import datasets import gradio as gr import torch from transformers import AutoFeatureExtractor, AutoModelForImageClassification dataset = datasets.load_dataset('beans') extractor = AutoFeatureExtractor.from_pretrained("tadeyina/BeanLeaf") model = AutoModelForImageClassification.from_pretrained("tadeyina/BeanLeaf") labels = dataset['train'].features['labels'].names def classify(im): features = 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(classify, inputs='image', outputs='label', title='Bean plant disease classifier',description='Detect diseases in beans using images of leaves') interface.launch()