esvazas/leafs-predictor

1import datasets2import torch3from transformers import AutoFeatureExtractor, AutoModelForImageClassification4 5dataset = datasets.load_dataset('beans', 'full_size')6extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")7model = AutoModelForImageClassification.from_pretrained("saved_model_files")8 9labels = dataset['train'].features['labels'].names10 11def classify(im):12  features = extractor(im, return_tensors='pt')13  logits = model(features["pixel_values"])[-1]14  probability = torch.nn.functional.softmax(logits, dim=-1)15  probs = probability[0].detach().numpy()16  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 17  return confidences18 19 20import gradio as gr21 22interface = gr.Interface(fn=classify, inputs=gr.Image(shape=(200, 200)),  outputs=gr.outputs.Label(num_top_classes=1),23             examples=["img1.jpeg", "img2.jpeg"], title='Leaf Classification App', description='Check if your image is healthy!', flagging_dir='flagged_examples/')24 25interface.launch(debug=True)