# -*- coding: utf-8 -*-
"""
📊 企业风险概览页面
选择企业 → 六维风险雷达图 + 关键指标卡片 + 详细财务数据
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import streamlit as st
import plotly.graph_objects as go
import plotly.express as px

from collectors.financial_collector import get_all_companies, get_company_by_code
from risk_engine.risk_scorer import calculate_six_dimension_scores, get_risk_level

from utils.session_helper import render_company_selector, render_sidebar_global_company_selector

st.set_page_config(page_title="企业风险概览", page_icon="📊", layout="wide")

with st.sidebar:
    render_sidebar_global_company_selector()
    st.markdown("---")

st.markdown("# 📊 企业风险概览")
st.markdown("选择一家科创企业，查看其六维风险画像和关键指标。")

# 企业选择（全局同步）
company = render_company_selector("🏢 选择目标企业", key_suffix="overview_page")
stock_code = company["stock_code"] if company else "688256"

if company:
    # 计算六维评分
    risk_result = calculate_six_dimension_scores(company)
    scores = risk_result["scores"]
    comprehensive = risk_result["comprehensive_score"]
    level_info = risk_result["risk_level"]

    # ============================================================
    # 企业信息 + 综合评分
    # ============================================================
    col1, col2 = st.columns([2, 1])

    with col1:
        st.markdown(f"### {company['short_name']}")
        st.markdown(f"**行业**: {company['industry']} | **领域**: {company['sector']} | **代码**: {company['stock_code']}")
        st.markdown(f"**简介**: {company['description']}")

        # 核心人员
        st.markdown("#### 👤 核心技术人员")
        for p in company.get("core_tech_personnel", []):
            status_emoji = "✅" if "在职" in p["status"] else "⚠️"
            st.markdown(f"- {status_emoji} **{p['name']}** ({p['title']}) - 重要性: {p['importance']} - 状态: {p['status']}")

    with col2:
        # 综合风险仪表盘
        fig = go.Figure(go.Indicator(
            mode="gauge+number",
            value=comprehensive,
            title={"text": "综合风险评分", "font": {"color": "white"}},
            number={"font": {"color": "white", "size": 48}},
            gauge={
                "axis": {"range": [0, 100], "tickcolor": "white"},
                "bar": {"color": level_info["color"]},
                "steps": [
                    {"range": [0, 30], "color": "rgba(76,175,80,0.3)"},
                    {"range": [30, 50], "color": "rgba(255,193,7,0.3)"},
                    {"range": [50, 70], "color": "rgba(255,152,0,0.3)"},
                    {"range": [70, 100], "color": "rgba(244,67,54,0.3)"},
                ],
                "threshold": {
                    "line": {"color": "red", "width": 4},
                    "thickness": 0.75,
                    "value": 70,
                },
            },
        ))
        fig.update_layout(
            height=250,
            margin=dict(l=20, r=20, t=40, b=10),
            paper_bgcolor="rgba(0,0,0,0)",
            font=dict(color="white"),
        )
        st.plotly_chart(fig, use_container_width=True)
        st.markdown(f"<div style='text-align:center; font-size:1.2em;'>"
                     f"{level_info['emoji']} 风险等级: <b>{level_info['level']}</b></div>",
                     unsafe_allow_html=True)

    st.markdown("---")

    # ============================================================
    # 六维风险雷达图
    # ============================================================
    col_radar, col_detail = st.columns([1, 1])

    with col_radar:
        st.markdown("#### 🎯 六维风险雷达图")

        dim_names_cn = ["技术路线颠覆", "核心人员流失", "算法/数据合规",
                        "地缘政治/出口管制", "研发资本化操纵", "客户/供应商集中"]
        dim_keys = ["tech_disruption", "talent_loss", "algo_compliance",
                    "geopolitical", "rd_capitalization", "concentration"]
        values = [scores[k] for k in dim_keys]

        fig = go.Figure()
        fig.add_trace(go.Scatterpolar(
            r=values + [values[0]],
            theta=dim_names_cn + [dim_names_cn[0]],
            fill="toself",
            fillcolor="rgba(233,69,96,0.3)",
            line=dict(color="#e94560", width=2),
            marker=dict(size=8, color="#e94560"),
            name=company["short_name"],
        ))

