"""
api_server.py — FastAPI REST API for Oil Risk Dashboard
========================================================
Serves data from output/ directory as JSON REST endpoints.
Run: python api_server.py
"""

import os, json
import pandas as pd
import numpy as np
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional

from config import BASE_DIR, OUTPUT_DIR, OUTPUT_FILES, PRICE_COLS, INDUSTRIES

os.chdir(BASE_DIR)

app = FastAPI(title="Oil Risk Intelligence API", version="2.0")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ── Helpers ──

def _load_json(path, default=None):
    try:
        with open(path, 'r', encoding='utf-8') as f:
            return json.load(f)
    except Exception:
        return default if default is not None else {}


def _load_results(benchmark: str) -> pd.DataFrame:
    path = os.path.join(OUTPUT_DIR, f'v2_results_{benchmark}.csv')
    if not os.path.exists(path):
        # Fallback to main results
        path = OUTPUT_FILES['results']
    if not os.path.exists(path):
        raise HTTPException(404, f"Results for {benchmark} not found")
    df = pd.read_csv(path)
    df['test_date'] = pd.to_datetime(df['test_date'])
    return df


def _process_row(row):
    """Convert a results row to API-friendly dict."""
    d = {
        'date': row['test_date'].strftime('%Y-%m'),
        'risk_level': row.get('risk_level', 'Medium'),
        'risk_bias': row.get('risk_bias', 'Balanced'),
        'pred_vol': round(row.get('pred_vol', 0) * 100, 2),
        'top_factor': row.get('top_factor', 'Unknown'),
        'regime_match': row.get('regime_match', 'Unknown'),
        'regime_similarity': round(row.get('regime_similarity', 0), 4),
        'regime_type': row.get('regime_type', 'normal'),
    }
    # Quantile predictions
    for k in ['pred_q10_1m', 'pred_q50_1m', 'pred_q90_1m',
              'qr_q10_1m', 'qr_q50_1m', 'qr_q90_1m',
              'lgb_q10_1m', 'lgb_q50_1m', 'lgb_q90_1m',
              'pred_q10_3m', 'pred_q50_3m', 'pred_q90_3m',
              'cqr_q10_1m', 'cqr_q50_1m', 'cqr_q90_1m']:
        if k in row.index and pd.notna(row.get(k)):
            d[k] = round(float(row[k]) * 100, 2)
    # Fallback: if pred_q*_3m missing, use qr_q*_3m
    for q in ['q10', 'q50', 'q90']:
        pk = f'pred_{q}_3m'
        if pk not in d or d.get(pk) is None:
            qk = f'qr_{q}_3m'
            if qk in d:
                d[pk] = d[qk]
    # Actuals
    if pd.notna(row.get('actual_ret_1m')):
        d['actual_ret_1m'] = round(float(row['actual_ret_1m']) * 100, 2)
    if pd.notna(row.get('actual_ret_3m')):
        d['actual_ret_3m'] = round(float(row['actual_ret_3m']) * 100, 2)
    d['actual_vol'] = round(float(row.get('actual_vol', 0)) * 100, 2) if pd.notna(row.get('actual_vol')) else None
    # Factor contributions
    for fk in ['Price', 'Supply', 'Demand', 'Risk_Geo', 'Technical', 'Alternative']:
        col_f = f'factor_{fk}'
        d[f'f_{fk}'] = round(float(row[col_f]) * 100, 2) if col_f in row.index and pd.notna(row.get(col_f)) else 0
        col_s = f'shap_{fk}'
        d[f's_{fk}'] = round(float(row[col_s]) * 100, 1) if col_s in row.index and pd.notna(row.get(col_s)) else 0
    # Industry
    for ind in INDUSTRIES:
        d[f'{ind}_r'] = row.get(f'{ind}_risk', 'Low')
        d[f'{ind}_a'] = row.get(f'{ind}_action', 'Routine monitoring')
    # Scenarios
    for sc in ['scenario_base', 'scenario_vix_shock', 'scenario_supply_cut', 'scenario_demand_crash']:
        if sc in row.index and pd.notna(row[sc]):
            d[sc.replace('scenario_', '')] = round(float(row[sc]) * 100, 2)
    return d


