app.py

from transformers import pipeline
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image as img

import numpy as np
import gradio as gr

featureextractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

def classify(input_img):
    filename = input_img
    imagearray = input_img
    inputs = featureextractor(images = imagearray, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    return model.config.id2label[predicted_class_idx]

demo = gr.Interface(fn=classify, inputs="image", outputs="text")
demo.launch()