        # 添加警戒线
        fig.add_trace(go.Scatterpolar(
            r=[70] * 7,
            theta=dim_names_cn + [dim_names_cn[0]],
            line=dict(color="rgba(244,67,54,0.5)", dash="dash", width=1),
            name="高风险线(70)",
        ))

        fig.update_layout(
            polar=dict(
                radialaxis=dict(visible=True, range=[0, 100], tickfont=dict(color="white")),
                angularaxis=dict(tickfont=dict(color="white", size=11)),
                bgcolor="rgba(0,0,0,0)",
            ),
            height=420,
            margin=dict(l=60, r=60, t=30, b=30),
            paper_bgcolor="rgba(0,0,0,0)",
            font=dict(color="white"),
            showlegend=True,
            legend=dict(x=0, y=-0.15),
        )
        st.plotly_chart(fig, use_container_width=True)

    with col_detail:
        st.markdown("#### 📋 各维度风险详情")
        for k in dim_keys:
            detail = risk_result["dimension_details"][k]
            score = detail["score"]
            level = detail["level"]
            emoji = level["emoji"]
            color = level["color"]

            st.markdown(
                f"<div style='background:{color}22; padding:10px; border-radius:8px; "
                f"margin:5px 0; border-left:4px solid {color};'>"
                f"<b>{emoji} {detail['name']}</b>: {score}分 ({level['level']})</div>",
                unsafe_allow_html=True,
            )

    st.markdown("---")

    # ============================================================
    # 关键财务指标
    # ============================================================
    st.markdown("#### 💰 关键财务指标")
    fin = company.get("financials", {})

    col_f1, col_f2, col_f3, col_f4 = st.columns(4)
    with col_f1:
        revenue = fin.get("revenue_2024", 0)
        st.metric("营业收入", f"¥{revenue/1e8:.1f}亿")
    with col_f2:
        profit = fin.get("net_profit_2024", 0)
        st.metric("净利润", f"¥{profit/1e8:.1f}亿",
                  delta="盈利" if profit > 0 else "亏损",
                  delta_color="normal" if profit > 0 else "inverse")
    with col_f3:
        rd = fin.get("rd_expense_2024", 0)
        st.metric("研发费用", f"¥{rd/1e8:.1f}亿")
    with col_f4:
        cap_rate = fin.get("rd_capitalization_rate", 0)
        st.metric("研发资本化率", f"{cap_rate:.0%}",
                  delta="⚠️ 偏高" if cap_rate > 0.3 else "正常",
                  delta_color="inverse" if cap_rate > 0.3 else "normal")

    col_f5, col_f6, col_f7, col_f8 = st.columns(4)
    with col_f5:
        st.metric("研发/营收比", f"{fin.get('rd_revenue_ratio', 0):.1%}")
    with col_f6:
        st.metric("前5大客户占比", f"{fin.get('top5_customer_ratio', 0):.0%}")
    with col_f7:
        st.metric("应收周转率", f"{fin.get('receivable_turnover', 0):.1f}次/年")
    with col_f8:
        st.metric("现金流比率", f"{fin.get('cash_flow_ratio', 0):.2f}")

    # ============================================================
    # 技术路线 & 合规信息
    # ============================================================
    st.markdown("---")
    col_tech, col_comp = st.columns(2)

    with col_tech:
        st.markdown("#### 🔬 技术路线")
        tech = company.get("tech_route", {})
        st.markdown(f"**当前技术**: {tech.get('current_tech', '未知')}")
        st.markdown(f"**技术壁垒**: {tech.get('tech_moat', '未知')}")
        st.markdown(f"**专利数量**: {tech.get('patent_count', 0)} 件")
        st.markdown("**竞争技术路线**:")
        for ct in tech.get("competing_techs", []):
            st.markdown(f"  - ⚔️ {ct}")

    with col_comp:
        st.markdown("#### 📋 合规状态")
        comp_info = company.get("compliance", {})
        st.markdown(f"**算法备案**: {comp_info.get('algo_filing_status', '未知')}")
        st.markdown(f"**数据出境风险**: {comp_info.get('data_export_risk', '未知')}")

        entity_status = comp_info.get("entity_list_status", "未知")
        if "被列入" in entity_status:
            st.error(f"⛔ 实体清单: {entity_status}")
            if comp_info.get("sanctions_detail"):
                st.warning(f"制裁详情: {comp_info['sanctions_detail']}")
        else:
            st.success(f"✅ 实体清单: {entity_status}")