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import gradio as gr | |
from sentence_transformers import SentenceTransformer | |
model_name = "BAAI/bge-large-zh-v1.5" | |
model = SentenceTransformer(model_name, device="cpu") | |
def cal_sim(*args): | |
intent = args[0] | |
cand_list = args[1:] | |
# cand_list = [cand1, cand2, cand3, cand4, cand5] | |
cand_list = [cand for cand in cand_list if cand] | |
embeddings_1 = model.encode([intent], normalize_embeddings=True) | |
embeddings_2 = model.encode(cand_list, normalize_embeddings=True) | |
similarity = embeddings_1 @ embeddings_2.T | |
similarity = similarity[0] | |
sim_output = {} | |
for i, sim in zip(cand_list, similarity): | |
if i: | |
sim_output[i] = float(sim) | |
return sim_output | |
inputs = [ | |
gr.components.Textbox(label="User query"), | |
] | |
candidate_box = [gr.components.Textbox(label=f"candidate_{i}") for i in range(30)] | |
inputs.extend(candidate_box) | |
demo = gr.Interface( | |
fn=cal_sim, | |
inputs=inputs, | |
outputs=gr.components.Label(), | |
) | |
if __name__ == "__main__": | |
demo.launch(share=True, debug=True) | |