app.py

import datasets
from datasets import load_dataset, Image
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

dataset = load_dataset("beans")

extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
model = AutoModelForImageClassification.from_pretrained("saved_model_files")

labels = dataset['train'].features['labels'].names

def classify(im):
  features = image_processor(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


interface = gr.Interface(
    fn=classify, 
    inputs="image", 
    outputs="label"
  )

interface.launch(share=True, debug=True)