import json
import time
from .mcp_client import MCP_Client

BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
MODEL = "qwen3-max"
TOKEN = "sk-ef26097310ec45c184e8d84b31ea9356"

# Alpha Vantage MCP配置
MCP_CONFIG = {
    "mcpServers": {
        "alpha-vantage": {
            "command": "npx",
            "args": ["alpha-ventage-mcp"],
            "env": {
                "ALPHA_VANTAGE_API_KEY": "97Q9TT7I6J9ZOLDS"
            }
        }
    }
}


def extract_stock_symbol(user_input: str) -> str:
    """
    从用户输入中提取股票代码
    支持多种格式:
    - "查询阿里巴巴股票" -> BABA
    - "查询AAPL股票" -> AAPL
    - "我想了解MSFT的股价" -> MSFT
    - "BABA股价如何" -> BABA
    """
    # 预定义一些常见股票代码的映射
    stock_mapping = {
        "阿里巴巴": "BABA",
        "阿里": "BABA",
        "腾讯": "TCEHY",
        "微软": "MSFT",
        "苹果": "AAPL",
        "谷歌": "GOOGL",
        "亚马逊": "AMZN",
        "特斯拉": "TSLA",
        "英伟达": "NVDA",
        "百度": "BIDU",
        "京东": "JD",
        "拼多多": "PDD"
    }

    # 检查预定义映射
    for chinese_name, symbol in stock_mapping.items():
        if chinese_name in user_input:
            return symbol

    # 尝试直接匹配常见的股票代码格式 (如 AAPL, MSFT 等)
    import re
    # 匹配常见的股票代码格式
    patterns = [
        r'\b([A-Z]{1,5})\b',  # 大写字母组成的股票代码
        r'股票代码[是为]?([A-Z]{1,5})',
        r'([A-Z]{1,5})股票'
    ]

    for pattern in patterns:
        match = re.search(pattern, user_input)
        if match:
            symbol = match.group(1)
            # 验证是否为有效的股票代码格式
            if 1 <= len(symbol) <= 5 and symbol.isalpha():
                return symbol

    # 默认返回阿里巴巴
    return "BABA"


def get_stock_price(symbol: str):
    """获取股票价格"""
    client = MCP_Client(MCP_CONFIG)
    if not client.initialize():
        raise Exception("MCP初始化失败")
    
    try:
        result = client.call_tool("get_stock_price", {"symbol": symbol})
        client.close()
        return result
    except Exception as e:
        client.close()
        raise e


def get_company_overview(symbol: str):
    """获取公司概况"""
    client = MCP_Client(MCP_CONFIG)
    if not client.initialize():
        raise Exception("MCP初始化失败")
    
    try:
        result = client.call_tool("get_company_overview", {"symbol": symbol})
        client.close()
        return result
    except Exception as e:
        client.close()
        raise e


def get_daily_time_series(symbol: str):
    """获取每日时间序列数据"""
    client = MCP_Client(MCP_CONFIG)
    if not client.initialize():
        raise Exception("MCP初始化失败")
    
    try:
        result = client.call_tool("get_daily_time_series", {"symbol": symbol})
        client.close()
        return result
    except Exception as e:
        client.close()
        raise e


def get_forex_rate(from_currency: str = "USD", to_currency: str = "CNY"):
    """获取外汇汇率"""
    client = MCP_Client(MCP_CONFIG)
    if not client.initialize():
        raise Exception("MCP初始化失败")
    
    try:
        result = client.call_tool("get_forex_rate", {
            "from_currency": from_currency, 
            "to_currency": to_currency
        })
        client.close()
        return result
    except Exception as e:
        client.close()
        raise e


def get_tools_for_stock(symbol: str):
    """根据股票代码生成工具列表"""
    return [
        {
            "name": "get_stock_price",
            "description": "获取股票价格",
            "params": {"symbol": symbol},
        },
        {
            "name": "get_company_overview",
            "description": "获取公司概况",
            "params": {"symbol": symbol},
        },
        {
            "name": "get_daily_time_series",
            "description": "获取每日时间序列数据",
            "params": {"symbol": symbol},
        },
        {
            "name": "get_forex_rate",
            "description": "获取外汇汇率",
            "params": {"from_currency": "USD", "to_currency": "CNY"},
        }
    ]


