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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 | |