app.py

import datasets
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import gradio as gr


dataset = datasets.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(fn = classify, inputs = "image", outputs = "label",
                         title = "Plant health classifier",
                         description = "Classifies plant health"
                         )

interface.launch()