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from sentence_transformers import SentenceTransformer, util |
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import gradio as gr |
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bert_model = SentenceTransformer('all-MiniLM-L6-v2') |
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def similar_headline(headline_1, headline_2): |
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headline_embedding_1 = bert_model.encode(headline_1) |
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headline_embedding_2 = bert_model.encode(headline_2) |
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bert_similarities = util.pytorch_cos_sim(headline_embedding_1, headline_embedding_2) |
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similarities_percent = bert_similarities * 100 |
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if bert_similarities > 0.7: |
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result = f"similar: {similarities_percent[0][0]}" |
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else: |
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result = f"not similar: {similarities_percent[0][0]}" |
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return result |
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demo = gr.Interface(similar_headline, inputs=[gr.inputs.Textbox(label="Input the first headline here"), |
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gr.inputs.Textbox(label="Input the second headline here")], |
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outputs = "text", |
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title="News Headline Similarities") |
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if __name__ == '__main__': |
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demo.launch(debug=True) |
