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Create app.py
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app.py
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# -*- coding: utf-8 -*-
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"""
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@author:XuMing(xuming624@qq.com)
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@description: text similarity example, fine-tuned by CoSENT model
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"""
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import gradio as gr
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from similarities import Similarity
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# 中文句向量模型
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sim_model = Similarity(model_name_or_path='shibing624/text2vec-base-chinese')
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def load_file(path):
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with open(path, 'r', encoding='utf-8') as f:
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return f.read().split('\n')
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sim_model.add_corpus(load_file('corpus.txt'))
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def ai_text(query):
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res = sim_model.most_similar(queries=query, topn=5)
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print(res)
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for q_id, c in res.items():
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print('query:', query)
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print("search top 5:")
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for corpus_id, s in c.items():
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print(f'\t{sim_model.corpus[corpus_id]}: {s:.4f}')
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res_show = '\n'.join(['search top5:'] + [f'text: {sim_model.corpus[corpus_id]} score: {s:.4f}' for corpus_id, s in
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list(res.values())[0].items()])
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return res_show
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if __name__ == '__main__':
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examples = [
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['星巴克被嘲笑了'],
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['西班牙失业率超过50%'],
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['她在看书'],
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['一个人弹琴'],
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]
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input = gr.inputs.Textbox(lines=2, placeholder="Enter Query")
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output_text = gr.outputs.Textbox()
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gr.Interface(ai_text,
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inputs=[input],
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outputs=[output_text],
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theme="grass",
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title="Chinese Text Semantic Search Model",
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description="Copy or input Chinese text here. Submit and the machine will find the most similarity texts.",
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article="Link to <a href='https://github.com/shibing624/similarities' style='color:blue;' target='_blank\'>Github REPO</a>",
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examples=examples
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).launch()
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