def call_llm(prompt: str):
    """模拟调用大模型的函数（你可以替换为真实请求）"""
    import requests

    headers = {
        "Authorization": f"Bearer {TOKEN}",
        "Content-Type": "application/json"
    }
    data = {
        "model": MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "stream": True  # 启用流式响应
    }
    response = requests.post(f"{BASE_URL}/chat/completions", headers=headers, json=data, stream=True)
    for line in response.iter_lines():
        if line:
            yield line.decode('utf-8')


def get_stock_data_example(symbol: str):
    """示例函数：获取股票数据的简化调用方式"""
    try:
        # 获取股票价格
        price_data = get_stock_price(symbol)
        print(f"股票价格数据: {price_data}")
        
        # 获取公司概况
        overview_data = get_company_overview(symbol)
        print(f"公司概况数据: {overview_data}")
        
        # 获取每日时间序列
        time_series_data = get_daily_time_series(symbol)
        print(f"时间序列数据: {time_series_data}")
        
        # 获取外汇汇率
        forex_data = get_forex_rate()
        print(f"外汇汇率数据: {forex_data}")
        
        return {
            "price": price_data,
            "overview": overview_data,
            "time_series": time_series_data,
            "forex": forex_data
        }
    except Exception as e:
        print(f"获取股票数据时出错: {str(e)}")
        raise e


def process_tool_analysis(user_input: str, history: list):
    """
    处理工具分析的主要逻辑
    - user_input: str
    - history: list of {"role": ..., "content": ...}
    Returns generator for streaming.
    """
    if not user_input.strip():
        yield "", history
        return

    # 添加用户消息到历史
    history.append({"role": "user", "content": user_input})

    try:
        # 从用户输入中提取股票代码
        stock_symbol = extract_stock_symbol(user_input)

        # 根据股票代码生成工具列表
        tools_to_test = get_tools_for_stock(stock_symbol)

        # 创建MCP客户端
        client = MCP_Client(MCP_CONFIG)

        # 初始化MCP会话
        if not client.initialize():
            error_msg = "❌ MCP初始化失败"
            history.append({"role": "assistant", "content": error_msg})
            yield "", history
            return

        # 收集所有工具的查询结果
        all_results = []

        # 依次调用每个工具
        for i, tool in enumerate(tools_to_test, 1):
            # 添加工具标题（使用HTML实现可折叠效果，默认折叠）
            tool_content = f'''<details>
<summary class="tool-header querying" style="background-color: #f0f8ff; border: 1px solid #d0e0f0; border-radius: 5px; padding: 10px; margin: 10px 0; color: #1e40af; font-weight: bold; cursor: pointer;">
[MCP] <span class="status-tag querying">查询中</span> 🔧 工具 {i}/{len(tools_to_test)}: {tool['name']} ({tool['description']})
</summary>
<div style="border: 1px solid #d0e0f0; border-top: none; border-radius: 0 0 5px 5px; padding: 15px; background-color: #f8fafc; margin-bottom: 10px;">'''

            history.append({"role": "assistant", "content": tool_content})
            yield "", history

            try:
                result = client.call_tool(tool["name"], tool["params"])

                # 解析结果
                if isinstance(result, dict) and "content" in result:
                    content = result["content"]
                    if isinstance(content, list) and len(content) > 0:
                        text_content = content[0].get("text", "") if isinstance(content[0], dict) else str(content[0])
                        # 立即调用模型总结这个信息内容
                        summary_prompt = f"给我总结这个信息内容：{text_content}"
                        # 更新状态为分析中
                        # 找到完整的summary标签并更新状态
                        start_idx = history[-1]["content"].find('<summary')
                        end_idx = history[-1]["content"].find('</summary>') + len('</summary>')
                        if start_idx != -1 and end_idx != -1:
                            summary_content = history[-1]["content"][start_idx:end_idx]
                            # 更新工具标题的类名和状态文本（添加过渡效果）
                            updated_summary = summary_content.replace('tool-header querying',
                                                                      'tool-header analyzing').replace(
                                'status-tag querying', 'status-tag analyzing').replace('查询中', '分析中')
                            history[-1]["content"] = history[-1]["content"].replace(summary_content, updated_summary)
                        yield "", history

