app.py

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



dataset = load_dataset('beans', 'full_size')

extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
model = AutoModelForImageClassification.from_pretrained("saved_model_files")

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


description = "Bean leaf health classification wit Google's ViT"
title = "Bean Leaf Health Check"
examples = [["'angular_leaf_spot': 0.9999030828475952, 'bean_rust': 5.320278796716593e-05, 'healthy': 4.378804806037806e-05"]]


gr_interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean Classification', description='Monitor your crops health in easier way')


gr_interface.launch(debug=True)