| import torch | |
| import datasets | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| import gradio as gr | |
| description = "Example bean leaf health classification app using a fine-tuned ViT model" | |
| title_string = "What's up with my leaf?" | |
| 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') | |
| encoding = model(**features) | |
| logits = torch.nn.functional.softmax(encoding.logits, dim=-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 | |
| example_paths = ['angular-leaf-spot-1.JPG', 'bean-rust.jpg'] | |
| interface = gr.Interface( | |
| classify, | |
| gr.inputs.Image(), | |
| gr.outputs.Label(num_top_classes=3), | |
| description=description, | |
| examples=example_paths, | |
| title = title_string | |
| ) | |
| interface.launch() | |