app.py

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

model_id = f'jonathanfernandes/vit-base-patch16-224-finetuned-flower'
labels = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

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=['flower-1.jpeg', 'rosetest.jpeg.'],
                         outputs='label').launch()