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import numpy as np |
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from sklearn.metrics.pairwise import cosine_similarity |
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from utils.convert_embedding import GetEmbedding |
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import random |
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import pickle |
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import os |
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user_query = "hi" |
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def dump_user_question(query): |
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try: |
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if os.path.exists: |
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with open(r"data\question_data.pkl","rb") as f: |
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que = pickle.load(f) |
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else: |
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que = [] |
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que.append(query) |
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with open(r"data\question_data.pkl","wb") as f: |
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que = pickle.dump(que,f) |
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except: |
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with open(r"data\question_data.pkl","wb") as f: |
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pickle.dump([],f) |
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def process(user_query): |
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dump_user_question(user_query) |
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user_embedding = GetEmbedding([user_query]).user_query_emb() |
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with open(r"data\question_embedding_latest.pkl","rb") as f: |
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load_embedding = pickle.load(f) |
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with open(r"data\answer.pkl","rb") as f: |
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ans = pickle.load(f) |
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similarity_scores = cosine_similarity(user_embedding, load_embedding) |
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index = np.argmax(similarity_scores) |
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answer = ans[index] |
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return random.choice(answer) |
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if __name__ == "__main__": |
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pass |
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