import transformers
import datasets
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
example_imgs = ["example_0.jpg", "example_1.jpg","example_2.jpg"]

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',
                          title='Bean Classification', 
                          description='Check the health of your bean leaves',
                          examples = example_imgs)

interface.launch(debug=True)