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title = "RestoreFormer
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To use it, simply upload your image.<br>
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"""
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article = r"""
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If you have any question, please email 📧 `wzhoux@connect.hku.hk`.
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"""
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demo = gr.Interface(
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inference, [
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gr.Image(type="filepath", label="Input"),
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title=title,
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description=description,
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article=article,
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# examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
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# ['10045.png', 'v1.4', 2, 50]]).launch()
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# examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
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# ['10045.png', 'v1.4', 2]]
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)
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demo.queue().launch()
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import os, sys
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import argparse
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import cv2
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import gradio as gr
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import torch
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from realesrgan.utils import RealESRGANer
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from glob import glob
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sys.path.insert(1, os.path.join(sys.path[0], '..'))
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from gradio_demo.RestoreFormer import RestoreFormer
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if not os.path.exists('experiments/pretrained_models'):
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os.makedirs('experiments/pretrained_models')
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realesr_model_path = 'experiments/pretrained_models/RealESRGAN_x4plus.pth'
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if not os.path.exists(realesr_model_path):
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -O experiments/pretrained_models/RealESRGAN_x4plus.pth")
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if not os.path.exists('experiments/RestoreFormer/'):
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os.makedirs('experiments/RestoreFormer/')
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restoreformer_model_path = 'experiments/RestoreFormer/last.ckpt'
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if not os.path.exists(restoreformer_model_path):
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt -O experiments/RestoreFormer/last.ckpt")
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if not os.path.exists('experiments/RestoreFormerPlusPlus/'):
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os.makedirs('experiments/RestoreFormerPlusPlus/')
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restoreformerplusplus_model_path = 'experiments/RestoreFormerPlusPlus/last.ckpt'
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if not os.path.exists(restoreformerplusplus_model_path):
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt -O experiments/RestoreFormerPlusPlus/last.ckpt")
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# background enhancer with RealESRGAN
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=realesr_model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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os.makedirs('output', exist_ok=True)
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if version == 'RestoreFormer':
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face_enhancer = RestoreFormer(
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model_path=restoreformer_model_path, upscale=2, arch='RestoreFormer', bg_upsampler=upsampler)
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elif version == 'RestoreFormer++':
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face_enhancer = RestoreFormer(
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model_path=restoreformerplusplus_model_path, upscale=2, arch='RestoreFormer++', bg_upsampler=upsampler)
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try:
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# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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return None, None
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title = "RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris"
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important_links=r'''
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<div align='center'>
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[](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf)
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[](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf)
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[](https://github.com/wzhouxiff/RestoreFormerPlusPlus)
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[](https://github.com/wzhouxiff/RestoreFormer)
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[](https://gradio.app/hub/wzhouxiff/RestoreFormerPlusPlus)
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</div>
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'''
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description = r"""
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<div align='center'>
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<a target='_blank' href='https://arxiv.org/pdf/2308.07228.pdf' style='float: left'>
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<img src='https://img.shields.io/badge/TPAMI-RestorFormer%2B%2B-green' alt='paper_RestroeForemer++'>
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</a>
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<a target='_blank' href='https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf' style='float: left'>
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<img src='https://img.shields.io/badge/CVPR22-RestorFormer-green' alt='paere_RestroeForemer' >
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</a>
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<a target='_blank' href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' style='float: left'>
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<img src='https://img.shields.io/badge/GitHub-RestoreFormer%2B%2B-red' alt='code_RestroeForemer++'>
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</a>
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<a target='_blank' href='https://github.com/wzhouxiff/RestoreFormer' style='float: left'>
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<img src='https://img.shields.io/badge/GitHub-RestoreFormer-red' alt='code_RestroeForemer' >
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</a>
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<a target='_blank' href='https://huggingface.co/spaces/wzhouxiff/RestoreFormerPlusPlus' style='float: left' >
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<img src='https://img.shields.io/badge/Demo-Gradio-orange' alt='demo' >
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</a>
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</div>
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<br>
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Gradio demo for <a href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' target='_blank'><b>RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris</b></a>.
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<br>
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It is used to restore your Old Photos.
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<br>
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To use it, simply upload your image.<br>
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"""
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article = r"""
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If the proposed algorithm is helpful, please help to ⭐ the GitHub Repositories: <a href='https://github.com/wzhouxiff/RestoreFormer' target='_blank'>RestoreFormer</a> and
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<a href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' target='_blank'>RestoreFormer++</a>. Thanks!
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[](https://github.com/wzhouxiff/RestoreFormer)
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[](https://github.com/wzhouxiff/RestoreFormerPlusPlus)
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---
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📝 **Citation**
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<br>
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If our work is useful for your research, please consider citing:
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```bibtex
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@article{wang2023restoreformer++,
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title={RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris},
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author={Wang, Zhouxia and Zhang, Jiawei and Chen, Tianshui and Wang, Wenping and Luo, Ping},
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booktitle={IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)},
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year={2023}
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}
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@article{wang2022restoreformer,
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title={RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs},
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author={Wang, Zhouxia and Zhang, Jiawei and Chen, Runjian and Wang, Wenping and Luo, Ping},
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booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2022}
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}
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```
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If you have any question, please email 📧 `wzhoux@connect.hku.hk`.
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"""
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css=r"""
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"""
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demo = gr.Interface(
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inference, [
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gr.Image(type="filepath", label="Input"),
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title=title,
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description=description,
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article=article,
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)
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demo.queue().launch()
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