aiData/Controller/YltAnalyticsController.py

import math
import asyncio
import json
from typing import List, Optional, Dict, Any

from fastapi import APIRouter, HTTPException
from fastapi.responses import StreamingResponse, FileResponse, JSONResponse

from Config.Config import DB_URL
from Util.LlmUtil import get_llm_response
from Tools.T6_Export import export_excel, DorisExcelExporter, extract_hourly_prices_from_schedule
import tempfile
import os
import zipfile
import subprocess
from pydantic import BaseModel
from starlette.background import BackgroundTask
from Model.YltAnalyticsModel import (
    StationBase,
    CompetitorStation,
    GeoCompetitionResponse,
    GeoCompetitionSummary,
    PriceSeries,
    PriceComparisonResponse,
    PriceComparisonSummary,
    YltAnalyticsModel,
)


from DbKit.Db import Db

router = APIRouter()

# db = Db(db_url=DB_URL) # Removed direct db instance


async def init_db():
    db = Db()
    await db.init_db()


async def close_db():
    db = Db()
    await db.close()


@router.get("/api/operators/hourly-prices")
async def get_operators_hourly_prices():
    operators = ["新电途", "特来电", "驿来特", "艾特吉易充"]
    model = YltAnalyticsModel()
    try:
        result = []
        for op in operators:
            rows = await model.fetch_current_station_rows(op)
            if not rows:
                result.append({"operator": op, "series": [None] * 24})
                continue
            sums = [0.0] * 24
            counts = [0] * 24
            for row in rows:
                schedule_json = row.get("schedule_json")
                series = extract_hourly_prices_from_schedule(schedule_json)
                for i in range(24):
                    v = series[i]
                    if v is None:
                        continue
                    sums[i] += float(v)
                    counts[i] += 1
            avg_series = []
            for i in range(24):
                c = counts[i]
                if c > 0:
                    avg_series.append(sums[i] / c)
                else:
                    avg_series.append(None)
            result.append({"operator": op, "series": avg_series})
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    return {"operators": result}


@router.get("/api/operators/price-trends")
async def get_operators_price_trends(days: int = 7):
    operators = ["新电途", "特来电", "驿来特", "艾特吉易充"]
    
    model = YltAnalyticsModel()
    rows = await model.get_operators_price_trends(days)
    
    # 数据结构: { operator: { datetime_str: [sums_of_price, counts_of_station] } }
    trend_data = {}
    for op in operators:
        trend_data[op] = {}
        
    for row in rows:
        op = row.get("operator")
        if op not in trend_data:
            continue
        
        # 将日期和 schedule_json 展开为 24 小时的数据点
        d_str = str(row.get("date_str"))
        # 将 2026-01-21 转换为 01/21
        try:
            date_parts = d_str.split('-')
            display_date = date_parts[1] + '/' + date_parts[2]
        except:
            display_date = d_str
            
        schedule_json = row.get("schedule_json")
        series = extract_hourly_prices_from_schedule(schedule_json)
        
        for hour in range(24):
            v = series[hour]
            if v is not None:
                # 使用原始日期 YYYY-MM-DD 用于排序，显示时由前端或后端格式化
                # 这里我们构造一个带补全的时间字符串，方便自然排序
                dt_key = f"{d_str} {hour:02d}:00"
                if dt_key not in trend_data[op]:
                    trend_data[op][dt_key] = {"sum": 0.0, "count": 0, "display": f"{display_date} {hour:02d}:00"}
                trend_data[op][dt_key]["sum"] += float(v)
                trend_data[op][dt_key]["count"] += 1
                
    # 转换为 ECharts 友好格式
    # 1. 获取所有时间点并排序
    all_time_keys = set()
    for op in operators:
        all_time_keys.update(trend_data[op].keys())
    sorted_keys = sorted(list(all_time_keys))
    
    # 2. 提取显示用的标签
    display_dates = []
    if sorted_keys:
        # 从任意一个存在的运营商数据中获取 display 标签
        first_op = operators[0]
        for key in sorted_keys:
            # 找到包含该 key 的 display 标签
            label = key # fallback
            for op in operators:
                if key in trend_data[op]:
                    label = trend_data[op][key]["display"]
                    break
            display_dates.append(label)
    
