| | |
| | """app_creation.ipynb |
| | |
| | Automatically generated by Colaboratory. |
| | |
| | Original file is located at |
| | https://colab.research.google.com/drive/1sguK4GohScbDFj7Toyodw7faqhPrfc1a |
| | """ |
| |
|
| | import json |
| | import torch |
| | import gradio as gr |
| | import onnxruntime as rt |
| | from transformers import AutoTokenizer |
| |
|
| | tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") |
| |
|
| | with open("encoded_keywords.json", "r") as fp: |
| | encode_keywords = json.load(fp) |
| |
|
| | keywords = list(encode_keywords.keys()) |
| |
|
| | inf_session = rt.InferenceSession('bert_quantized.onnx') |
| | input_name = inf_session.get_inputs()[0].name |
| | output_name = inf_session.get_outputs()[0].name |
| |
|
| | def classify_keywords(abstract): |
| | input_ids = tokenizer(abstract)['input_ids'][:512] |
| |
|
| | logits = inf_session.run([output_name], { |
| | input_name: [input_ids] |
| | })[0] |
| |
|
| | logits = torch.FloatTensor(logits) |
| |
|
| | probs = torch.sigmoid(logits)[0] |
| |
|
| | return dict(zip(keywords, map(float, probs))) |
| |
|
| | label = gr.Label(num_top_classes = 5) |
| |
|
| | interface = gr.Interface( |
| | fn = classify_keywords, |
| | inputs = "text", |
| | outputs = label |
| | ) |
| |
|
| | interface.launch() |
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