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import gradio as gr
import torch
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory 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('resnet50', pretrained=True)
transform = create_transform(
**resolve_data_config({}, model=model)
)
model.eval()
def predict_fn(img):
img = img.convert('RGB')
img = transform(img).unsqueeze(0)
with torch.no_grad():
out = model(img)
probabilities = torch.nn.functional.softmax(out[0], dim=0)
values, indices = torch.topk(probabilities, k=3)
return {LABELS[i]: v.item() for i, v in zip(indices, values)}
gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label').launch()
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