    # 3. 为每个运营商构建完整的时间序列数据
    series_result = []
    for op in operators:
        op_data = []
        for key in sorted_keys:
            if key in trend_data[op]:
                stats = trend_data[op][key]
                op_data.append(round(stats["sum"] / stats["count"], 4))
            else:
                op_data.append(None)
        series_result.append({"name": op, "data": op_data})
        
    return {
        "dates": display_dates,
        "series": series_result
    }


@router.get("/api/export/prices-zip")
async def export_prices_zip():
    operators = ["新电途", "特来电", "驿来特", "艾特吉易充"]
    tmp_dir = tempfile.mkdtemp(prefix="price_export_")
    excel_paths = []
    for op in operators:
        filename = f"{op}_{asyncio.get_event_loop().time():.0f}.xlsx"
        output_path = os.path.join(tmp_dir, filename)
        await export_excel(op, output_path)
        excel_paths.append(output_path)
    zip_path = os.path.join(tmp_dir, "prices_export.zip")
    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
        for p in excel_paths:
            arcname = os.path.basename(p)
            zf.write(p, arcname=arcname)
    return FileResponse(
        zip_path,
        media_type="application/zip",
        filename="多供应商电价导出.zip",
    )


class AiReportRequest(BaseModel):
    content: str


@router.post("/api/export/ai-report-docx")
async def export_ai_report_docx(req: AiReportRequest):
    content = req.content
    if not content:
        raise HTTPException(status_code=400, detail="Content is empty")
    
    # Create temp markdown file
    with tempfile.NamedTemporaryFile(mode="w", suffix=".md", delete=False, encoding="utf-8") as tmp_md:
        tmp_md.write(content)
        tmp_md_path = tmp_md.name
    
    output_docx_path = tmp_md_path.replace(".md", ".docx")
    
    # Check template
    template_path = "static/template/templates.docx"
    cmd = ['pandoc', '-s', tmp_md_path, '-o', output_docx_path, '--resource-path=static']
    
    # Only add reference doc if it exists, but the user requested it specifically.
    # We'll check if it exists, if not, we might fail or warn, but let's try to include it if possible.
    if os.path.exists(template_path):
        cmd.extend(['--reference-doc', template_path])
    
    try:
        subprocess.run(cmd, check=True)
    except subprocess.CalledProcessError as e:
        # Clean up
        if os.path.exists(tmp_md_path):
            os.remove(tmp_md_path)
        raise HTTPException(status_code=500, detail=f"Pandoc conversion failed: {str(e)}")
    
    def cleanup():
        if os.path.exists(tmp_md_path):
            os.remove(tmp_md_path)
        if os.path.exists(output_docx_path):
            os.remove(output_docx_path)

    return FileResponse(
        output_docx_path,
        media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
        filename="AI分析报告.docx",
        background=BackgroundTask(cleanup)
    )



@router.get("/api/ai/pricing/strategy-summary")
async def ai_pricing_strategy_summary():
    async def generate_stream():
        try:
            # 发送初始信息并增加一些空白填充，防止某些代理缓存
            yield "正在收集各供应商价格数据，请稍候...\n\n" + (" " * 512) + "\n"
            print("AI分析开始: 获取运营商价格数据...")
            
            # 使用 asyncio.wait_for 防止数据库查询无限挂起
            try:
                # 1. 获取当前最新 24 小时平均价格
                resp = await asyncio.wait_for(get_operators_hourly_prices(), timeout=30.0)
                
                # 2. 获取最近 3 天的价格变动趋势
                trend_resp = await asyncio.wait_for(get_operators_price_trends(days=3), timeout=30.0)
            except asyncio.TimeoutError:
                print("获取价格数据超时")
                yield "\n\n**错误**: 获取价格数据超时，数据库响应过慢，请稍后重试。"
                return

