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from transformers import AutoFeatureExtractor, ResNetForImageClassification
import torch
# from datasets import load_dataset
# dataset = load_dataset("huggingface/cats-image")
# image = dataset["test"]["image"][0]
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
import gradio as gr
def segment(image):
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
# print(model.config.id2label[predicted_label])
return model.config.id2label[predicted_label]
gr.Interface(fn=segment, inputs="image", outputs="label").launch() |