akaAKR47/streamlit_app.py

import streamlit as st
import pandas as pd
import numpy as np
from agent import ChurnPredictionAgent, CustomerData

# 设置页面标题和布局
st.set_page_config(
    page_title="客户流失预测系统",
    page_icon="📊",
    layout="wide"
)

# 页面标题
st.title("📊 客户流失预测与行动建议系统")

# 创建Agent实例
agent = ChurnPredictionAgent()

# 侧边栏：客户信息输入
st.sidebar.header("客户信息输入")

# 客户信息表单
with st.sidebar.form("customer_form"):
    # 基本信息
    col1, col2 = st.columns(2)
    
    with col1:
        gender = st.selectbox("性别", ["Male", "Female"])
        SeniorCitizen = st.selectbox("是否为老年人", [0, 1])
        Partner = st.selectbox("是否有伴侣", ["Yes", "No"])
        Dependents = st.selectbox("是否有家属", ["Yes", "No"])
        tenure = st.number_input("在网时长（月）", min_value=0, max_value=100, value=12)
    
    with col2:
        PhoneService = st.selectbox("是否开通电话服务", ["Yes", "No"])
        MultipleLines = st.selectbox("是否开通多条线路", ["Yes", "No", "No phone service"])
        InternetService = st.selectbox("网络服务类型", ["DSL", "Fiber optic", "No"])
        OnlineSecurity = st.selectbox("是否开通在线安全服务", ["Yes", "No", "No internet service"])
        OnlineBackup = st.selectbox("是否开通在线备份服务", ["Yes", "No", "No internet service"])
    
    # 服务信息
    col3, col4 = st.columns(2)
    
    with col3:
        DeviceProtection = st.selectbox("是否开通设备保护服务", ["Yes", "No", "No internet service"])
        TechSupport = st.selectbox("是否开通技术支持服务", ["Yes", "No", "No internet service"])
        StreamingTV = st.selectbox("是否开通流媒体电视服务", ["Yes", "No", "No internet service"])
        StreamingMovies = st.selectbox("是否开通流媒体电影服务", ["Yes", "No", "No internet service"])
    
    with col4:
        Contract = st.selectbox("合同类型", ["Month-to-month", "One year", "Two year"])
        PaperlessBilling = st.selectbox("是否使用无纸化账单", ["Yes", "No"])
        PaymentMethod = st.selectbox("支付方式", [
            "Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"
        ])
        MonthlyCharges = st.number_input("月费用", min_value=0.0, max_value=200.0, value=50.0, step=0.01)
        TotalCharges = st.number_input("总费用", min_value=0.0, max_value=10000.0, value=600.0, step=0.01)
    
    # 提交按钮
    submit_button = st.form_submit_button("🚀 预测流失风险")

# 主内容区
if submit_button:
    # 创建CustomerData实例
    customer_data = CustomerData(
        gender=gender,
        SeniorCitizen=SeniorCitizen,
        Partner=Partner,
        Dependents=Dependents,
        tenure=tenure,
        PhoneService=PhoneService,
        MultipleLines=MultipleLines,
        InternetService=InternetService,
        OnlineSecurity=OnlineSecurity,
        OnlineBackup=OnlineBackup,
        DeviceProtection=DeviceProtection,
        TechSupport=TechSupport,
        StreamingTV=StreamingTV,
        StreamingMovies=StreamingMovies,
        Contract=Contract,
        PaperlessBilling=PaperlessBilling,
        PaymentMethod=PaymentMethod,
        MonthlyCharges=MonthlyCharges,
        TotalCharges=TotalCharges
    )
    
    # 使用ML预测工具
    with st.spinner("🔄 正在预测流失风险..."):
        prediction_result = agent.predict_churn(customer_data)
    
    # 显示预测结果
    st.header("📋 预测结果")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.subheader("客户基本信息")
        info_df = pd.DataFrame({
            "属性": ["性别", "是否为老年人", "是否有伴侣", "是否有家属", "在网时长（月）"],
            "值": [gender, SeniorCitizen, Partner, Dependents, tenure]
        })
        st.dataframe(info_df, use_container_width=True, hide_index=True)
    
    with col2:
        st.subheader("服务信息")
        service_df = pd.DataFrame({
            "属性": ["合同类型", "网络服务类型", "支付方式", "月费用", "总费用"],
            "值": [Contract, InternetService, PaymentMethod, MonthlyCharges, TotalCharges]
        })
        st.dataframe(service_df, use_container_width=True, hide_index=True)
    
