transcription-cost-usage-report/backend/app/services/report_service.py

from flask import current_app
from datetime import datetime
from decimal import Decimal
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.styles import Font, PatternFill
import pandas as pd
import os 
from typing import List, Dict, Any, Optional 
from app.utils.mysql_query import execute_query  
from app.utils.calc_api_usage import calculate_api_usage
import math


class TranscriptionReportService:
    def __init__(self, company_id: str, start_date: str, end_date: str):
        self.company_id = str(company_id)
        self.start_date = start_date
        self.end_date = end_date
        self.end_date = end_date
        self.mongo_client = current_app.mongo_client 
        self.mongo_results = []
        self.unique_ids= []  


    def _fetch_mongo_data(self, page: int = 1, page_size: int = 20, all_data: Optional[bool] = False) -> Dict[str, int]:
        collection = self.mongo_client["billing-api"]["api_usages"]

        # Define os produtos válidos a partir da tabela de preços
        pricing_collection = self.mongo_client["billing-api"]["api_pricings"]
        result_stt = pricing_collection.find({"product": {"$nin": ["whatsapp"]}})
        products = [t["product"] for t in result_stt]

        match_stage = {
            "$match": {
                "companyId": self.company_id,
                "product": {"$in": products},
                "createdAt": {
                    "$gte": datetime.strptime(f"{self.start_date} 00:00:00", "%Y-%m-%d %H:%M:%S"),
                    "$lte": datetime.strptime(f"{self.end_date} 23:59:59", "%Y-%m-%d %H:%M:%S")
                }
            }
        }

        lookup_stage = {
            "$lookup": {
                "from": "api_pricings",
                "localField": "product",
                "foreignField": "product",
                "as": "pricing"
            }
        }

        unwind_stage = {
            "$unwind": "$pricing"
        }

        # Agrupa por sessionId + type + product
        group_stage_1 = {
            "$group": {
                "_id": {
                    "sessionId": "$sessionId",
                    "type": "$pricing.type",
                    "product": "$product"
                },
                "usage": {"$sum": {"$toDouble": "$usage"}},
                "totalCost": {"$sum": {"$toDouble": "$total_cost"}},
                "callerIds": {"$addToSet": "$callerId"},
                "firstCreatedAt": {"$min": "$createdAt"}
            }
        }

        # Agrupa por sessionId final, montando maps de uso e custo
        group_stage_2 = {
            "$group": {
                "_id": "$_id.sessionId",
                "count": {"$sum": 1},
                "firstCreatedAt": {"$first": "$firstCreatedAt"},
                "callerIds": {"$first": "$callerIds"},
                "totalCost": {"$sum": "$totalCost"},
                "usageByType": {
                    "$push": {
                        "k": "$_id.type",
                        "v": "$usage"
                    }
                },
                "costByType": {
                    "$push": {
                        "k": "$_id.type",
                        "v": "$totalCost"
                    }
                },
                "usageByProduct": {
                    "$push": {
                        "k": "$_id.product",
                        "v": "$usage"
                    }
                }
            }
        }

        project_stage = {
            "$project": {
                "count": 1,
                "firstCreatedAt": 1,
                "callerIds": 1,
                "totalCost": 1,
                "usageByType": {"$arrayToObject": "$usageByType"},
                "costByType": {"$arrayToObject": "$costByType"},
                "usageByProduct": {"$arrayToObject": "$usageByProduct"}
            }
        }

        sort_stage = {"$sort": {"firstCreatedAt": 1}}

        # Monta o pipeline
        pipeline = [
            match_stage,
            lookup_stage,
            unwind_stage,
            group_stage_1,
            group_stage_2,
            project_stage,
            sort_stage
        ]

        if not all_data:
            pipeline.extend([
                {"$skip": (page - 1) * page_size},
                {"$limit": page_size}
            ])

        # Executa agregação principal
        self.mongo_results = list(collection.aggregate(pipeline))
        self.unique_ids = [doc["_id"] for doc in self.mongo_results]

        # print("=====> self.mongo_results: ", self.mongo_results)

        # Pipeline para contagem total
        count_pipeline = [
            match_stage,
            lookup_stage,
            unwind_stage,
            group_stage_1,
            group_stage_2,
            {"$count": "total"}
        ]
        count_result = list(collection.aggregate(count_pipeline))
        total = count_result[0]["total"] if count_result else 0

        return {
            "total": total,
            "page": page,
            "page_size": page_size,
            "total_pages": (total + page_size - 1) // page_size
        }





    def _fetch_mysql_data(self,  hit_report: Optional[bool] = False)-> List[Dict[str, Any]]:

        collection = self.mongo_client["billing-api"]["api_products"]

        products = list(collection.find({})) 

        sql = f"""SELECT 
                    uniqueid,
                    src,
                    dst,
                    MIN(calldate) AS start_call,
                    MAX(calldate) AS end_call,
                    SUM(CASE 
                            WHEN dstchannel LIKE 'PJSIP/%' AND lastapp = 'Queue' 
                            THEN billsec 
                            ELSE 0 
                        END) AS total_billsec
                FROM 
                    tab_cdr
                WHERE 
                    uniqueid IN {tuple(self.unique_ids)}
                GROUP BY 
                    uniqueid, src, dst;""" 
        rows = execute_query(self.company_id, sql) 

        if hit_report:
            # collection = self.mongo_client["billing-api"]["api_pricings"]  
            # result_stt = collection.find({"type": "stt"}) 
 
