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import gradio as gr | |
import torch | |
from PIL import Image | |
from transformers import ViTImageProcessor, ViTForImageClassification | |
def test(image): | |
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') | |
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
inputs = processor(images=image, return_tensors="pt") | |
outputs = model(**inputs) | |
logits = outputs.logits | |
probabilities = torch.nn.functional.softmax(logits, dim=-1) | |
probabilities_list = probabilities.tolist()[0] | |
class_probabilities = { | |
model.config.id2label[class_idx]: probability | |
for class_idx, probability in enumerate(probabilities_list) | |
} | |
top_4_probabilities = dict(sorted(class_probabilities.items(), key=lambda item: item[1], reverse=True)[:4]) | |
return top_4_probabilities | |
demo = gr.Interface(fn=test, inputs=gr.Image(type="pil"), outputs=gr.Label("Top 4 Scores and Classes")) | |
demo.launch() | |