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| import datasets | |
| import gradio as gr | |
| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| dataset = datasets.load_dataset('beans', 'full_size') | |
| 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=gr.Image(shape=(200, 200)), outputs=gr.outputs.Label(num_top_classes=3), | |
| examples=['leaf1.png', 'leaf2.png', 'leaf3.jpg', 'leaf4.jpg'], title='Leaf Classification App', description='Check if the leaves of your plant are healthy!', flagging_dir='flagged_examples/') | |
| interface.launch(debug=True) |