# -*- coding: utf-8 -*-
"""
数学题目分析系统 v6.3（稳定流式处理版）
"""

import re
import json
import hashlib
import requests
from py2neo import Graph
from typing import Dict, List
from Config.Config import NEO4J_URI, NEO4J_AUTH, MODEL_API_URL, MODEL_API_KEY, MODEL_NAME


# ================== 配置类 ==================
class Config:
    NEO4J_URI = NEO4J_URI
    NEO4J_AUTH = NEO4J_AUTH
    MODEL_API_URL = MODEL_API_URL
    MODEL_API_KEY = MODEL_API_KEY
    MODEL_NAME = MODEL_NAME
    MAX_CONTENT_LENGTH = 500
    STREAM_TIMEOUT = 30


# ================== 知识图谱管理 ==================
class KnowledgeManager:
    def __init__(self):
        self.graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
        self._verify_connection()
        self.knowledge_map = self._load_knowledge()
        self.literacy_map = self._load_literacy()
        print("✅ 知识库加载完成")
        print(f"有效知识点({len(self.knowledge_map)}个): {list(self.knowledge_map.keys())[:3]}...")
        print(f"有效素养点({len(self.literacy_map)}个): {list(self.literacy_map.keys())[:3]}...")

    def _verify_connection(self):
        """安全连接验证"""
        try:
            self.graph.run("RETURN 1 AS test")
            print("✅ Neo4j连接验证成功")
        except Exception as e:
            print(f"❌ Neo4j连接失败: {str(e)}")
            raise

    def _load_knowledge(self) -> Dict[str, str]:
        """安全加载知识点"""
        records = self.graph.run("MATCH (n:KnowledgePoint) WHERE n.name IS NOT NULL RETURN n.id, n.name").data()
        return {rec['n.name'].strip(): rec['n.id'] for rec in records if rec['n.name'] and rec['n.name'].strip()}

    def _load_literacy(self) -> Dict[str, str]:
        """安全加载素养点"""
        records = self.graph.run("MATCH (n:LiteracyNode) WHERE n.title IS NOT NULL RETURN n.value, n.title").data()
        return {rec['n.title'].strip(): rec['n.value'] for rec in records if rec['n.title'] and rec['n.title'].strip()}

    def store_analysis(self, question_id: str, content: str,
                       knowledge: List[str], literacy: List[str]):
        """增强存储方法"""
        tx = self.graph.begin()
        try:
            # 创建/更新题目节点
            tx.run("""
                MERGE (q:Question {id: $id})
                SET q.content = $content, q.updateTime = timestamp()
                """, {"id": question_id, "content": content})

            # 关联知识点
            valid_kp = []
            for name in knowledge:
                clean_name = name.strip()
                if kp_id := self.knowledge_map.get(clean_name):
                    tx.run("""
                        MATCH (q:Question {id: $qid}), (kp:KnowledgePoint {id: $kpid})
                        MERGE (q)-[r:REQUIRES_KNOWLEDGE]->(kp)
                        SET r.lastUsed = timestamp()
                        """, {"qid": question_id, "kpid": kp_id})
                    valid_kp.append(clean_name)

            # 关联素养点
            valid_lp = []
            for title in literacy:
                clean_title = title.strip()
                if lit_id := self.literacy_map.get(clean_title):
                    tx.run("""
                        MATCH (q:Question {id: $qid}), (lp:LiteracyNode {value: $lpid})
                        MERGE (q)-[r:DEVELOPS_LITERACY]->(lp)
                        SET r.lastUsed = timestamp()
                        """, {"qid": question_id, "lpid": lit_id})
                    valid_lp.append(clean_title)

            self.graph.commit(tx)
            print(f"✅ 存储成功 - 知识点: {valid_kp}, 素养点: {valid_lp}")
        except Exception as e:
            self.graph.rollback(tx)
            print(f"❌ 存储失败: {str(e)}")


# ================== 大模型客户端 ==================
class StreamLLMClient:
    def __init__(self, kg: KnowledgeManager):
        self.kg = kg
        self.base_url = Config.MODEL_API_URL
        self.headers = {
            "Authorization": f"Bearer {Config.MODEL_API_KEY}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

    def analyze_problem(self, content: str) -> dict:
        """增强的流式分析"""
        try:
            prompt = self._build_prompt(content)
            response = self._send_request(prompt)
            return self._process_stream(response)
        except Exception as e:
            print(f"🔴 分析失败: {str(e)}")
            return {}

    def _build_prompt(self, content: str) -> str:
        """构建精准提示词"""
        return f"""请严格按以下要求分析数学题目：
1. 知识点必须完全匹配以下列表中的名称（不要新增或修改）：
{self.kg.knowledge_map.keys()}

2. 素养点必须完全匹配以下列表中的名称：
{self.kg.literacy_map.keys()}

3. 返回严格JSON格式：
{{
    "problem_types": ["题型"],
    "knowledge_points": ["匹配的知识点"],
    "literacy_points": ["匹配的素养点"]
}}

题目内容：{content}"""

    def _send_request(self, prompt: str):
        """发送API请求"""
        return requests.post(
            f"{self.base_url}/chat/completions",
            headers=self.headers,
            json={
                "model": Config.MODEL_NAME,
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.1,
                "stream": True
            },
            timeout=Config.STREAM_TIMEOUT,
            stream=True
        )

    def _process_stream(self, response) -> dict:
        """可靠的流式处理"""
        full_content = ""
        try:
            for chunk in response.iter_lines():
                if chunk:
                    decoded = chunk.decode('utf-8').strip()
                    if decoded.startswith('data:'):
                        json_str = decoded[5:].strip()
                        if json_str == "[DONE]":
                            break
                        try:
                            data = json.loads(json_str)
                            if content := data['choices'][0]['delta'].get('content'):
                                full_content += content
                        except:
                            continue