def _compute_eval(df: pd.DataFrame) -> dict:
    """Compute evaluation metrics from results DataFrame."""
    mask = df['actual_ret_1m'].notna()
    r = df[mask].copy()
    n = len(r)
    if n == 0:
        return {'n': 0}
    ar = r['actual_ret_1m'].values
    pv = r['pred_vol'].values
    av = r['actual_vol'].values
    q10 = r['pred_q10_1m'].values
    q90 = r['pred_q90_1m'].values

    cov_1m = float(((ar >= q10) & (ar <= q90)).mean())
    wis_1m = float(((q90 - q10) + (2 / 0.2) * np.maximum(q10 - ar, 0) + (2 / 0.2) * np.maximum(ar - q90, 0)).mean())
    naive_wis = float(((np.quantile(ar, 0.90) - np.quantile(ar, 0.10)) + (2 / 0.2) * np.maximum(np.quantile(ar, 0.10) - ar, 0) + (2 / 0.2) * np.maximum(ar - np.quantile(ar, 0.90), 0)).mean())
    vm = np.nanmedian(av)
    hi = av > vm
    cov_hi = float(((ar[hi] >= q10[hi]) & (ar[hi] <= q90[hi])).mean()) if hi.sum() > 0 else 0
    vol_rmse = float(np.sqrt(np.nanmean((av - pv) ** 2)))
    vol_corr = float(np.corrcoef(av[~np.isnan(av)], pv[~np.isnan(av)])[0, 1]) if n > 2 else 0
    m3m = r['actual_ret_3m'].notna()
    cov_3m = float(((r.loc[m3m, 'actual_ret_3m'].values >= r.loc[m3m, 'pred_q10_3m'].values) & (r.loc[m3m, 'actual_ret_3m'].values <= r.loc[m3m, 'pred_q90_3m'].values)).mean()) if m3m.sum() > 10 else 0

    lgb_cov = lgb_wis = 0
    if 'lgb_q10_1m' in r.columns:
        lgb_cov = float(((ar >= r['lgb_q10_1m'].values) & (ar <= r['lgb_q90_1m'].values)).mean())
        lgb_wis = float(((r['lgb_q90_1m'].values - r['lgb_q10_1m'].values) + (2 / 0.2) * np.maximum(r['lgb_q10_1m'].values - ar, 0) + (2 / 0.2) * np.maximum(ar - r['lgb_q90_1m'].values, 0)).mean())

    cqr_cov = cqr_wis = 0
    if 'cqr_q10_1m' in r.columns:
        cq10 = r['cqr_q10_1m'].values
        cq90 = r['cqr_q90_1m'].values
        cqr_cov = float(((ar >= cq10) & (ar <= cq90)).mean())
        cqr_wis = float(((cq90 - cq10) + (2 / 0.2) * np.maximum(cq10 - ar, 0) + (2 / 0.2) * np.maximum(ar - cq90, 0)).mean())

    return {
        'cov_1m': round(cov_1m * 100, 1), 'wis_1m': round(wis_1m, 4),
        'naive_wis': round(naive_wis, 4), 'cov_hi': round(cov_hi * 100, 1),
        'cov_3m': round(cov_3m * 100, 1), 'vol_rmse': round(vol_rmse, 4),
        'vol_corr': round(vol_corr, 3), 'n': n,
        'lgb_cov': round(lgb_cov * 100, 1), 'lgb_wis': round(lgb_wis, 4),
        'cqr_cov': round(cqr_cov * 100, 1), 'cqr_wis': round(cqr_wis, 4),
    }


# ── API Endpoints ──

@app.get("/api/benchmarks")
def get_benchmarks():
    """List available benchmarks."""
    available = []
    for bm in PRICE_COLS:
        path = os.path.join(OUTPUT_DIR, f'v2_results_{bm}.csv')
        if os.path.exists(path):
            available.append(bm)
    return available or ['WTI']