            # 处理当前价格数据
            data = resp.get("operators", [])
            text_data = []
            for item in data:
                text_data.append({"operator": item.get("operator"), "series": item.get("series")})
            
            # 处理 3 天趋势数据
            trend_dates = trend_resp.get("dates", [])
            trend_series = trend_resp.get("series", [])
            trend_text = []
            for s in trend_series:
                trend_text.append({"operator": s.get("name"), "daily_avg_prices": s.get("data")})

            print(f"数据获取完成，准备请求LLM. 数据条数: {len(text_data)}, 趋势天数: {len(trend_dates)}")
            yield "数据收集完成，正在分析最近 3 天的价格波动趋势并生成深度建议...\n\n"
            
            # 增加一个心跳，确保连接不断开
            yield " " * 128 + "\n"
            
            prompt = (
                "你是一位专业的充电桩调价策略分析顾问。下面是四家供应商（新电途、特来电、驿来特、艾特吉易充）的电价分析数据：\n\n"
                "### 1. 当前最新 24 小时平均分时电价 (元/kWh)\n"
                f"{json.dumps(text_data, ensure_ascii=False)}\n\n"
                "### 2. 最近 3 天的价格变动趋势 (每日平均电价)\n"
                f"日期序列: {trend_dates}\n"
                f"各司趋势: {json.dumps(trend_text, ensure_ascii=False)}\n\n"
                "请根据以上数据进行深度分析：\n"
                "1. **现状对比**：对比我司（驿来特）与竞对在不同时段的电价水平，找出我司偏高或偏低的关键时段。\n"
                "2. **趋势洞察**：分析最近 3 天各供应商的价格调整动态，判断市场整体是在涨价、降价还是保持稳定，我司的反应是否及时。\n"
                "3. **问题诊断**：指出我司目前定价中存在的潜在风险（如价格倒挂、错失高峰收益、低谷缺乏竞争力等）。\n"
                "4. **优化方案**：给出 2-3 条具体的、可落地的调价建议，并说明理由。\n\n"
                "要求：\n"
                "- 使用专业、客观的语气。\n"
                "- 采用 Markdown 格式，适当使用加粗和表格。\n"
                "- 回答控制在 800-1000 字以内。"
            )
            
            # 清空之前的提示信息，开始正式输出 AI 内容
            yield "---CLEAR_PREVIOUS_HINTS---\n"
            
            chunk_count = 0
            # 使用 asyncio.wait_for 防止 LLM 请求完全死掉
            try:
                # 某些时候 LLM 可能会卡住，设置一个合理的整体超时
                async for chunk in get_llm_response(
                    prompt,
                    stream=True,
                    system_prompt="你是熟悉中国充电桩行业的电价策略分析顾问。",
                ):
                    chunk_count += 1
                    if chunk_count == 1:
                        print("收到LLM首个chunk")
                    yield chunk
            except Exception as llm_e:
                print(f"LLM请求异常: {str(llm_e)}")
                yield f"\n\n**AI 分析服务异常**: {str(llm_e)}。这可能是由于大模型服务商（如 DeepSeek）响应过慢或连接中断导致的。"
                return

            print(f"AI分析完成，共发送 {chunk_count} 个chunks")
        except Exception as e:
            error_msg = f"\n\n**分析过程出现严重错误**: {str(e)}"
            print(error_msg)
            yield error_msg

    return StreamingResponse(
        generate_stream(), 
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
            "Content-Type": "text/event-stream; charset=utf-8"
        }
    )


