File size: 1,359 Bytes
eb66dcb f631f81 eb66dcb 5916e02 f631f81 eb66dcb 8863a1e eb66dcb 8863a1e eb66dcb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from utils.convert_embedding import GetEmbedding
import random
import pickle
import os
# def dump_user_question(query):
# try:
# if os.path.exists:
# with open(r"data\question_data.pkl","rb") as f:
# que = pickle.load(f)
# else:
# que = []
# que.append(query)
# with open(r"data\question_data.pkl","wb") as f:
# que = pickle.dump(que,f)
# except:
# with open(r"data\question_data.pkl","wb") as f:
# pickle.dump([],f)
def process(user_query:str):
# dump_user_question(user_query)
user_embedding = GetEmbedding([user_query]).user_query_emb()
with open(r"question_embedding_latest.pkl","rb") as f:
load_embedding = pickle.load(f)
with open(r"answer.pkl","rb") as f:
ans = pickle.load(f)
similarity_scores = cosine_similarity(user_embedding, load_embedding)
index = np.argmax(similarity_scores)
answer = ans[index]
return random.choice(answer)
if __name__ == "__main__":
pass
# 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) |