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# import numpy as np
# from sklearn.metrics.pairwise import cosine_similarity
# from utils.convert_embedding import GetEmbedding
# import random
# import pickle
# import os
# from utils.rag import RAG
# from faster_whisper import WhisperModel
# def process(user_query:str):
# # dump_user_question(user_query)
# user_embedding = GetEmbedding([user_query]).user_query_emb()
# with open(r"all_mix_embedding.pkl","rb") as f:
# load_embedding = pickle.load(f)
# with open(r"all_answers.pkl","rb") as f:
# ans = pickle.load(f)
# similarity_scores = cosine_similarity(user_embedding, load_embedding)
# index = np.argmax(similarity_scores)
# answer = ans[index]
# score = similarity_scores[0,index]
# print(f"Index : {index}:\tscore:{score} \tquery: {user_query}")
# if float(score) > 0.60 :
# final_output = random.choice(answer)
# else:
# final_output = RAG().pipeline(query=user_query)
# return final_output
# def audio_process(audio):
# try:
# model = WhisperModel(model_size_or_path="medium.en")
# segments, info = model.transcribe(audio)
# transcription = " ".join([seg.text for seg in segments])
# result = process(user_query=transcription)
# return result
# except Exception as e:
# print("Error:", e)
# return str(e)
# if __name__ == "__main__":
# res = audio_process(r"C:\Users\lalit\Documents\Sound recordings\who_is_lalit.m4a")
# print(res)
# # for _ in range(3):
# # user = input("How can i help you :? \n")
# # result = process(user)
# # print(result)
# # with open(r"data\question_data.pkl","rb") as f:
# # que = pickle.load(f)
# # print(que) |