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import gradio as gr
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import onnxruntime as rt
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from transformers import AutoTokenizer
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import torch, json
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tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
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with open("genre_types_encoded.json", "r") as fp:
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encode_genre_types = json.load(fp)
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genres = list(encode_genre_types.keys())
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inf_session = rt.InferenceSession('book-classifier-quantized.onnx')
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input_name = inf_session.get_inputs()[0].name
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output_name = inf_session.get_outputs()[0].name
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def classify_book_genre(description):
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input_ids = tokenizer(description)['input_ids'][:512]
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logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
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logits = torch.FloatTensor(logits)
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probs = torch.sigmoid(logits)[0]
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return dict(zip(genres, map(float, probs)))
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label = gr.outputs.Label(num_top_classes=5)
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iface = gr.Interface(fn=classify_book_genre, inputs="text", outputs=label)
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iface.launch(inline=False)
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