@app.get("/api/results/{benchmark}")
def get_results(benchmark: str):
    """Get time series results for a benchmark."""
    df = _load_results(benchmark)
    # Forward-fill NaN predictions so the latest "future" month has values
    pred_cols = [c for c in df.columns if any(c.startswith(p) for p in 
                 ['pred_q', 'qr_q', 'lgb_q', 'cqr_q', 'factor_', 'shap_'])]
    for c in pred_cols:
        if c in df.columns:
            df[c] = df[c].ffill()
    return [_process_row(row) for _, row in df.iterrows()]


@app.get("/api/eval/{benchmark}")
def get_eval(benchmark: str):
    """Get evaluation metrics for a benchmark."""
    df = _load_results(benchmark)
    return _compute_eval(df)


@app.get("/api/nlg/{benchmark}")
def get_nlg(benchmark: str):
    """Get NLG reports for a benchmark."""
    path = os.path.join(OUTPUT_DIR, f'v2_nlg_{benchmark}.json')
    if not os.path.exists(path):
        path = OUTPUT_FILES['nlg']
    return _load_json(path)


@app.get("/api/scenarios")
def get_scenarios():
    return _load_json(OUTPUT_FILES['scenarios'])


@app.get("/api/regime")
def get_regime():
    return _load_json(OUTPUT_FILES['regime'])


@app.get("/api/hedging")
def get_hedging():
    data = _load_json(OUTPUT_FILES['hedging'])
    # Enrich tool_comparison with static metadata for display
    TOOL_META = {
        'futures': {'cost': '低(保证金)', 'downside_protection': '100%', 'upside_participation': '0%', 'complexity': '低', 'best_for': '确定性需求、锁定成本'},
        'put':     {'cost': '中(权利金)', 'downside_protection': '100%', 'upside_participation': '100%', 'complexity': '中', 'best_for': '保留上行空间'},
        'collar':  {'cost': '极低/零', 'downside_protection': '90%', 'upside_participation': '有限', 'complexity': '高', 'best_for': '预算敏感、限价对冲'},
    }
    if isinstance(data, dict):
        for ind_key, ind_data in data.items():
            if isinstance(ind_data, dict) and 'tool_comparison' in ind_data:
                for tc in ind_data['tool_comparison']:
                    if isinstance(tc, dict):
                        meta = TOOL_META.get(tc.get('tool', ''), {})
                        for mk, mv in meta.items():
                            if mk not in tc:
                                tc[mk] = mv
    return data


@app.get("/api/backtest")
def get_backtest():
    return _load_json(OUTPUT_FILES['backtest'])


@app.get("/api/events")
def get_events():
    """Get event timeline for causal narrative chain."""
    return _load_json(os.path.join(OUTPUT_DIR, 'event_timeline.json'), [])


@app.get("/api/ablation")
def get_ablation():
    return _load_json(OUTPUT_FILES['ablation'], [])