    # 预测结果卡片
    st.subheader("🎯 流失预测")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric(
            label="预测结果",
            value="会流失" if prediction_result.prediction == 1 else "不会流失",
            delta="高风险" if prediction_result.prediction == 1 else "低风险",
            delta_color="inverse"
        )
    
    with col2:
        st.metric(
            label="流失概率",
            value=f"{prediction_result.probability:.2%}",
            delta=f"{prediction_result.probability:.2%}",
            delta_color="inverse"
        )
    
    with col3:
        st.metric(
            label="使用模型",
            value=prediction_result.model_used.upper(),
            delta="LightGBM",
            delta_color="off"
        )
    
    # 行动建议
    st.header("💡 行动建议")
    
    with st.spinner("🔄 正在生成行动建议..."):
        suggestions = agent.get_action_suggestions(
            customer_id="CUST-" + np.random.choice(1000, size=1)[0].astype(str),
            prediction=prediction_result.prediction,
            probability=prediction_result.probability,
            customer_data=customer_data
        )
    
    # 显示行动建议
    st.subheader("📋 个性化行动建议")
    
    for i, suggestion in enumerate(suggestions.suggestions, 1):
        with st.expander(f"建议 {i}"):
            st.write(suggestion)
    
    # 数据可视化
    st.header("📊 数据可视化")
    
    # 流失概率仪表盘
    st.subheader("流失概率仪表盘")
    
    # 创建仪表盘
    col1, col2 = st.columns(2)
    
    with col1:
        # 流失概率图表（使用Streamlit内置的进度条）
        st.subheader(f"流失概率: {prediction_result.probability:.2%}")
        
        # 进度条显示流失概率
        st.progress(prediction_result.probability, text=f"流失概率: {prediction_result.probability:.2%}")
        
        # 风险等级
        if prediction_result.probability < 0.3:
            risk_level = "低风险"
            risk_color = "green"
        elif prediction_result.probability < 0.7:
            risk_level = "中风险"
            risk_color = "yellow"
        else:
            risk_level = "高风险"
            risk_color = "red"
        
        st.markdown(f"**风险等级**: <span style='color:{risk_color}; font-size:20px;'>{risk_level}</span>", unsafe_allow_html=True)
    
    with col2:
        # 客户特征重要性
        st.subheader("客户特征分析")
        
        # 示例特征重要性数据（实际应用中应从模型获取）
        feature_importance = {
            "合同类型": 0.25,
            "网络服务类型": 0.20,
            "在网时长": 0.15,
            "月费用": 0.12,
            "是否开通技术支持": 0.10,
            "支付方式": 0.08,
            "是否开通在线安全服务": 0.05,
            "是否有伴侣": 0.03,
            "是否有家属": 0.02
        }
        
        feature_df = pd.DataFrame({
            "特征": list(feature_importance.keys()),
            "重要性": list(feature_importance.values())
        }).sort_values(by="重要性", ascending=False)
        
        st.bar_chart(feature_df.set_index("特征"), use_container_width=True, color="#1f77b4")
    
    # 系统信息
    st.header("ℹ️ 系统信息")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.subheader("模型性能")
        st.markdown("- **模型类型**: LightGBM")
        st.markdown("- **ROC-AUC**: 0.8352")
        st.markdown("- **F1分数**: 0.5731")
        st.markdown("- **训练样本数**: 7043")
    
    with col2:
        st.subheader("系统功能")
        st.markdown("✅ 客户流失预测")
        st.markdown("✅ 个性化行动建议")
        st.markdown("✅ 数据可视化分析")
        st.markdown("✅ 交互式用户界面")
else:
    # 初始页面
    st.info("请在左侧填写客户信息，点击'🚀 预测流失风险'按钮开始预测")
    
    # 系统介绍
    st.header("ℹ️ 系统介绍")
    
    st.markdown("""        
    本系统基于机器学习和AI Agent技术，实现了客户流失预测与行动建议的闭环。
    
    ### 系统功能
    - **客户流失预测**: 使用LightGBM模型预测客户流失概率
    - **个性化行动建议**: 根据客户特征生成可执行的行动建议
    - **数据可视化分析**: 直观展示预测结果和客户特征重要性
    
    ### 技术栈
    - **机器学习**: LightGBM、Logistic Regression
    - **数据处理**: Polars、Pandas
    - **AI Agent**: Pydantic
    - **Web框架**: Streamlit
    
    ### 如何使用
    1. 在左侧填写客户信息
    2. 点击'🚀 预测流失风险'按钮
    3. 查看预测结果和行动建议
    4. 分析客户特征重要性
    """)