# -*- coding: utf-8 -*-
"""app

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1XX8pCT291obpzL4fc1vu5L_HTG027lle
"""
import gradio as gr
import torch
import datasets
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

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')
  with torch.no_grad():
    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

interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean plant disease classifier', description='Detect diseases in beans leaves using their images.', examples=['bean-plant-example.jpeg', 'non-bean-leaf-example.jpeg'])

interface.launch(debug=False)