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from _typeshed import OpenBinaryModeUpdating
from types import resolve_bases
import requests
import gradio as gr
import torch
from timm import create_model
from tim.data import reslove_data_config
from timm.data.transformer import create_transform
IMAGENET_1k_URL = "https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt"
LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n')
model = create_model('restnet50',pretrained=True)
transofrm = create_transform(**resolve_data_config{}, model=model)
model.eval()
def predict_fn(img):
img = img.convert('RGB')
img = transofrm(img).unsqueez(0)
with torch.no_grad():
out = model(img)
probabilites = torch.nn.functional.softmax(out[0], dim=0)
values , indices = torch.topk(probabilites, k=5)
# return
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