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)