import datasets from transformers import AutoFeatureExtractor, AutoModelForImageClassification import gradio as gr import torch 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 = 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 = 'Leaf Classication', description = "Many farmers are turning machine learning to monitor their crops automatically with great accuracy. This application can be used to detect Angular Leaf Spot and Bean Rust!", examples = [["bean_rust.jpeg"] , ["healthy.jpeg"] , ["angular_leaf_spot.jpeg"]] ) interface.launch()