import json
import os
from datetime import datetime, timedelta, timezone

import boto3
import pandas as pd
from classify import classify
from tabulate import tabulate
from users import load_users

S3_BUCKET = os.environ["S3_BUCKET"]


def upload(s3, table, filename):
    table.to_csv(f"/tmp/{filename}", index=False)
    try:
        s3.upload_file(f"/tmp/{filename}", S3_BUCKET, f"data/classes/{filename}")
        print(f"uploaded {filename}.")
    except Exception as e:
        print(e)


def class_1_1(u):
    return u.is_business_email() or u.count_event("PoC Form Submission") > 0 or u.has_bind_card()


def class_1_2(u):
    mails = [
        "gmail.com",
        "msn.com",
        "outlook.com",
        "hotmail.com",
        "yahoo.com",
        "foxmail.com",
        "yopmail.com",
        "icloud.com",
        "protonmail.com",
    ]
    return not class_1_1(u) and u.email().split("@")[1] in mails


def class_1_3(u):
    return not class_1_1(u) and not class_1_2(u)


def lambda_handler(event, context):
    # begin = datetime(2022, 7, 1, tzinfo=timezone.utc)
    # end = datetime(event['year'], event['month'], event['day'], tzinfo=timezone.utc)
    end = datetime.now(timezone.utc)
    begin = end - timedelta(days=90)
    if "kwargs" in event:
        end = datetime.fromtimestamp(event["kwargs"]["time"], tz=timezone.utc)

    users = load_users(begin)
    users = [u for u in users if not u.is_pingcap()]
    users = [u for u in users if u.signup_time() > begin]

    class_1 = [u for u in users if classify(u) == "first class"]
    class_2 = [u for u in users if classify(u) == "second class"]
    class_3 = [u for u in users if classify(u) == "third class"]
    class_4 = [u for u in users if classify(u) == "others"]

    table_1_1 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.owner_id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                "Y" if u.count_event("PoC Form Submission") > 0 else "N",
                "Y" if u.has_bind_card() else "N",
                u.region(),
                u.country(),
            ]
            for u in class_1
            if class_1_1(u)
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "PoC Form Submission",
            "Has Bind Card",
            "Region",
            "Country",
        ],
    )

    table_1_2 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.owner_id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                "Y" if u.count_event("PoC Form Submission") > 0 else "N",
                "Y" if u.has_bind_card() else "N",
                u.region(),
                u.country(),
            ]
            for u in class_1
            if class_1_2(u)
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "PoC Form Submission",
            "Has Bind Card",
            "Region",
            "Country",
        ],
    )

    table_1_3 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.owner_id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                "Y" if u.count_event("PoC Form Submission") > 0 else "N",
                "Y" if u.has_bind_card() else "N",
                u.region(),
                u.country(),
            ]
            for u in class_1
            if class_1_3(u)
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "PoC Form Submission",
            "Has Bind Card",
            "Region",
            "Country",
        ],
    )

    table_2 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                u.region(),
                u.country(),
            ]
            for u in class_2
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "Region",
            "Country",
        ],
    )

    table_3 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.owner_id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                u.region(),
                u.country(),
            ]
            for u in class_3
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "Region",
            "Country",
        ],
    )

    table_4 = pd.DataFrame(
        [
            [
                u.email(),
                str(u.id()),
                u.owner_id(),
                u.signup_time().date(),
                u.alive().total_seconds() / 24 / 60 / 60,
                u.event_types(),
                u.first_cluster_create_time().date()
                if u.first_cluster_create_time() is not None
                else "",
                u.clusters_alive().total_seconds() / 24 / 60 / 60,
                u.region(),
                u.country(),
            ]
            for u in class_4
        ],
        columns=[
            "Email",
            "Tenant ID",
            "Owner",
            "Signup Time",
            "DBaaS Trial (day)",
            "Event Types",
            "Cluster Create",
            "Clusters Trial (day)",
            "Region",
            "Country",
        ],
    )

    _date = end.date() - timedelta(days=1)

    s3 = boto3.client("s3")
    upload(s3, table_1_1, f"class-1-1-{_date}.csv")
    upload(s3, table_1_2, f"class-1-2-{_date}.csv")
    upload(s3, table_1_3, f"class-1-3-{_date}.csv")
    upload(s3, table_2, f"class-2-{_date}.csv")
    upload(s3, table_3, f"class-3-{_date}.csv")
    upload(s3, table_4, f"class-4-{_date}.csv")

    return {
        "statusCode": 200,
        "body": json.dumps("classfied users are uploaded!"),
    }