import gradio as gr from transformers import AutoConfig,ViTImageProcessor,ViTForImageClassification,AutoModel import base64 import os processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') images = 'room.jpg' def image_classifier(image): inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits logits_np = logits.detach().cpu().numpy() logits_args = logits_np.argsort()[0][-3:] prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ] result = {} for i,item in enumerate(prediction_classes): result[item] = logits_np[0][i] return result with gr.Blocks(title="Image Classification using Google Vision Transformer") as demo : gr.Markdown( """