@router.get("/api/ylt/stations", response_model=List[StationBase])
async def list_ylt_stations(q: Optional[str] = None):
    model = YltAnalyticsModel()
    rows = await model.list_ylt_stations(q)
    result: List[StationBase] = []
    for r in rows:
        result.append(
            StationBase(
                station_hash=r.get("station_hash"),
                operator=r.get("operator"),
                station_name=r.get("station_name"),
                address=r.get("address"),
                coord_x=r.get("coord_x"),
                coord_y=r.get("coord_y"),
                current_price=r.get("current_price"),
            )
        )
    return result


def haversine_km(lon1: float, lat1: float, lon2: float, lat2: float) -> float:
    r = 6371.0
    phi1 = math.radians(lat1)
    phi2 = math.radians(lat2)
    d_phi = math.radians(lat2 - lat1)
    d_lambda = math.radians(lon2 - lon1)
    a = math.sin(d_phi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(d_lambda / 2) ** 2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    return r * c


async def fetch_current_stations() -> List[dict]:
    model = YltAnalyticsModel()
    return await model.fetch_current_stations()


async def build_geo_competition(station_hash: str, radius_km: float = 3.0) -> GeoCompetitionResponse:
    rows = await fetch_current_stations()
    if not rows:
        raise HTTPException(status_code=404, detail="no station data")
    base_row = None
    for r in rows:
        if r.get("station_hash") == station_hash and r.get("operator") == "驿来特":
            base_row = r
            break
    if base_row is None:
        raise HTTPException(status_code=404, detail="base station not found for 驿来特")
    base_lon = base_row.get("coord_x")
    base_lat = base_row.get("coord_y")
    if base_lon is None or base_lat is None:
        raise HTTPException(status_code=400, detail="base station has no coordinates")
    competitors: List[CompetitorStation] = []
    ylt_price = base_row.get("current_price")
    cheaper = 0
    same = 0
    more_expensive = 0
    min_price: Optional[float] = None
    max_price: Optional[float] = None
    for r in rows:
        if r.get("operator") == "驿来特":
            continue
        lon = r.get("coord_x")
        lat = r.get("coord_y")
        if lon is None or lat is None:
            continue
        dist = haversine_km(base_lon, base_lat, lon, lat)
        if dist > radius_km:
            continue
        price = r.get("current_price")
        competitors.append(
            CompetitorStation(
                station_hash=r.get("station_hash"),
                operator=r.get("operator"),
                station_name=r.get("station_name"),
                distance_km=round(dist, 3),
                current_price=price,
            )
        )
        if price is not None:
            if min_price is None or price < min_price:
                min_price = price
            if max_price is None or price > max_price:
                max_price = price
            if ylt_price is not None:
                if price < ylt_price:
                    cheaper += 1
                elif price > ylt_price:
                    more_expensive += 1
                else:
                    same += 1
    base_station = StationBase(
        station_hash=base_row.get("station_hash"),
        operator=base_row.get("operator"),
        station_name=base_row.get("station_name"),
        address=base_row.get("address"),
        coord_x=base_lon,
        coord_y=base_lat,
        current_price=ylt_price,
    )
    return GeoCompetitionResponse(
        base_station=base_station,
        competitors=competitors,
        ylt_price=ylt_price,
        min_competitor_price=min_price,
        max_competitor_price=max_price,
        cheaper_count=cheaper,
        same_count=same,
        more_expensive_count=more_expensive,
    )


async def fetch_station_schedule_json(station_hash: str) -> Optional[str]:
    model = YltAnalyticsModel()
    value = await model.fetch_station_schedule_json(station_hash)
    if value is None:
        return None
    if isinstance(value, str):
        return value
    try:
        return json.dumps(value, ensure_ascii=False)
    except Exception:
        return None


def extract_price_from_item(item: Dict[str, Any]) -> Optional[float]:
    if not isinstance(item, dict):
        return None
    for key in ("price", "price_kwh", "priceKwh", "total_price", "totalPrice"):
        v = item.get(key)
        if isinstance(v, (int, float)):
            return float(v)
    elec = item.get("elec_price")
    service = item.get("service_price")
    if isinstance(elec, (int, float)) and isinstance(service, (int, float)):
        return float(elec) + float(service)
    elec2 = item.get("electric_fee_kwh")
    service2 = item.get("service_fee_kwh")
    if isinstance(elec2, (int, float)) and isinstance(service2, (int, float)):
        return float(elec2) + float(service2)
    elec3 = item.get("ele_fee")
    service3 = item.get("ser_fee")
    if isinstance(elec3, (int, float)) and isinstance(service3, (int, float)):
        return float(elec3) + float(service3)
    return None