            # 调试日志
            print(f"原始响应内容：\n{full_content}")

            # 提取有效JSON
            json_str = re.search(r'\{[\s\S]*\}', full_content).group()
            return json.loads(json_str)
        except json.JSONDecodeError:
            print(f"⚠️ JSON解析失败，原始内容：{full_content}")
            return {}
        except Exception as e:
            print(f"流处理异常：{str(e)}")
            return {}


# ================== 核心引擎 ==================
class ProblemAnalyzer:
    def __init__(self, content: str):
        self.original = content
        self.content = self._preprocess(content)
        self.question_id = hashlib.sha256(content.encode()).hexdigest()[:12]
        self.kg = KnowledgeManager()
        self.llm = StreamLLMClient(self.kg)

    def _preprocess(self, text: str) -> str:
        """文本预处理"""
        return re.sub(r'[^\w\u4e00-\u9fa5]', '', text)[:Config.MAX_CONTENT_LENGTH]

    def execute(self):
        """执行分析流程"""
        print(f"\n🔍 分析题目: {self.original[:50]}...")

        analysis = self.llm.analyze_problem(self.original)
        if not analysis:
            print("⚠️ 大模型分析失败")
            return

        print("\n📊 分析结果:")
        print(f"  题型: {analysis.get('problem_types', [])}")
        print(f"  知识点: {analysis.get('knowledge_points', [])}")
        print(f"  素养点: {analysis.get('literacy_points', [])}")

        self.kg.store_analysis(
            question_id=self.question_id,
            content=self.content,
            knowledge=analysis.get('knowledge_points', []),
            literacy=analysis.get('literacy_points', [])
        )


# ================== 查询接口 ==================
def query_question(question_id: str):
    try:
        graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
        result = graph.run("""
            MATCH (q:Question {id: $id})
            OPTIONAL MATCH (q)-[:REQUIRES_KNOWLEDGE]->(kp)
            OPTIONAL MATCH (q)-[:DEVELOPS_LITERACY]->(lp)
            RETURN q.content AS content,
                   collect(kp.name) AS knowledge,
                   collect(lp.title) AS literacy
        """, id=question_id).data()

        if result:
            data = result[0]
            print(f"\n🔍 查询结果（ID: {question_id}）")
            print(f"内容: {data['content']}")
            print(f"知识点: {data['knowledge']}")
            print(f"素养点: {data['literacy']}")
        else:
            print("未找到相关题目")
    except Exception as e:
        print(f"查询错误: {str(e)}")


# ================== 测试执行 ==================
if __name__ == '__main__':
    test_cases = [
        # 基础运算类
        "小明用50元买了3本笔记本，每本8元，还剩多少钱？",  # 知识点：四则运算应用 素养点：数学运算能力

        # 几何问题
        "一个长方形花坛长5米，宽3米，四周铺1米宽的小路，求小路面积？",  # 知识点：面积计算 素养点：空间观念

        # 统计概率
        "某班30人，数学成绩90分以上占1/5，80-89分占1/3，求80分以下人数？",  # 知识点：分数运算 素养点：数据分析

        # 典型应用题
        "甲乙两车相距300公里，甲车速度60km/h，乙车40km/h，几小时后相遇？",  # 知识点：相遇问题 素养点：数学建模

        # 方程应用
        "鸡兔同笼，头共10个，脚共28只，问鸡兔各多少？",  # 知识点：二元一次方程 素养点：代数思维

        # 比例问题
        "配制农药药液，药粉和水的比例是3:100，现有水500kg，需要多少药粉？",  # 知识点：比例应用 素养点：量化分析

        # 函数图像
        "某物体做匀加速运动，初速度2m/s，加速度0.5m/s²，写出速度v与时间t的关系式",  # 知识点：一次函数 素养点：数形结合

        # 立体几何
        "圆柱形水桶底面半径0.4米，高1.2米，求它的容积（π取3.14）",  # 知识点：圆柱体积 素养点：空间想象

        # 概率统计
        "掷两个骰子，点数和为7的概率是多少？",  # 知识点：概率计算 素养点：数据预测

        # 工程问题
        "甲队单独完成工程需20天，乙队需30天，两队合作需要多少天？",  # 知识点：工程问题 素养点：模型构建

        # 最优化问题
        "用20米篱笆围矩形菜地，怎样围面积最大？最大面积是多少？",  # 知识点：二次函数 素养点：优化思想

        # 利率问题
        "本金10000元，年利率3.5%，存3年到期本息合计多少？",  # 知识点：单利计算 素养点：金融素养

        # 数列问题
        "等差数列首项5，公差3，求第10项的值",  # 知识点：等差数列 素养点：模式识别

        # 测量问题
        "在比例尺1:5000的地图上，2cm线段代表实际距离多少米？",  # 知识点：比例尺应用 素养点：单位换算

        # 逻辑推理
        "A说B在说谎，B说C在说谎，C说AB都在说谎，问谁在说真话？"  # 知识点：逻辑推理 素养点：批判性思维
    ]

    for q in test_cases:
        print("\n" + "=" * 60)
        analyzer = ProblemAnalyzer(q)
        analyzer.execute()