            # result_stt = [{"product": t["product"], "clientPrice": t["clientPrice"]} for t in result_stt if "clientPrice" in t] 
      
            for row in rows:  
                row["companyId"] = self.company_id 

                if rowMongo := next((m for m in self.mongo_results if m["_id"] == row["uniqueid"] ), None): 
                    row["custo_hit"] = f"{float(rowMongo["totalCost"])}" 
                    row["qtd_token_input"] = rowMongo.get('usageByType', {}).get('input', 0)
                    row["qtd_token_output"] = rowMongo.get('usageByType', {}).get('output', 0)  
                
                self.client_price_row(products, row)                    
                self.formate_properties(row) 
                 
        else:
            for row in rows:
                row["total_min"] = f"{(int(row['total_billsec']) / 60):.2f}"

                self.client_price_row(products, row)
                self.formate_properties(row) 
        
        return rows

    def formate_properties(self, row):
        for key in row:
            if isinstance(row[key], datetime):
                row[key] = row[key].isoformat(sep=' ')
            elif isinstance(row[key], Decimal):
                row[key] = float(row[key])
            elif key == "uniqueid":
                row[key] = str(row[key])

    def client_price_row(self, products, row):
        if products and len(products) > 0 and products[0]["priceHistory"]:
            matched_period = None
            last_period = None 
            
            for period in products[0]["priceHistory"]: 
                last_period = period
                start = period['startDate'].date()       
                end = period['endDate'].date() if period['endDate'] else None
                start_call = row['start_call'].date()   

                if end:
                    if start <= start_call <= end:
                        matched_period = period
                        break
                else: 
                    if start_call >= start: 
                        matched_period = period
                        break 

                f"{(int(row['total_billsec']) / 60):.2f}"
            if matched_period: 
                row['client_total_cost'] = f"{((int(row['total_billsec']) / 60) * matched_period['price'])}"
                row["client_price"] = matched_period['price']
            else: 
                row['client_total_cost'] = f"{((int(row['total_billsec']) / 60) * last_period['price'])}"
                row["client_price"] = last_period['price']


    def _create_excel(self, data: list, hit_report: Optional[bool] = False) -> str: 

        if hit_report:
            header_mapping = {
                "companyId": "Empresa",
                "uniqueid": "Identificador da chamada", 
                "src": "Origem",
                "dst": "Destino",
                "total_billsec": "Quantidade de segundos", 
                "custo_hit": "Custo HIT",
                "qtd_token_input": "Quantidade de tokens(input)",
                "qtd_token_output": "Quantidade de tokens(output)", 
                "client_total_cost": "Custo Cliente",
                "client_price": "Preço Cliente por Minuto",
                "start_call": "Inicio",
                "end_call": "Fim"
            }
        else:
            header_mapping = {
                "uniqueid": "Identificador da chamada",
                "src": "Origem",
                "dst": "Destino",
                "start_call": "Inicio da Chamada",
                "total_billsec": "Duração (Em segundos)",
                "total_min": "Duração (Em minutos)",
                "client_total_cost": "Custo Cliente",
            }

        # Filtrar e ordenar os dados conforme header_mapping
        selected_keys = list(header_mapping.keys())

        filtered_data = [{k: row.get(k, "") for k in selected_keys} for row in data]
        df = pd.DataFrame(filtered_data, columns=selected_keys)

        # Criação do Excel
        wb = Workbook()
        ws = wb.active
        ws.title = "tab_cdr"

        header_font = Font(bold=True)
        yellow_fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")

        # Adiciona cabeçalhos personalizados
        custom_headers = [header_mapping[col] for col in selected_keys]
        ws.append(custom_headers)
        for cell in ws[ws.max_row]:
            cell.font = header_font
            cell.fill = yellow_fill

        # Adiciona os dados
        for row in df.itertuples(index=False, name=None):
            ws.append(row)

        # Define caminho e salva o arquivo
        filename = f"HISTORICO-CHAMADAS-GRAVADAS-{self.start_date}_{self.end_date}.xlsx" 
        BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
        reports_dir = os.path.join(BASE_DIR, "reports")
        os.makedirs(reports_dir, exist_ok=True)
        path = os.path.join(reports_dir, filename) 
        wb.save(path)
        return path
    
    
    def reportDataXLSX(self, hit_report: Optional[bool] = False) -> str:
        self._fetch_mongo_data(all_data=True)

        if hit_report:
            mysql_data = self._fetch_mysql_data(hit_report=True)
            return self._create_excel(mysql_data, hit_report=True)
            
        mysql_data = self._fetch_mysql_data() 
        return self._create_excel(mysql_data) 
    

    def reportData(self, page: int = 1, page_size: int = 20, hit_report: Optional[bool] = False) -> Dict[str, Any]:
        mongo_data = self._fetch_mongo_data(page=page, page_size=page_size)

        if hit_report:
            mysql_data = self._fetch_mysql_data(hit_report=True)
        else:
            mysql_data = self._fetch_mysql_data()

        return {
            "pagination": mongo_data,
            "data": mysql_data
        }