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import cv2 |
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import gradio as gr |
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import os |
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import functools |
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from PIL import Image |
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from rembg import remove |
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from io import BytesIO |
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import numpy as np |
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import torch |
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from torch.autograd import Variable |
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from torchvision import transforms |
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import torch.nn.functional as F |
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import gdown |
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import matplotlib.pyplot as plt |
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import warnings |
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warnings.filterwarnings("ignore") |
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import requests |
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@functools.lru_cache() |
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def get_url_im(t): |
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user_agent = {'User-agent': 'gradio-app'} |
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response = requests.get(t, headers=user_agent) |
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return (BytesIO(response.content)) |
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def inference(image): |
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im_path = get_url_im(image) |
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im = Image.open(im_reader(im_path)) |
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return im, im , im |
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title = "Bg remover for sarvm catalog" |
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description = "Bg remover for sarvm catalog" |
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article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_cmp_public' alt='visitor badge'></center></div>" |
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interface = gr.Interface( |
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fn=inference, |
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inputs=gr.Textbox(label="Text or Image URL", interactive=True), |
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outputs=["image", "image", "image"], |
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title=title , |
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description=description, |
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article=article, |
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allow_flagging='never', |
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cache_examples=False, |
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).queue().launch(show_error=True, share = True) |