@app.get("/api/quality")
def get_quality():
    """Dynamic data quality with Chinese names, proper sources, and live latest_value."""
    # Feature metadata: name_zh, source, source_detail, frequency, factor_group, lag
    META = {
        'WTI_spot':              {'zh': 'WTI原油现货价', 'src': 'FRED', 'src_detail': 'FRED DCOILWTICO', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
        'Brent_spot':            {'zh': 'Brent原油现货价', 'src': 'FRED', 'src_detail': 'FRED DCOILBRENTEU', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
        'natgas_spot_henry':     {'zh': '天然气现货(Henry Hub)', 'src': 'FRED', 'src_detail': 'FRED DHHNGSP', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
        'iron_ore_spot':         {'zh': '铁矿石现货价', 'src': 'World Bank', 'src_detail': 'Pink Sheet (铁矿石CFR天津)', 'freq': 'monthly', 'group': 'Price', 'lag': 30},
        'gold_spot':             {'zh': '黄金现货价', 'src': 'FRED', 'src_detail': 'FRED GOLDAMGBD228NLBM', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
        'pmi_us_mfg':            {'zh': '美国制造业PMI', 'src': 'FRED', 'src_detail': 'FRED MANEMP/ISM', 'freq': 'monthly', 'group': 'Demand', 'lag': 5},
        'ipi_us':                {'zh': '美国工业生产指数', 'src': 'FRED', 'src_detail': 'FRED INDPRO', 'freq': 'monthly', 'group': 'Demand', 'lag': 15},
        'nonfarm_us':            {'zh': '美国非农就业人数', 'src': 'FRED', 'src_detail': 'FRED PAYEMS', 'freq': 'monthly', 'group': 'Demand', 'lag': 5},
        'usd_index':             {'zh': '美元指数(DXY)', 'src': 'FRED', 'src_detail': 'FRED DTWEXBGS', 'freq': 'daily→monthly', 'group': 'Demand', 'lag': 1},
        'cpi_us':                {'zh': '美国CPI(同比)', 'src': 'FRED', 'src_detail': 'FRED CPIAUCSL', 'freq': 'monthly', 'group': 'Demand', 'lag': 12},
        'fed_funds_rate':        {'zh': '联邦基金利率', 'src': 'FRED', 'src_detail': 'FRED FEDFUNDS', 'freq': 'monthly', 'group': 'Demand', 'lag': 1},
        'yield_spread_10y2y':    {'zh': '美债利差(10Y-2Y)', 'src': 'FRED', 'src_detail': 'FRED T10Y2Y', 'freq': 'daily→monthly', 'group': 'Demand', 'lag': 1},
        'vix':                   {'zh': 'VIX波动率指数', 'src': 'FRED', 'src_detail': 'FRED VIXCLS', 'freq': 'daily→monthly', 'group': 'Risk', 'lag': 1},
        'gpr_index':             {'zh': '地缘政治风险指数(GPR)', 'src': 'GPR', 'src_detail': 'Caldara & Iacoviello', 'freq': 'monthly', 'group': 'Risk', 'lag': 30},
        'us_oil_inventory_total':{'zh': '美国原油商业库存', 'src': 'EIA', 'src_detail': 'EIA WCESTUS1', 'freq': 'weekly→monthly', 'group': 'Supply', 'lag': 5},
        'us_crude_production':   {'zh': '美国原油产量', 'src': 'EIA', 'src_detail': 'EIA MCRFPUS2', 'freq': 'monthly', 'group': 'Supply', 'lag': 60},
        'rig_count_us_new':      {'zh': '美国石油钻井数', 'src': 'Baker Hughes', 'src_detail': 'Baker Hughes Rig Count', 'freq': 'weekly→monthly', 'group': 'Supply', 'lag': 3},
        'supply_saudi':          {'zh': '沙特原油产量', 'src': 'OPEC', 'src_detail': 'OPEC MOMR', 'freq': 'monthly', 'group': 'Supply', 'lag': 15},
        # Derived features (computed from source features)
        'vix_lag1':              {'zh': 'VIX滞后1期', 'src': '派生计算', 'src_detail': 'VIX t-1', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
        'vix_lag2':              {'zh': 'VIX滞后2期', 'src': '派生计算', 'src_detail': 'VIX t-2', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
        'geo_shock_count':       {'zh': '地缘冲击事件数', 'src': '派生计算', 'src_detail': 'GPR超阈值计数', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
        'geo_active_events':     {'zh': '活跃地缘事件数', 'src': '派生计算', 'src_detail': '事件时间线活跃计数', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
        'mom1m_lag1':            {'zh': '油价动量(1M滞后)', 'src': '派生计算', 'src_detail': 'WTI月收益率 t-1', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
        'hist_vol_12m':          {'zh': '12月历史波动率', 'src': '派生计算', 'src_detail': 'WTI 12M 滚动std', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
        'rsi12m':                {'zh': '12月RSI指标', 'src': '派生计算', 'src_detail': 'WTI 12M RSI', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
    }

    # Try to read actual panel data for live values
    panel = None
    for pp in ['output/panel_monthly_live.csv', 'output/panel_monthly.csv']:
        if os.path.exists(pp):
            try:
                panel = pd.read_csv(pp, index_col=0, parse_dates=True)
            except:
                pass
            break

    # Also try the static quality report as fallback
    static = _load_json(OUTPUT_FILES.get('quality', ''), {})

    result = {}
    from config import FEATURES
    all_feats = list(META.keys())
    # Also add any features in FEATURES list not in META
    for f in FEATURES:
        if f not in all_feats:
            all_feats.append(f)

    for feat in all_feats:
        meta = META.get(feat, {})
        sq = static.get(feat, {})

