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8505abe d88e26b 8505abe d88e26b 8505abe 75dfb26 8505abe 75dfb26 8505abe 75dfb26 8505abe 75dfb26 8505abe 75dfb26 8505abe 75dfb26 8505abe 75dfb26 1ea5b3e 528a948 8505abe 8f0c9ff 8505abe 7a7ddbe 8505abe d78ce39 8505abe 1d71423 8505abe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import os
import gradio as gr
from PIL import Image
from app_predict import main
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/beit_1.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/convnext.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/dmnfnet.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/ecaresnet_50.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/efficient.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/regnet.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/swin.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification/releases/download/v0.0/vit.pth -P experiments/pretrained_models')
def inference(img, model):
os.system('mkdir test')
img.save("test/1.png", "PNG")
if model == 'Swin transformer':
predict = main('swin')
elif model == 'BEiT':
predict = main('beit')
elif model == 'NFNet':
predict = main('dmnfnet')
elif model == 'ECA-Resnet':
predict = main('ecaresnet_50')
elif model == 'EfficientNet':
predict = main('efficient')
elif model == 'Regnet':
predict = main('regnet')
elif model == 'ViT':
predict = main('vit')
elif model == 'ConvNext':
predict = main('convnext')
print(predict )
return predict
title = "[AICUP 2022] Orchid Image Classification (single image quick demo)"
description = ""
article = "<p style='text-align: center'><a href='https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification' target='_blank'>Orchid image classification</a> | <a href='https://github.com/FanChiMao/Competition-2022-Pytorch-Orchid_Classification' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=2022aicuphg' alt='visitor badge'></center>"
examples = [
['figures/1.jpg', 'ConvNext'],
['figures/2.jpg', 'ConvNext'],
['figures/3.jpg', 'ConvNext'],
['figures/4.jpg', 'ConvNext'],
['figures/5.jpg', 'ConvNext'],
]
gr.Interface(
inference,
[gr.inputs.Image(type="pil", label="Input"), gr.inputs.Dropdown(choices=[
'Swin transformer',
'BEiT',
'NFNet',
'ECA-Resnet',
'EfficientNet',
'Regnet',
'ViT',
'ConvNext'
], type="value", default='Swin transformer', label="model")],
outputs="label",
title=title,
description=description,
article=article,
allow_flagging=False,
allow_screenshot=False,
examples=examples
).launch(debug=True) |