import os
import sys
sys.path.append(os.getcwd())
import numpy as np
import faiss
import pickle
from datetime import datetime
from config import logger
from config import embedding_dim
from config import test_user_id
from config import subject_mind_user_emb1_dict
from config import subject_mind_user_embedding1_data
from config import subject_mind_user1_faiss_model_path
from config import subject_mind_user_embedding1_index_dict
from config import subject_mind_item_emb_dict
from config import subject_mind_item_embedding_data
from config import subject_mind_item_faiss_model_path
from config import subject_mind_item_embedding_index_dict

logger.info('加载MIND召回数据')
mind_user_emb_dict = pickle.load(open(subject_mind_user_emb1_dict, 'rb'))
mind_user_embedding_data = pickle.load(open(subject_mind_user_embedding1_data, 'rb'))
mind_user_embedding_index_dict = pickle.load(open(subject_mind_user_embedding1_index_dict, 'rb'))

mind_item_emb_dict = pickle.load(open(subject_mind_item_emb_dict, 'rb'))
mind_item_embedding_data = pickle.load(open(subject_mind_item_embedding_data, 'rb'))
mind_item_embedding_index_dict = pickle.load(open(subject_mind_item_embedding_index_dict, 'rb'))

logger.info('加载MIND召回模型')
mind_user_faiss_model = faiss.read_index(subject_mind_user1_faiss_model_path)
mind_item_faiss_model = faiss.read_index(subject_mind_item_faiss_model_path)


def mind1_recall(user_id, topk=20):
    """
    通过mind user1 embedding的相似度推荐学习伙伴
    """
    start_time = datetime.now()
    logger.info(f"本次需要进行MIND召回的用户ID: {user_id}")

    recommend_results = {}
    recommend_results.clear()    

    if (user_id not in mind_user_emb_dict):
        return recommend_results

     # 取出用户向量
    user_embs = mind_user_emb_dict[user_id]

    # reshape为二维
    user_embs = user_embs.reshape(-1, embedding_dim)

    # 找topk个相似的物品向量
    D, I = mind_item_faiss_model.search(np.ascontiguousarray(user_embs), topk + 1)

    top_k_index = list(I.ravel())
    top_k_sim = list(D.ravel())
    top_k_item = {}
    top_k_item.clear()

    # 还原相似向量索引对应的用户
    for i, index in enumerate(top_k_index):        
        cur_user_id = mind_item_embedding_index_dict[index]

        # 过滤第一个最相似的用户是自己
        if cur_user_id != user_id:
            top_k_item[cur_user_id] = top_k_sim[i]

    top_k_item = sorted(top_k_item.items(), key=lambda x: x[1], reverse=True)[:topk]

   # 计算耗时毫秒
    end_time = datetime.utcnow()
    cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)    

    logger.info(f"本次推荐总耗时: {cost_time_millisecond} 毫秒")
    return top_k_item  


if __name__ == '__main__':
    recall_results = mind1_recall(user_id=test_user_id, topk=20)
    print(recall_results)