        # Get latest_value from panel
        latest_value = None
        total_months = 0
        missing = 0
        missing_rate = 0.0
        first_valid = None
        last_valid = None
        staleness = None
        status = 'OK'

        if panel is not None and feat in panel.columns:
            series = panel[feat].dropna()
            total_months = len(panel)
            missing = int(panel[feat].isna().sum())
            missing_rate = round(missing / total_months, 3) if total_months > 0 else 0
            if len(series) > 0:
                latest_value = round(float(series.iloc[-1]), 4)
                first_valid = str(series.index[0])[:10]
                last_valid = str(series.index[-1])[:10]
                staleness = (pd.Timestamp.now() - series.index[-1]).days
        else:
            # Fall back to static data
            latest_value = sq.get('latest_value')
            total_months = sq.get('total_months', 0)
            missing = sq.get('missing', 0)
            missing_rate = sq.get('missing_rate', 0)
            first_valid = sq.get('first_valid')
            last_valid = sq.get('last_valid')
            staleness = sq.get('staleness_days')

        # Determine status
        if missing_rate > 0.3:
            status = 'HIGH_MISSING'
        elif staleness and staleness > 60:
            status = 'STALE'
        else:
            status = 'OK'

        result[feat] = {
            'name_zh': meta.get('zh', feat),
            'source': meta.get('src', sq.get('source', 'CSV')),
            'source_detail': meta.get('src_detail', ''),
            'factor_group': meta.get('group', sq.get('factor_group', 'Other')),
            'frequency': meta.get('freq', sq.get('frequency', 'monthly')),
            'release_lag_days': meta.get('lag', sq.get('release_lag_days', 0)),
            'total_months': total_months,
            'missing': missing,
            'missing_rate': missing_rate,
            'first_valid': first_valid,
            'last_valid': last_valid,
            'staleness_days': staleness,
            'latest_value': latest_value,
            'status': status,
        }

    return result


@app.get("/api/lineage")
def get_lineage():
    lineage = _load_json(OUTPUT_FILES.get('lineage', ''), {})
    # Enrich sources if present
    if 'sources' in lineage:
        src = lineage['sources']
        # Fix CSV source name
        if 'CSV' in src:
            src['Baker Hughes'] = {'name': 'Baker Hughes Rig Count', 'url': 'https://rigcount.bakerhughes.com/', 'type': '公开CSV', 'features_count': 1}
            src['World Bank'] = {'name': 'World Bank Pink Sheet', 'url': 'https://www.worldbank.org/en/research/commodity-markets', 'type': '公开Excel', 'features_count': 1}
            src['OPEC'] = {'name': 'OPEC Monthly Oil Market Report', 'url': 'https://www.opec.org/opec_web/en/', 'type': '公开PDF/CSV', 'features_count': 1}
            src['GPR'] = {'name': 'Geopolitical Risk Index', 'url': 'https://www.matteoiacoviello.com/gpr.htm', 'type': '公开CSV', 'features_count': 1}
            del src['CSV']
    return lineage


@app.get("/api/feat_sel")
def get_feat_sel():
    return _load_json(OUTPUT_FILES['feat_sel'])


@app.get("/api/causal")
def get_causal():
    return _load_json(os.path.join(OUTPUT_DIR, 'causal_analysis.json'), {})


# ── AI Agent Chat ──

class ChatRequest(BaseModel):
    message: str
    session_id: Optional[str] = None

_sessions = {}

@app.post("/api/chat")
async def chat(req: ChatRequest):
    """AI Agent chat endpoint."""
    try:
        from agent.chat import chat_with_agent
        session_id = req.session_id or 'default'
        history = _sessions.get(session_id, [])
        reply, history = chat_with_agent(req.message, history)
        _sessions[session_id] = history
        return {"reply": reply}
    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        print(f"[AGENT ERROR] {type(e).__name__}: {e}\n{tb}")
        error_msg = f"⚠️ Agent 调用出错 ({type(e).__name__}): {str(e)}"
        if "timeout" in str(e).lower() or "connect" in str(e).lower():
            error_msg += "\n🔄 LLM 服务暂时不可用，请稍后重试。"
        return {"reply": error_msg}


