88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
import mimetypes
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from collections import defaultdict
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from urllib.parse import quote
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import pandas as pd
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import numpy as np
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from fastapi import APIRouter, Depends, Request
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from motor.motor_asyncio import AsyncIOMotorDatabase
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from starlette.responses import StreamingResponse
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import crud, schemas
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from common import *
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from api import deps
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from db import get_database
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from db.ckdb import get_ck_db, CKDrive
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from db.redisdb import get_redis_pool, RedisDrive
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from models.behavior_analysis import BehaviorAnalysis
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from models.user_analysis import UserAnalysis
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from models.x_analysis import XAnalysis
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from utils import DfToStream
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router = APIRouter()
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@router.post("/ltv_model_sql")
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async def ltv_model_sql(
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request: Request,
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game: str,
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analysis: XAnalysis = Depends(XAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" ltv模型sql """
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await analysis.init()
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data = analysis.ltv_model_sql()
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return schemas.Msg(code=0, msg='ok', data=[data])
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@router.post("/ltv_model")
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async def ltv_model_sql(
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request: Request,
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game: str,
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analysis: XAnalysis = Depends(XAnalysis),
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ckdb: CKDrive = Depends(get_ck_db),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" ltv模型sql """
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await analysis.init()
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res = analysis.ltv_model_sql()
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sql = res['sql']
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quota = res['quota']
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df = await ckdb.query_dataframe(sql)
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df.fillna(0, inplace=True)
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df.rename(columns={'date': '注册日期'}, inplace=True)
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if quota == '#account_id':
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df.rename(columns={'cnt1': '角色数'}, inplace=True)
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elif quota in ('#distinct_id',):
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df.rename(columns={'cnt1': '设备数'}, inplace=True)
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data = {
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'title': df.columns.tolist(),
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'rows': df.values.tolist()
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}
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return schemas.Msg(code=0, msg='ok', data=data)
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@router.post("/ltv_model_export")
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async def ltv_model_export(request: Request,
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game: str,
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ckdb: CKDrive = Depends(get_ck_db),
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analysis: XAnalysis = Depends(XAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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):
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""" ltv分析 数据导出"""
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await analysis.init()
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data = analysis.ltv_model_sql()
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file_name = quote(f'lvt.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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sql = data['sql']
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df = await ckdb.query_dataframe(sql)
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df_to_stream = DfToStream((df, 'ltv'))
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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