app.py

def classify(im):
  features = feature_extractor(im, return_tensors='pt')
  logits = model(features["pixel_values"])[-1]
  probability = torch.nn.functional.softmax(logits, dim=-1)
  probs = probability[0].detach().numpy()
  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
  return confidences
  
 import gradio as gr

sample_images=[['https://s3.amazonaws.com/moonup/production/uploads/1663933284359-611f9702593efbee33a4f7c9.png'],
['https://s3.amazonaws.com/moonup/production/uploads/1663933284374-611f9702593efbee33a4f7c9.png'],
['https://s3.amazonaws.com/moonup/production/uploads/1663933284412-611f9702593efbee33a4f7c9.png']]

title = 'Bean Leaf Classifier'
description = 'This model is trained for beans leaf classification but might give a false result on other leaves'
interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'label',
                         title = title,
                         description = description,
                         examples=sample_images)

interface.launch(debug=True)