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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 |
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import json |
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tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") |
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with open("dataset_types_encoded.json", "r") as fp: |
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encode_category_types = json.load(fp) |
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categories = list(encode_category_types.keys()) |
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inf_session = rt.InferenceSession('dataset-classifier-distilroberta-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_dataset_type(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(categories, map(float, probs))) |
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label = gr.outputs.Label(num_top_classes=3) |
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iface = gr.Interface(fn=classify_dataset_type, inputs="text", outputs=label) |
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iface.launch(inline=False, share=True) |
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