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import gradio as gr |
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from sentence_transformers import SentenceTransformer |
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model_name = "BAAI/bge-large-zh-v1.5" |
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model = SentenceTransformer(model_name, device="cpu") |
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def cal_sim(*args): |
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intent = args[0] |
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cand_list = args[1:] |
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cand_list = [cand for cand in cand_list if cand] |
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embeddings_1 = model.encode([intent], normalize_embeddings=True) |
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embeddings_2 = model.encode(cand_list, normalize_embeddings=True) |
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similarity = embeddings_1 @ embeddings_2.T |
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similarity = similarity[0] |
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sim_output = {} |
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for i, sim in zip(cand_list, similarity): |
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if i: |
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sim_output[i] = float(sim) |
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return sim_output |
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inputs = [ |
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gr.components.Textbox(label="User query"), |
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] |
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candidate_box = [gr.components.Textbox(label=f"candidate_{i}") for i in range(30)] |
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inputs.extend(candidate_box) |
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demo = gr.Interface( |
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fn=cal_sim, |
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inputs=inputs, |
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outputs=gr.components.Label(), |
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) |
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if __name__ == "__main__": |
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demo.launch(share=True, debug=True) |
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