def parse_hour_from_item(item: Dict[str, Any], default_index: int) -> Optional[int]:
    start = item.get("start")
    if isinstance(start, str) and ":" in start:
        parts = start.split(":")
        try:
            h = int(parts[0])
            if 0 <= h <= 23:
                return h
        except Exception:
            pass
    end = item.get("end")
    if isinstance(end, str) and ":" in end:
        parts = end.split(":")
        try:
            h2 = int(parts[0])
            if 0 < h2 <= 24:
                return h2 - 1
        except Exception:
            pass
    if 0 <= default_index <= 23:
        return default_index
    return None


def extract_hourly_prices(schedule_json_str: str) -> List[Optional[float]]:
    series: List[Optional[float]] = [None] * 24
    if not schedule_json_str:
        return series
    try:
        data = json.loads(schedule_json_str)
    except Exception:
        return series
    if not isinstance(data, list):
        return series
    for idx, item in enumerate(data):
        price = extract_price_from_item(item)
        if price is None:
            continue
        hour_idx = parse_hour_from_item(item, idx)
        if hour_idx is None or not (0 <= hour_idx < 24):
            continue
        series[hour_idx] = float(price)
    return series


async def build_price_comparison(station_hash: str) -> PriceComparisonResponse:
    geo = await build_geo_competition(station_hash)
    base_station = geo.base_station
    base_schedule_str = await fetch_station_schedule_json(base_station.station_hash)
    if base_schedule_str is None:
        raise HTTPException(status_code=404, detail="no price schedule for YLT station")
    ylt_series = extract_hourly_prices(base_schedule_str)
    hours = list(range(24))
    operator_series_sum: Dict[str, List[float]] = {}
    operator_series_count: Dict[str, List[int]] = {}
    for comp in geo.competitors:
        schedule_str = await fetch_station_schedule_json(comp.station_hash)
        if not schedule_str:
            continue
        series = extract_hourly_prices(schedule_str)
        op = comp.operator
        if op not in operator_series_sum:
            operator_series_sum[op] = [0.0] * 24
            operator_series_count[op] = [0] * 24
        sums = operator_series_sum[op]
        counts = operator_series_count[op]
        for i in range(24):
            v = series[i]
            if v is None:
                continue
            sums[i] += v
            counts[i] += 1
    competitors_series: List[PriceSeries] = []
    for op, sums in operator_series_sum.items():
        counts = operator_series_count[op]
        avg_series: List[Optional[float]] = []
        for i in range(24):
            c = counts[i]
            if c > 0:
                avg_series.append(sums[i] / c)
            else:
                avg_series.append(None)
        competitors_series.append(PriceSeries(operator=op, series=avg_series))
    diffs: List[float] = []
    for i in range(24):
        y = ylt_series[i]
        if y is None:
            continue
        competitor_prices: List[float] = []
        for s in competitors_series:
            v = s.series[i]
            if v is not None:
                competitor_prices.append(float(v))
        if not competitor_prices:
            continue
        min_comp = min(competitor_prices)
        diffs.append(y - min_comp)
    min_diff = min(diffs) if diffs else None
    max_diff = max(diffs) if diffs else None
    avg_diff = sum(diffs) / len(diffs) if diffs else None
    ylt_price_series = PriceSeries(operator=base_station.operator, series=ylt_series)
    return PriceComparisonResponse(
        hours=hours,
        ylt=ylt_price_series,
        competitors=competitors_series,
        min_diff=min_diff,
        max_diff=max_diff,
        avg_diff=avg_diff,
    )


@router.get("/health")
async def health():
    return {"status": "ok"}


@router.get("/api/ylt/geo/competitors/{station_hash}", response_model=GeoCompetitionResponse)
async def get_geo_competitors(station_hash: str):
    return await build_geo_competition(station_hash)


