from pydantic import BaseModel, Field
import joblib
import json
import pandas as pd
import numpy as np
import logging
import os
import random

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# ==========================================
# 1. 定义 Pydantic 数据模型
# ==========================================

class CustomerFeatures(BaseModel):
    """客户特征输入模型"""
    age: int = Field(ge=18, le=120, description="客户年龄")
    job: str = Field(description="职业")
    marital: str = Field(description="婚姻状况")
    education: str = Field(description="教育程度")
    default: str = Field(pattern="^(yes|no)$", description="是否有违约记录")
    balance: int = Field(description="账户余额")
    housing: str = Field(pattern="^(yes|no)$", description="是否有住房贷款")
    loan: str = Field(pattern="^(yes|no)$", description="是否有个人贷款")
    contact: str = Field(description="联系方式")
    day: int = Field(ge=1, le=31, description="最后联系日")
    month: str = Field(description="最后联系月份")
    campaign: int = Field(ge=1, description="本次活动联系次数")
    pdays: int = Field(description="距离上次联系天数 (-1代表未联系)")
    previous: int = Field(ge=0, description="活动前联系次数")
    poutcome: str = Field(description="上次活动结果")
    
    # 注意：我们不包含 duration，因为它是事后变量

class Decision(BaseModel):
    """Agent 输出的结构化决策"""
    risk_score: float = Field(ge=0, le=1, description="预测购买概率 (0-1)")
    customer_segment: str = Field(description="客户分群 (如: 高价值/潜在/沉睡)")
    decision: str = Field(description="建议策略 (如: 立即致电/邮件触达/放弃)")
    actions: list[str] = Field(description="可执行动作清单")
    rationale: str = Field(description="决策依据 (结合模型预测与业务规则)")

# ==========================================
# 2. 定义 Agent 类
# ==========================================

class MarketingAgent:
    def __init__(self, model_path="models/model_artifacts.pkl"):
        self.model_path = model_path
        self.artifacts = None
        self._load_model()
        
    def _load_model(self):
        if os.path.exists(self.model_path):
            self.artifacts = joblib.load(self.model_path)
            logger.info(f"Agent 已加载模型: {self.model_path}")
        else:
            logger.warning(f"模型文件不存在: {self.model_path}，Agent 将无法进行预测")

    def predict_risk(self, features: CustomerFeatures) -> dict:
        """
        Tool 1: 调用 ML 模型预测购买概率
        """
        if not self.artifacts:
            return {"score": 0.0, "reason": "Model not loaded"}
            
        # 转换输入为 DataFrame
        data = features.model_dump()
        df = pd.DataFrame([data])
        
        # 预处理 (使用训练时保存的 encoder)
        # 注意：这里需要严格复现训练时的预处理逻辑
        # 训练时我们做了 Label Encoding
        for col, le in self.artifacts['encoders'].items():
            if col in df.columns:
                # 处理未知类别
                try:
                    df[col] = le.transform(df[col].astype(str))
                except:
                    # 遇到未知类别，这里简单处理为 0 (或者 mode)
                    logger.warning(f"Unknown category in {col}")
                    df[col] = 0
        
        # 确保列顺序一致
        # 我们训练时用了 X (df.drop(target))
        # 这里需要筛选出 numeric_cols + categorical_cols
        # 简单起见，我们假设 feature names 保存了顺序
        feature_names = self.artifacts['features']
        
        # 补齐可能缺失的列
        for col in feature_names:
            if col not in df.columns:
                df[col] = 0
                
        X_input = df[feature_names]
        
        # 预测
        model = self.artifacts['lgb_model'] # 优先使用 LightGBM
        prob = model.predict_proba(X_input)[0][1]
        
        return {
            "score": float(prob),
            "top_features": ["balance", "poutcome"] # 这里简化，实际可用 SHAP
        }

    def get_strategy(self, score: float) -> dict:
        """
        Tool 2: 规则引擎/检索工具
        """
        if score > 0.6:
            return {
                "segment": "高意向 VIP",
                "action_type": "人工介入",
                "templates": ["尊贵的客户，鉴于您...", "专属理财经理一对一服务"]
            }
        elif score > 0.3:
            return {
                "segment": "潜在客户",
                "action_type": "自动化营销",
                "templates": ["你好，近期理财活动...", "点击领取加息券"]
            }
        else:
            return {
                "segment": "低意向群体",
                "action_type": "静默/邮件",
                "templates": ["月度财经摘要"]
            }

    def run(self, features: CustomerFeatures) -> Decision:
        """
        Agent 主流程
        """
        logger.info(f"Agent 正在处理客户: {features.job}, {features.age}岁")
        
        # 1. 感知 (调用 ML 工具)
        pred_result = self.predict_risk(features)
        score = pred_result["score"]
        
        # 2. 规划 (调用 策略工具)
        strategy = self.get_strategy(score)
        
        # 3. 决策 (模拟 LLM 整合)
        # 在真实场景中，这里构建 Prompt 发送给 DeepSeek
        # 这里我们用 Python 逻辑模拟 LLM 的结构化输出能力
        
        decision = Decision(
            risk_score=round(score, 4),
            customer_segment=strategy["segment"],
            decision=f"建议采取 {strategy['action_type']}",
            actions=[f"使用话术: {t}" for t in strategy["templates"]],
            rationale=f"模型预测概率为 {score:.1%}，属于{strategy['segment']}。该群体对{strategy['action_type']}转化率较高。"
        )
        
        return decision

if __name__ == "__main__":
    # 测试 Agent
    agent = MarketingAgent()
    
    # 构造一个测试用例
    test_customer = CustomerFeatures(
        age=35,
        job="management",
        marital="married",
        education="tertiary",
        default="no",
        balance=2000,
        housing="yes",
        loan="no",
        contact="cellular",
        day=15,
        month="may",
        campaign=1,
        pdays=-1,
        previous=0,
        poutcome="unknown"
    )
    
    result = agent.run(test_customer)
    print("\n=== Agent Decision ===")
    print(result.model_dump_json(indent=2))