app.py

import gradio as gr
from datasets import load_dataset
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 = 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
  
interface = gr.Interface(
    title="Plant Health Identifier",
    description="Upload an image of plant leaf to determine if it is infected or healthy",
    #examples= ['beanrot.jpeg'],
    fn=classify, 
    inputs="image", 
    outputs="label",
    )
interface.launch(debug=True)