app.py

import datasets
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

dataset =  datasets.load_dataset('beans') # This should be the same as the first line of Python code in this Colab notebook

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

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

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

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

interface.launch(debug=True)