app.py

import torch
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import gradio as gr

model_id = f'rsadaphule/vit-base-patch16-224-finetuned-wildcats'
labels = ['AFRICAN LEOPARD',
 'CARACAL',
 'CHEETAH',
 'CLOUDED LEOPARD',
 'JAGUAR',
 'LIONS',
 'OCELOT',
 'PUMA',
 'SNOW LEOPARD',
 'TIGER']


def classify_image(image):
  model = AutoModelForImageClassification.from_pretrained(model_id)
  feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
  inp = feature_extractor(image, return_tensors='pt')
  outp = model(**inp)
  pred = torch.nn.functional.softmax(outp.logits, dim=-1)
  preds = pred[0].cpu().detach().numpy()
  confidence = {label: float(preds[i]) for i, label in enumerate(labels)}
  return confidence

interface = gr.Interface(fn=classify_image,
                         inputs='image',
                         examples=['cat1.jpg', 'cat2.jpg'],
                         outputs='label').launch(debug=True, share=True)