BrandCultivation/api/recommend.py

from database import MySqlDao
from fastapi import APIRouter, BackgroundTasks
from .request_body import RecommendRequest

from models import Recommend
import os
from utils import FileStreamUtils, ReportUtils

dao = MySqlDao()

router = APIRouter()

@router.post("/recommend")
async def recommend(request: RecommendRequest, backgroundTasks: BackgroundTasks):
    """推荐接口"""
    gbdtlr_model_path = os.path.join("./models/rank/weights", request.city_uuid, "gbdtlr_model.pkl")
    if not os.path.exists(gbdtlr_model_path):
        return {"code": 200, "msg": "model not defined", "data": {"recommendationInfo": "该城市的模型未训练，请先进行训练"}}
    
    # 初始化模型
    recommend_model = Recommend(request.city_uuid)
    
    # 判断该品规是否是新品规
    products_in_oreder = dao.get_product_from_order(request.city_uuid)["product_code"].unique().tolist()
    if request.product_code in products_in_oreder:
        recommend_list = recommend_model.get_recommend_list_by_gbdtlr(request.product_code, recall_count=request.recall_cust_count)
    else:
        print("走这了")
        recommend_list = recommend_model.get_recommend_list_by_item2vec(request.product_code, recall_count=request.recall_cust_count)
    recommend_data = recommend_model.get_recommend_and_delivery(recommend_list, delivery_count=request.delivery_count)
    request_data = []
    for index, data in enumerate(recommend_data):
        id = index + 1
        request_data.append(
            {
                "id": id,
                "cust_code": data["cust_code"],
                "recommend_score": data["recommend_score"],
                "delivery_count": data["delivery_count"]
            }
        )
    
    # 异步执行报告生成任务
    backgroundTasks.add_task(
        generate_and_upload_report,
        request
    )
    
    return {"code": 200, "msg": "success", "data": {"recommendationInfo": request_data}}

def generate_and_upload_report(request: RecommendRequest):
    """生成并上传报告到阿里云文件数据库"""
    # 生成相关报告
    report_util = ReportUtils(request.city_uuid, request.product_code)
    report_util.generate_all_data(request.recall_cust_count, request.delivery_count)
    
    # 上传报告
    reports_dir = os.path.join('./data/reports', request.city_uuid, request.product_code)
    report_files = [
        '卷烟信息表',
        '品规商户特征关系表',
        '相似卷烟表',
        '商户售卖推荐表'
    ]
    file_id_map = FileStreamUtils.upload_files(reports_dir, report_files)
    
    # 将返回的file_id保存到数据库中
    data_dict = {
        'cultivacation_id': request.cultivacation_id,
        'city_uuid': request.city_uuid,
        'limit_cycle_name': request.limit_cycle_name,
        'product_code': request.product_code,
        'product_info_table': file_id_map.get('卷烟信息表'),
        'relation_table': file_id_map.get('品规商户特征关系表'),
        'similarity_product_table': file_id_map.get('相似卷烟表'),
        'recommend_table': file_id_map.get('商户售卖推荐表'),
    }
    dao.insert_report(data_dict)