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import torch |
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from datasets import load_dataset |
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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dataset = load_dataset('beans') |
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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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labels = dataset['train'].features['labels'].names |
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def classify(im): |
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features = feature_extractor(im, return_tensors='pt') |
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inp = model(**features) |
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logits = torch.nn.functional.softmax(inp.logits, dim=-1) |
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probability = torch.nn.functional.softmax(logits, dim=-1) |
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probs = probability[0].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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import gradio as gr |
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interface = gr.Interface(fn=classify, inputs=gr.Image(shape=(224, 224)), outputs="text") |
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interface.launch(debug=True) |
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