| 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(intent, cand1, cand2, cand3, cand4, cand5): | |
| 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 | |
| demo = gr.Interface(fn=cal_sim, | |
| inputs=[gr.components.Textbox(label="User query"), | |
| gr.components.Textbox(label="candidate01"), | |
| gr.components.Textbox(label="candidate02"), | |
| gr.components.Textbox(label="candidate03"), | |
| gr.components.Textbox(label="candidate04"), | |
| gr.components.Textbox(label="candidate05"), | |
| ], | |
| outputs=gr.components.Label()) | |
| if __name__ == "__main__": | |
| demo.launch(share=True, debug=True) | |