# ═══════════════════════════════════════════════════════════
# Live Oil Prices — AKShare real-time daily data
# ═══════════════════════════════════════════════════════════

import time as _time

_price_cache = {"data": None, "ts": 0}
_news_cache = {"data": None, "ts": 0}
CACHE_TTL = 3600  # 1 hour cache


def _fetch_live_prices():
    """Fetch latest oil prices from AKShare (daily frequency)."""
    now = _time.time()
    if _price_cache["data"] and (now - _price_cache["ts"]) < CACHE_TTL:
        return _price_cache["data"]

    try:
        import akshare as ak

        result = {}

        # WTI Crude (CL)
        try:
            df_cl = ak.futures_foreign_hist(symbol='CL')
            if len(df_cl) >= 2:
                cur = df_cl.iloc[-1]
                prev = df_cl.iloc[-2]
                price = float(cur['close'])
                change = round(price - float(prev['close']), 2)
                pct = round(change / float(prev['close']) * 100, 2)
                result['wti'] = {
                    'price': price, 'change': change, 'pct': pct,
                    'date': str(cur['date'])[:10]
                }
        except Exception as e:
            print(f"[LIVE] WTI fetch failed: {e}")

        # Brent Crude — estimate from WTI + typical spread (~$3-5)
        if 'wti' in result:
            wti_p = result['wti']['price']
            # Use historical Brent-WTI spread
            brent_spread = 3.8
            brent_price = round(wti_p + brent_spread, 2)
            brent_change = result['wti']['change']
            brent_pct = round(brent_change / (brent_price - brent_change) * 100, 2)
            result['brent'] = {
                'price': brent_price, 'change': brent_change, 'pct': brent_pct,
                'date': result['wti']['date'],
                'note': 'estimated_from_spread'
            }

        # Natural Gas (NG - Henry Hub)
        try:
            df_ng = ak.futures_foreign_hist(symbol='NG')
            if len(df_ng) >= 2:
                cur = df_ng.iloc[-1]
                prev = df_ng.iloc[-2]
                price = float(cur['close'])
                change = round(price - float(prev['close']), 2)
                pct = round(change / float(prev['close']) * 100, 2)
                result['natgas'] = {
                    'price': price, 'change': change, 'pct': pct,
                    'date': str(cur['date'])[:10]
                }
        except Exception as e:
            print(f"[LIVE] NG fetch failed: {e}")

        if result:
            _price_cache["data"] = result
            _price_cache["ts"] = now
            print(f"[LIVE] Prices updated: WTI=${result.get('wti',{}).get('price','?')}, "
                  f"Brent=${result.get('brent',{}).get('price','?')}, "
                  f"NG=${result.get('natgas',{}).get('price','?')}")

        return result

    except Exception as e:
        print(f"[LIVE] Price fetch error: {e}")
        return {}


def _fetch_live_news():
    """Fetch latest oil-related news from Google News RSS."""
    now = _time.time()
    if _news_cache["data"] and (now - _news_cache["ts"]) < CACHE_TTL:
        return _news_cache["data"]

    import urllib.request
    import re
    from datetime import datetime

    news_items = []

    # Google News RSS for oil price
    feeds = [
        ("https://news.google.com/rss/search?q=oil+price+crude+OPEC&hl=en-US&gl=US&ceid=US:en", "en"),
        ("https://news.google.com/rss/search?q=油价+原油+OPEC&hl=zh-CN&gl=CN&ceid=CN:zh-Hans", "zh"),
    ]

    for feed_url, lang in feeds:
        try:
            req = urllib.request.Request(feed_url, headers={'User-Agent': 'Mozilla/5.0'})
            with urllib.request.urlopen(req, timeout=8) as resp:
                data = resp.read().decode('utf-8', errors='replace')

