import json
import requests
from config.logger import setup_logging
from core.providers.llm.base import LLMProviderBase

TAG = __name__
logger = setup_logging()


class LLMProvider(LLMProviderBase):
    """
    蚂蚁阿福 LLM Provider
    通过 Frida HTTP Bridge (port 18900) 对接蚂蚁阿福 App 的文字对话 API。
    Bridge 运行在手机上，通过 adb forward 或网络暴露 SSE 流式接口。
    """

    def __init__(self, config):
        self.bridge_url = config.get("bridge_url", "http://127.0.0.1:18900")
        self.timeout = config.get("timeout", 60)
        self.should_idle = False  # signal to send system idle after TTS
        logger.bind(tag=TAG).info(
            f"AntafLLM 初始化: bridge={self.bridge_url}, timeout={self.timeout}s"
        )

    @staticmethod
    def _is_thinking(text):
        """检测蚂蚁阿福的内心思考/推理过程，这些不应该发给用户"""
        thinking_patterns = [
            "用户问", "用户说", "用户的", "用户可能", "用户真正",
            "我得", "我会", "我在想", "我决定", "我要",
            "语气比较", "感觉他", "让他知道", "让他觉得",
            "先安抚", "得先", "不想表现",
            "整体语气", "这样能", "这样他",
            "所以我", "还带了个",
        ]
        for p in thinking_patterns:
            if p in text:
                return True
        return False

    @staticmethod
    def _clean_text(text):
        """清理阿福返回文本中的脏数据、链接、markdown、表格"""
        import re
        # 去掉阿福内部状态文本
        junk = [
            "完成资料引用", "内容生成", "正在思考", "正在搜索",
            "开始获取资料", "找到资料",
        ]
        for j in junk:
            text = text.replace(j, "")
        # Markdown链接 [文字](url) → 只保留文字
        text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
        # 裸URL
        text = re.sub(r'https?://\S+', '', text)
        # Markdown加粗 **文字** → 文字（包括空的 ****）
        text = re.sub(r'\*{2,}([^*]*)\*{2,}', r'\1', text)
        # Markdown斜体 *文字* → 文字
        text = re.sub(r'\*([^*]+)\*', r'\1', text)
        # 残留星号
        text = text.replace('*', '')
        # Markdown表格行 | xxx | xxx |
        text = re.sub(r'\|[^|]*\|[^|]*\|[^|\n]*\|?', '', text)
        # 表格对齐行 | :--- | :--- |
        text = re.sub(r'\|\s*:?-+:?\s*(\|\s*:?-+:?\s*)+\|?', '', text)
        # Markdown标题 ### 文字
        text = re.sub(r'#{1,6}\s*', '', text)
        # 多余空格和空行
        text = re.sub(r'  +', ' ', text)
        text = re.sub(r'\n{2,}', '\n', text)
        return text.strip()

    @staticmethod
    def _is_system_injected(content):
        """检测是否为系统注入的消息（非用户真实输入）"""
        if not content:
            return True
        markers = [
            "[系统提示]", "tool_call", "<tool_call>", "TOOL USE",
            "系统提示", "工具调用", "function_call",
            "handle_exit_intent", "你有以下工具", "You have access",
        ]
        for m in markers:
            if m in content:
                return True
        # 超过200字的 user 消息大概率是系统注入的
        if len(content) > 200:
            return True
        return False

    def response(self, session_id, dialogue, **kwargs):
        # 从 dialogue 中提取真正的用户消息（跳过系统注入的 user 消息）
        query = ""
        for msg in reversed(dialogue):
            if msg.get("role") == "user":
                content = msg.get("content", "")
                if not self._is_system_injected(content):
                    # ASR 结果可能是 JSON: {"content":"...", "language":"zh", "emotion":"..."}
                    try:
                        parsed = json.loads(content)
                        if isinstance(parsed, dict) and "content" in parsed:
                            query = parsed["content"]
                        else:
                            query = content
                    except (json.JSONDecodeError, TypeError):
                        query = content
                    break

        if not query:
            logger.bind(tag=TAG).warning("对话中没有用户消息")
            yield "抱歉，我没有收到您的问题。"
            return

        self.should_idle = False
        logger.bind(tag=TAG).info(f"AntafLLM 请求: {query[:50]}...")

        try:
            url = f"{self.bridge_url}/chat"
            resp = requests.get(
                url,
                params={"q": query},
                stream=True,
                timeout=self.timeout,
            )
            resp.encoding = "utf-8"

            seen_texts = set()
            for line in resp.iter_lines(decode_unicode=True):
                if not line:
                    continue
                if line.startswith("data: "):
                    data = line[6:]
                    if data == "[DONE]":
                        break
                    if not data or len(data.strip()) == 0:
                        continue
                    # 去重：跳过完全相同的文本块
                    if data in seen_texts:
                        continue
                    seen_texts.add(data)
                    # 过滤思考过程
                    if self._is_thinking(data):
                        logger.bind(tag=TAG).debug(f"过滤思考内容: {data[:50]}...")
                        continue
                    # 清理脏数据
                    data = self._clean_text(data)
                    if not data:
                        continue
                    yield data

        except requests.exceptions.ConnectionError:
            logger.bind(tag=TAG).error("无法连接蚂蚁阿福 Bridge，请检查手机和 Frida 状态")
            self.should_idle = True
            yield "抱歉，蚂蚁阿福服务暂时不可用。"
        except requests.exceptions.Timeout:
            logger.bind(tag=TAG).error(f"蚂蚁阿福 Bridge 超时 ({self.timeout}s)")
            self.should_idle = True
            yield "抱歉，回答超时了。"
        except Exception as e:
            logger.bind(tag=TAG).error(f"AntafLLM 异常: {e}")
            self.should_idle = True
            yield "抱歉，发生了错误。"