@router.get("/api/ylt/geo/competitors/{station_hash}/summary", response_model=GeoCompetitionSummary)
async def get_geo_competitors_summary(station_hash: str):
    data = await build_geo_competition(station_hash)
    base = data.base_station
    total_comp = len(data.competitors)
    cheaper = data.cheaper_count
    same = data.same_count
    more_expensive = data.more_expensive_count
    ylt_price = data.ylt_price
    min_price = data.min_competitor_price
    max_price = data.max_competitor_price
    summary_input = {
        "station_name": base.station_name,
        "operator": base.operator,
        "ylt_price": ylt_price,
        "competitor_count": total_comp,
        "cheaper_count": cheaper,
        "same_count": same,
        "more_expensive_count": more_expensive,
        "min_competitor_price": min_price,
        "max_competitor_price": max_price,
    }
    text = (
        "请作为驿来特价格策略分析顾问，用简明中文解释当前场站在3公里范围内的价格竞争情况，"
        "给出可操作的价格调整或产品策略建议，控制在300字以内。以下是结构化数据：\n"
        f"{summary_input}"
    )
    chunks: List[str] = []
    async for chunk in get_llm_response(
        text,
        stream=False,
        system_prompt="你是驿来特电价和选址策略顾问。",
    ):
        chunks.append(chunk)
    summary_text = "".join(chunks)
    return GeoCompetitionSummary(summary=summary_text)


@router.get("/api/ylt/pricing/comparison/{station_hash}", response_model=PriceComparisonResponse)
async def get_price_comparison(station_hash: str):
    return await build_price_comparison(station_hash)


@router.get("/api/ylt/pricing/comparison/{station_hash}/summary", response_model=PriceComparisonSummary)
async def get_price_comparison_summary(station_hash: str):
    data = await build_price_comparison(station_hash)
    ylt_series = data.ylt.series
    text_data = {
        "hours": data.hours,
        "ylt_prices": ylt_series,
        "competitors": [
            {"operator": s.operator, "series": s.series} for s in data.competitors
        ],
        "min_diff": data.min_diff,
        "max_diff": data.max_diff,
        "avg_diff": data.avg_diff,
    }
    text = (
        "请作为驿来特价格策略分析顾问，对下列分时电价数据进行比较分析：\n"
        "1) 解释驿来特与三家竞品在一天24小时内的价格差距特征；\n"
        "2) 指出在哪些时间段我们明显偏贵、在哪些时间段有优势；\n"
        "3) 给出2到3条可执行的调价或营销策略建议；\n"
        "控制在400字以内。数据如下：\n"
        f"{text_data}"
    )
    chunks: List[str] = []
    async for chunk in get_llm_response(
        text,
        stream=False,
        system_prompt="你是驿来特电价策略分析顾问。",
    ):
        chunks.append(chunk)
    summary_text = "".join(chunks)
    return PriceComparisonSummary(summary=summary_text)


@router.get("/api/ylt/pricing/comparison/{station_hash}/sse")
async def stream_price_comparison_summary(station_hash: str):
    data = await build_price_comparison(station_hash)
    text_data = {
        "hours": data.hours,
        "ylt_prices": data.ylt.series,
        "competitors": [
            {"operator": s.operator, "series": s.series} for s in data.competitors
        ],
        "min_diff": data.min_diff,
        "max_diff": data.max_diff,
        "avg_diff": data.avg_diff,
    }
    text = (
        "请作为驿来特价格策略分析顾问，对下列分时电价数据进行比较分析：\n"
        "1) 解释驿来特与三家竞品在一天24小时内的价格差距特征；\n"
        "2) 指出在哪些时间段我们明显偏贵、在哪些时间段有优势；\n"
        "3) 给出2到3条可执行的调价或营销策略建议；\n"
        "控制在400字以内。数据如下：\n"
        f"{text_data}"
    )

    async def event_generator():
        async for chunk in get_llm_response(
            text,
            stream=True,
            system_prompt="你是驿来特电价策略分析顾问。",
        ):
            if chunk is None:
                continue
            yield f"data: {chunk}\n\n"
        yield "event: end\ndata: [DONE]\n\n"

    return StreamingResponse(event_generator(), media_type="text/event-stream")