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| import gradio as gr | |
| import re | |
| import torch | |
| from transformers import pipeline | |
| import os | |
| AUTH_TOKEN = os.environ["AUTH_TOKEN"] | |
| cate_classifier = pipeline('text-classification', | |
| model="djsull/kobigbird-cate-class-finder", | |
| use_auth_token=AUTH_TOKEN, | |
| return_all_scores=True, | |
| function_to_apply='softmax', | |
| ) | |
| def predict(text): | |
| query = text | |
| cleanr = re.compile('<.*?>') | |
| query = re.sub(cleanr, '', query) | |
| query = ' '.join(re.sub('[^가-힣a-zA-Z0-9 ]', ' ', query).split()) | |
| result = cate_classifier(text)[0] | |
| ress = {} | |
| ch = 0 | |
| chch = 0 | |
| for i in range(len(result)): | |
| if result[i]['score'] >= ch: | |
| ch = result[i]['score'] | |
| chch = i | |
| text_tmp = result[chch]["label"] | |
| ress[text_tmp] = int(result[chch]["score"] * 10000) / 100 | |
| return ress | |
| gr.Interface( | |
| predict, | |
| inputs=gr.inputs.Textbox(label="Type anything"), | |
| outputs=gr.outputs.Textbox(label="labels"), | |
| title="Single-label Category classification", | |
| ).launch() |