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| from datasets import load_dataset | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| # This should be the same as the first line of Python code in this Colab notebook | |
| dataset = 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 = feature_extractor(im, return_tensors='pt') | |
| inp = model(**features) | |
| logits = torch.nn.functional.softmax(inp.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 | |
| import gradio as gr | |
| interface = gr.Interface(fn=classify, inputs='image', outputs="text") | |
| interface.launch(debug=True) | |