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

description = "Example bean leaf health classification app using a fine-tuned ViT model"
title_string = "What's up with my leaf?"

dataset = datasets.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 = extractor(im, return_tensors='pt')
  encoding = model(**features)
  logits = torch.nn.functional.softmax(encoding.logits, dim=-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


example_paths = ['angular-leaf-spot-1.JPG', 'bean-rust.jpg']

interface = gr.Interface(
    classify,
    gr.inputs.Image(),
    gr.outputs.Label(num_top_classes=3),
    description=description,
    examples=example_paths,
    title = title_string

)

interface.launch()