import datasets
from datasets import load_dataset

import torch
import gradio as gr
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 = 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

interface = gr.Interface(
    fn=classify, inputs="image", 
    outputs="label", 
    title="Bean Leaf Classifier", 
    description="An app to help us test our bean leaf classifier in the real world!",
    examples=['bean-plant-example.jpeg', 'non-bean-leaf-example.jpeg'])
interface.launch(debug=False)