app.py

import transformers
import gradio as gr
import datasets
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
# from transformers import ViTFeatureExtractor, ViTForImageClassification

dataset = datasets.load_dataset('beans')

extractor = AutoFeatureExtractor.from_pretrained("suresh-subramanian/beans-classification")
model = AutoModelForImageClassification.from_pretrained("suresh-subramanian/beans-classification")
# feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

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
  
  # examples = [["powdery mildew.jpg"], ["375010.jpg"]]
# Set gradio interface
gr_interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean Classification', description='Monitor your crops health in easier way')
# Launch gradio
gr_interface.launch(debug=True)