            # Parse XML items
            items = re.findall(r'<item>(.*?)</item>', data, re.DOTALL)

            for item_xml in items[:8]:
                title = re.search(r'<title>(.*?)</title>', item_xml)
                source = re.search(r'<source[^>]*>(.*?)</source>', item_xml)
                pub_date = re.search(r'<pubDate>(.*?)</pubDate>', item_xml)

                if title:
                    title_text = title.group(1).strip()
                    # Clean HTML entities
                    title_text = title_text.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>').replace('&#39;', "'").replace('&quot;', '"')

                    src = source.group(1).strip() if source else 'News'

                    # Parse date
                    date_str = ''
                    if pub_date:
                        try:
                            dt = datetime.strptime(pub_date.group(1).strip()[:25],
                                                   '%a, %d %b %Y %H:%M:%S')
                            date_str = dt.strftime('%m-%d %H:%M')
                        except:
                            date_str = pub_date.group(1).strip()[:16]

                    # Auto-tag based on keywords
                    tag = _auto_tag(title_text)

                    news_items.append({
                        'text': title_text,
                        'src': src,
                        'time': date_str,
                        'tag': tag,
                        'lang': lang,
                    })

        except Exception as e:
            print(f"[NEWS] Feed fetch error ({lang}): {e}")

    if news_items:
        _news_cache["data"] = news_items[:15]
        _news_cache["ts"] = now
        print(f"[NEWS] Fetched {len(news_items)} news items")

    return news_items[:15]


def _auto_tag(text: str) -> str:
    """Auto-tag news based on keywords."""
    t = text.lower()
    if any(w in t for w in ['opec', 'supply', 'production', 'output', 'barrel',
                            '产量', '减产', '增产', '供给', '库存', 'inventory']):
        return '供给'
    if any(w in t for w in ['demand', 'consumption', 'growth', 'recession',
                            '需求', '消费', '增长', '衰退', 'gdp']):
        return '需求'
    if any(w in t for w in ['war', 'sanction', 'iran', 'russia', 'conflict', 'military',
                            '制裁', '冲突', '地缘', '战争', 'tariff', '关税', 'trump']):
        return '地缘'
    if any(w in t for w in ['fed', 'rate', 'inflation', 'dollar', 'central bank',
                            '利率', '通胀', '美联储', '央行', '美元']):
        return '宏观'
    if any(w in t for w in ['renewable', 'ev', 'solar', 'wind', 'transition', 'climate',
                            '新能源', '碳', '气候', '转型']):
        return '政策'
    return '市场'


@app.get("/api/live-prices")
def api_live_prices():
    """Return latest daily oil prices (WTI, Brent, Natural Gas)."""
    data = _fetch_live_prices()
    if not data:
        raise HTTPException(status_code=503, detail="Unable to fetch live prices")
    return data


@app.get("/api/live-news")
def api_live_news():
    """Return latest oil-related news from RSS feeds."""
    data = _fetch_live_news()
    return {"items": data}


# ═══════════════════════════════════════════════════════════
# Static file serving (for Docker / HuggingFace deployment)
# ═══════════════════════════════════════════════════════════

_frontend_dist = os.path.join(BASE_DIR, 'frontend', 'dist')
if os.path.isdir(_frontend_dist):
    from fastapi.staticfiles import StaticFiles
    from fastapi.responses import FileResponse

    # Serve static assets (JS/CSS/images)
    app.mount("/assets", StaticFiles(directory=os.path.join(_frontend_dist, "assets")), name="static-assets")

    # Serve favicon and other root files
    @app.get("/favicon.svg")
    async def favicon():
        return FileResponse(os.path.join(_frontend_dist, "favicon.svg"))

    # Catch-all: serve index.html for SPA routing
    @app.get("/{full_path:path}")
    async def serve_spa(full_path: str):
        file_path = os.path.join(_frontend_dist, full_path)
        if os.path.isfile(file_path):
            return FileResponse(file_path)
        return FileResponse(os.path.join(_frontend_dist, "index.html"))


if __name__ == '__main__':
    import uvicorn
    port = int(os.environ.get("PORT", 8765))
    print("=" * 60)
    print(f"油刃有余 OilVerse API — http://localhost:{port}")
    print("=" * 60)
    uvicorn.run(app, host="0.0.0.0", port=port)