import tensorflow as tf
import json
import pandas as pd
import time
import sys
import os

sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utils.connUtils as connUtils
import utils.configUtils as configUtils

ITEM_NUMBER_COLUMNS = ["item_"+c for c in ["smart_rank2"]]
embedding_columns = ["itemid","userid"] + ["item_"+c for c in ["doctor_id","hospital_id"]]
multi_columns = ["tags_v3","first_demands","second_demands","first_solutions","second_solutions","first_positions","second_positions"]
one_hot_columns = ["item_"+c for c in ["service_type","doctor_type","doctor_famous","hospital_city_tag_id","hospital_type","hospital_is_high_quality"]]
# history_columns = ["userRatedHistory"]

# 数据加载
# data_path_train = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
# data_path_test = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"

VERSION = configUtils.SERVICE_VERSION
trainDay = time.strftime("%Y%m%d", time.localtime())
data_path_test = "/data/files/service_feature_{}_test.csv".format(VERSION)
model_file = configUtils.SERVICE_MODEL_PATH + "/" + trainDay

def is_float(s):
    try:
        float(s)
        return True
    except ValueError:
        return False

#数据字典
def getDataVocabFromRedis(version):
    conn = connUtils.getRedisConn()
    key = "Strategy:rec:vocab:service:"+version
    dataVocabStr = conn.get(key)
    if dataVocabStr:
        dataVocab = json.loads(str(dataVocabStr, encoding="utf-8"),encoding='utf-8')
        print("-----data_vocab-----")
        for k, v in dataVocab.items():
            print(k, len(v))
    else:
        dataVocab = None

    return dataVocab

# 数据类型转换
def csvTypeConvert(columns,df,data_vocab):
    df["label"] = df["label"].astype("int")
    for k in columns:
        # 离散na值填充
        if data_vocab.get(k):
            df[k] = df[k].fillna("-1")
            df[k] = df[k].astype("string")
        elif k != "label":
            # df[k] = df[k].map(lambda x:x if is_float(x) else 0)
            df[k] = df[k].fillna(0)
            df[k] = df[k].astype("float")
    # print(df.dtypes)
    return df

def loadData(data_path):
    print("读取数据...")
    timestmp1 = int(round(time.time() * 1000))
    df = pd.read_csv(data_path, sep="|")
    timestmp2 = int(round(time.time() * 1000))
    print("读取数据耗时ms:{}".format(timestmp2 - timestmp1))
    return df


def getTrainColumns(train_columns,data_vocab):
    emb_columns = []
    number_columns = []
    oneHot_columns = []
    dataColumns = []
    inputs = {}
    # 离散特征
    for feature in train_columns:
        if data_vocab.get(feature):
            if feature.count("__")>0:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
                col = tf.feature_column.embedding_column(cat_col, 5)
                emb_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
            elif feature in one_hot_columns or feature.count("Bucket") > 0:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
#                 col = tf.feature_column.indicator_column(cat_col)
                col = tf.feature_column.embedding_column(cat_col, 3)
                oneHot_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
            else:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
                col = tf.feature_column.embedding_column(cat_col, 10)
                emb_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')

        elif feature in ITEM_NUMBER_COLUMNS:
            col = tf.feature_column.numeric_column(feature)
            number_columns.append(col)
            dataColumns.append(feature)
            inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='float32')


    return emb_columns,number_columns,oneHot_columns,dataColumns,inputs


def test(df_train,n=100):
    import requests

    # ddd = {}
    # datasColumnss = df_train.columns.to_list()
    # dd = df_train.sample(n=n)
    # for c in datasColumnss:
    #     vvv = dd[c].tolist()
    #     ddd[c] = vvv

    # # print(ddd)
    # pre_data = {"inputs":ddd}
    # pre_data = json.dumps(pre_data)
    # pre_data = pre_data.replace("'",'"')
    # print(pre_data)
    # print("测试样本数：{},测试耗时ms:{}".format(n,int(round(time.time()*1000))- timestmp1))
    # print(r)

    for i in range(100):
        ddd = {}
        datasColumnss = df_train.columns.to_list()
        dd = df_train.sample(n=n)
        for c in datasColumnss:
            vvv = dd[c].tolist()
            ddd[c] = vvv

        # print(ddd)

        pre_data = {"inputs": ddd}
        pre_data = json.dumps(pre_data)
        # pre_data = pre_data.replace("'",'"')
        # print(pre_data)
        timestmp1 = int(round(time.time() * 1000))
        r = requests.post('http://tensorserving.paas-develop.env/v1/models/service:predict', data=pre_data)
        print("测试样本数：{},测试耗时ms:{}".format(n, int(round(time.time() * 1000)) - timestmp1))
        print(r)

if __name__ == '__main__':
    n = int(sys.argv[1])

    # redis中加载数据字典
    print("redis 中加载模型字典...")
    data_vocab = getDataVocabFromRedis(VERSION)
    assert data_vocab

    print("读取数据...")
    timestmp1 = int(round(time.time()))
    df_test = loadData(data_path_test)
    timestmp2 = int(round(time.time()))
    print("读取数据耗时s:{}".format(timestmp2 - timestmp1))

    # 获取训练列
    columns = df_test.columns.tolist()
    print(columns)
    emb_columns,number_columns,oneHot_columns, datasColumns,inputs = getTrainColumns(columns, data_vocab)

    df_test = df_test[datasColumns + ["label"]]

    # 数据类型转换
    df_test = csvTypeConvert(datasColumns,df_test,data_vocab)

    test(df_test,n)