305 lines
9.3 KiB
Python
305 lines
9.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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🛡️ 科创企业智能风控与核保系统 - 首页 / 风控大屏入口
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"""
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import sys
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import os
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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# 确保项目根目录在 Python 路径中
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from config import PAGE_TITLE, PAGE_ICON, LAYOUT
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from collectors.financial_collector import get_all_companies
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from risk_engine.risk_scorer import calculate_six_dimension_scores, get_risk_level
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# ============================================================
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# 页面配置
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# ============================================================
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st.set_page_config(
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page_title=PAGE_TITLE,
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page_icon=PAGE_ICON,
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layout=LAYOUT,
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initial_sidebar_state="expanded",
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)
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# ============================================================
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# 自定义样式
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# ============================================================
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st.markdown("""
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<style>
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/* 主标题渐变 */
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.main-title {
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background: linear-gradient(120deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%);
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padding: 30px;
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border-radius: 15px;
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text-align: center;
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margin-bottom: 30px;
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box-shadow: 0 4px 15px rgba(0,0,0,0.3);
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}
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.main-title h1 {
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color: #e94560;
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font-size: 2.2em;
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margin: 0;
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}
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.main-title p {
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color: #a8a8b3;
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font-size: 1.1em;
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margin-top: 10px;
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}
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/* 统计卡片 */
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.stat-card {
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background: linear-gradient(135deg, #1a1a2e, #16213e);
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padding: 20px;
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border-radius: 12px;
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text-align: center;
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border: 1px solid #2a2a4a;
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transition: transform 0.2s;
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}
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.stat-card:hover {
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transform: translateY(-2px);
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}
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.stat-card .number {
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font-size: 2.5em;
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font-weight: bold;
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color: #e94560;
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}
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.stat-card .label {
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color: #a8a8b3;
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font-size: 0.95em;
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margin-top: 5px;
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}
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/* 企业卡片 */
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.company-card {
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background: #16213e;
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padding: 15px;
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border-radius: 10px;
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margin: 8px 0;
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border-left: 4px solid;
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}
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/* 侧边栏 */
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[data-testid="stSidebar"] {
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background: linear-gradient(180deg, #1a1a2e 0%, #0f0f23 100%);
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}
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</style>
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""", unsafe_allow_html=True)
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from utils.session_helper import render_sidebar_global_company_selector
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# ============================================================
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# 侧边栏
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# ============================================================
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with st.sidebar:
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render_sidebar_global_company_selector()
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st.markdown("---")
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st.markdown("### 🛡️ 系统导航")
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st.markdown("---")
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st.markdown("""
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**功能模块**
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- 🏠 系统首页
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- 📊 企业风险概览
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- 🕸️ 供应链知识图谱
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- ⚖️ 多智能体辩论诊断
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- 💰 动态定价与核保
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""")
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st.markdown("---")
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st.markdown("""
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**技术栈**
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- 🤖 DeepSeek API (LLM)
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- 🕸️ NetworkX (知识图谱)
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- 📊 Plotly (可视化)
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- 🔧 Streamlit (Web框架)
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""")
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st.markdown("---")
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st.caption("中国平安 × 挑战杯 · 科创风控原型")
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# ============================================================
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# 主页内容
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# ============================================================
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# 标题
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st.markdown("""
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<div class="main-title">
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<h1>🛡️ 科创企业智能风控与核保系统</h1>
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<p>基于多智能体辩论 × 知识图谱 × 动态定价的全链条风控平台</p>
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<p style="font-size: 0.85em; color: #666;">中国青基会平安励志计划 · XH-202626 · 科创企业特有风险的识别与管理</p>
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</div>
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""", unsafe_allow_html=True)
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# 加载企业数据
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companies = get_all_companies()
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# ============================================================
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# 统计概览卡片
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# ============================================================
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st.markdown("### 📊 系统概览")
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# 计算所有企业的风险评分
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risk_data = []
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for comp in companies:
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scores = calculate_six_dimension_scores(comp)
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risk_data.append({
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"company": comp["short_name"],
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"stock_code": comp["stock_code"],
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"industry": comp["industry"],
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"sector": comp["sector"],
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"comprehensive_score": scores["comprehensive_score"],
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"risk_level": scores["risk_level"]["level"],
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**scores["scores"],
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})
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df = pd.DataFrame(risk_data)
