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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
# 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"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]
print(f"{index}:\t {user_query}")
score = similarity_scores[0,index]
if score > 50 :
final_output = random.choice(answer)
else:
final_output = "Sorry, I didn't understand."
return final_output
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) |