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create app.py
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app.py
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import datasets
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification
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import numpy as np
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import gradio as gr
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dataset = load_dataset("beans") # This should be the same as the first line of Python code in this Colab notebook
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extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
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model = AutoModelForImageClassification.from_pretrained("saved_model_files")
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# add to cuda?
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model.eval()
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model.to(device)
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labels = dataset['train'].features['labels'].names
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def classify(im):
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features = extractor(im, return_tensors='pt')
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features.to(device) # move to gpu as model, if available
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with torch.no_grad():
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logits = model(**features).logits
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probability = torch.nn.functional.softmax(logits, dim=-1)
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probs = probability[0].to('cpu').detach().numpy()
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confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
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return confidences
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interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'text')
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#demo.launch()
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interface.launch(debug=False)
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