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# 统计卡片
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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st.markdown(f"""
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<div class="stat-card">
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<div class="number">{len(companies)}</div>
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<div class="label">监控企业总数</div>
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</div>
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""", unsafe_allow_html=True)
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with col2:
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high_risk = len(df[df["comprehensive_score"] >= 70])
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st.markdown(f"""
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<div class="stat-card">
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<div class="number" style="color: #F44336;">{high_risk}</div>
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<div class="label">⚠️ 高风险企业</div>
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</div>
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""", unsafe_allow_html=True)
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with col3:
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med_risk = len(df[(df["comprehensive_score"] >= 40) & (df["comprehensive_score"] < 70)])
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st.markdown(f"""
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<div class="stat-card">
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<div class="number" style="color: #FF9800;">{med_risk}</div>
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<div class="label">🟡 中风险企业</div>
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</div>
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""", unsafe_allow_html=True)
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with col4:
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low_risk = len(df[df["comprehensive_score"] < 40])
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st.markdown(f"""
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<div class="stat-card">
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<div class="number" style="color: #4CAF50;">{low_risk}</div>
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<div class="label">🟢 低风险企业</div>
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</div>
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""", unsafe_allow_html=True)
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with col5:
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sanctioned = len([c for c in companies if "被列入" in c.get("compliance", {}).get("entity_list_status", "")])
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st.markdown(f"""
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<div class="stat-card">
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<div class="number" style="color: #B71C1C;">{sanctioned}</div>
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<div class="label">⛔ 受制裁企业</div>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("<br>", unsafe_allow_html=True)
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# ============================================================
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# 风险分布图
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# ============================================================
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col_left, col_right = st.columns([3, 2])
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with col_left:
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st.markdown("#### 🎯 企业综合风险评分分布")
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# 水平柱状图，按风险排序
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df_sorted = df.sort_values("comprehensive_score", ascending=True)
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colors = []
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for score in df_sorted["comprehensive_score"]:
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if score >= 70:
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colors.append("#F44336")
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elif score >= 50:
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colors.append("#FF9800")
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elif score >= 30:
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colors.append("#FFC107")
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else:
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colors.append("#4CAF50")
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fig = go.Figure(go.Bar(
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x=df_sorted["comprehensive_score"],
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y=df_sorted["company"],
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orientation="h",
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marker_color=colors,
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text=df_sorted["comprehensive_score"],
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textposition="outside",
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))
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fig.update_layout(
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height=400,
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margin=dict(l=0, r=30, t=10, b=10),
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plot_bgcolor="rgba(0,0,0,0)",
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paper_bgcolor="rgba(0,0,0,0)",
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font=dict(color="white"),
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xaxis=dict(
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title="综合风险评分",
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range=[0, 105],
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gridcolor="rgba(255,255,255,0.1)",
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),
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yaxis=dict(gridcolor="rgba(255,255,255,0.1)"),
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)
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# 添加阈值线
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fig.add_vline(x=70, line_dash="dash", line_color="#F44336",
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annotation_text="高风险线(70)", annotation_position="top right")
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fig.add_vline(x=40, line_dash="dash", line_color="#FF9800",
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annotation_text="中风险线(40)", annotation_position="top right")
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st.plotly_chart(fig, use_container_width=True)
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with col_right:
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st.markdown("#### 🏷️ 行业风险热力")
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# 按行业汇总
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sector_risk = df.groupby("sector")["comprehensive_score"].mean().reset_index()
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sector_risk.columns = ["行业", "平均风险"]
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sector_risk = sector_risk.sort_values("平均风险", ascending=False)
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fig2 = px.bar(
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sector_risk, x="行业", y="平均风险",
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color="平均风险",
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color_continuous_scale=["#4CAF50", "#FFC107", "#F44336"],
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range_color=[0, 100],
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)
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fig2.update_layout(
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height=400,
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margin=dict(l=0, r=0, t=10, b=10),
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plot_bgcolor="rgba(0,0,0,0)",
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paper_bgcolor="rgba(0,0,0,0)",
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font=dict(color="white"),
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showlegend=False,
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coloraxis_showscale=False,
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)
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st.plotly_chart(fig2, use_container_width=True)
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# ============================================================
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# 企业列表
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# ============================================================
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st.markdown("### 📋 企业风险速览")
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# 格式化数据表格
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display_df = df[["company", "stock_code", "industry", "comprehensive_score", "risk_level"]].copy()
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display_df.columns = ["企业名称", "股票代码", "行业", "综合风险评分", "风险等级"]
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st.dataframe(display_df, use_container_width=True, height=400)
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# ============================================================
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# 底部信息
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# ============================================================
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st.markdown("---")
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st.markdown("""
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<div style="text-align: center; color: #666; font-size: 0.85em;">
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<p>🛡️ 科创企业智能风控与核保系统 v1.0</p>
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<p>技术架构: Multi-Agent 交叉验证 × 供应链知识图谱 × 动态保险定价</p>
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<p>数据来源: 科创板公开年报 · BIS 实体清单 · 网信办算法备案公示</p>
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</div>
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""", unsafe_allow_html=True)
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