                        # 流式显示模型处理结果（在可折叠面板内部）
                        model_summary = ""
                        # 移除所有冗余提示文本，直接显示分析结果区域
                        history[-1]["content"] += "<div id='analysis-result'>"
                        yield "", history

                        for chunk in call_llm(summary_prompt):
                            if not chunk or chunk == "[DONE]":
                                continue

                            if chunk.startswith("data: "):
                                chunk = chunk[6:]

                            try:
                                response_data = json.loads(chunk)
                                delta = response_data.get("choices", [{}])[0].get("delta", {})
                                content = delta.get("content", "")
                                if content:
                                    model_summary += content
                                    # 更新分析结果（在可折叠面板内部）
                                    history[-1]["content"] = history[-1]["content"][0:history[-1]["content"].find(
                                        "<div id='analysis-result'>") + 26] + model_summary
                                    yield "", history
                            except json.JSONDecodeError:
                                continue  # 忽略无效 JSON

                        history[-1]["content"] += "</div>"

                        result_msg = f"✅ {tool['name']}: {model_summary}"
                        all_results.append(f"{tool['description']}: {model_summary}")
                        # 添加最终结果到历史（在可折叠面板内部），移除所有冗余提示文本
                        history[-1]["content"] += f"<p><strong>分析结果:</strong></p><p>{result_msg}</p>"

                        # 更新状态为已完成（添加过渡效果）
                        # 找到完整的summary标签并更新状态
                        start_idx = history[-1]["content"].find('<summary')
                        end_idx = history[-1]["content"].find('</summary>') + len('</summary>')
                        if start_idx != -1 and end_idx != -1:
                            summary_start = history[-1]["content"][start_idx:end_idx]
                            updated_summary = summary_start.replace('tool-header analyzing',
                                                                    'tool-header completed').replace(
                                'status-tag analyzing', 'status-tag completed').replace('分析中', '已完成')
                            history[-1]["content"] = history[-1]["content"].replace(summary_start, updated_summary)

                        # 关闭可折叠面板
                        history[-1]["content"] += "</div>\n</details>"

            except Exception as e:
                error_msg = f"❌ {tool['name']} 查询失败: {str(e)}"
                history.append({"role": "assistant", "content": error_msg})
                yield "", history

                # 关闭可折叠面板（即使是错误情况）
                history[-1]["content"] += "</div>\n</details>"
                all_results.append(f"{tool['name']}: 查询失败 - {str(e)}")

            # 添加短暂延迟以改善用户体验
            time.sleep(0.5)

        # 关闭MCP连接
        client.close()

        # 将所有结果汇总后传递给AI模型进行总结
        final_summary_header = '''<div style="border: 2px solid #4CAF50; border-radius: 8px; padding: 20px; background-color: #f8fff8; margin: 15px 0;">
<h3 style="color: #2E7D32; text-align: center; margin-top: 0;">📈 最终总结</h3>'''
        history.append({"role": "assistant", "content": final_summary_header})
        yield "", history

        summary_msg = "📊 正在分析所有数据并生成总结..."
        history.append({"role": "assistant", "content": summary_msg})
        yield "", history

        # 构造给AI模型的提示
        all_results_text = "\n".join(all_results)
        summary_prompt = f"""用户问题: {user_input}

收集到的金融数据:
{all_results_text}

请根据以上数据回答用户问题，提供简洁明了的总结。"""

        # 调用AI模型进行总结
        bot_response = ""
        for chunk in call_llm(summary_prompt):
            if not chunk or chunk == "[DONE]":
                continue

            if chunk.startswith("data: "):
                chunk = chunk[6:]

            try:
                response_data = json.loads(chunk)
                delta = response_data.get("choices", [{}])[0].get("delta", {})
                content = delta.get("content", "")
                if content:
                    bot_response += content
                    # 更新累积历史记录中的最后一条消息，实现流式显示
                    history[-1]["content"] = "📊 正在分析所有数据并生成总结...\n" + bot_response
                    yield "", history
            except json.JSONDecodeError:
                continue  # 忽略无效 JSON

        # 最终完整历史
        final_footer = "</div>"
        history.append({"role": "assistant", "content": bot_response + final_footer})
        yield "", history

    except Exception as e:
        error_msg = f"❌ 错误: {str(e)}"
        history.append({"role": "assistant", "content": error_msg})
        yield "", history