| import transformers | |
| import datasets | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| dataset = load_dataset('beans') | |
| extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") | |
| model = AutoModelForImageClassification.from_pretrained("saved_model_files") | |
| labels = dataset['train'].features['labels'].names | |
| example_imgs = ["example_0.jpg", "example_1.jpg","example_2.jpg"] | |
| 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 | |
| import gradio as gr | |
| interface = gr.Interface(classify, inputs='image', | |
| outputs='label', | |
| title='Bean Classification', | |
| description='Check the health of your bean leaves', | |
| examples = example_imgs) | |
| interface.launch(debug=True) |