app.py

import datasets
import transformers
import torch
import gradio as gr
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")
model = model.eval()  # set to eval mode for predictions

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
  
# gradio app
interface = gr.Interface(fn = classify,
                         inputs = "image",
                         outputs = "label",
                         title = "Leaf Health Classifier",
                         examples =["beans1.jpg", "beans2.jpg", "beans3.jpg"],
                         description = "A fine tuned ViT based image classfier which returns the health of a bean leaf")

interface.launch(debug=True)