"""AI统计上报客户端"""
import os
from datetime import datetime
from decimal import Decimal
from typing import Optional, List
from dataclasses import dataclass, asdict

from .trace import get_trace_id, get_tenant_id, get_user_id


@dataclass
class AICallEvent:
    """AI调用事件"""
    tenant_id: int
    app_code: str
    module_code: str
    prompt_name: str
    model: str
    input_tokens: int = 0
    output_tokens: int = 0
    cost: Decimal = Decimal("0")
    latency_ms: int = 0
    status: str = "success"
    user_id: Optional[int] = None
    trace_id: Optional[str] = None
    event_time: datetime = None
    
    def __post_init__(self):
        if self.event_time is None:
            self.event_time = datetime.now()
        if self.trace_id is None:
            self.trace_id = get_trace_id()
        if self.user_id is None:
            self.user_id = get_user_id()


class StatsClient:
    """统计上报客户端
    
    使用示例:
        stats = StatsClient(tenant_id=1, app_code="011-ai-interview")
        
        # 上报AI调用
        stats.report_ai_call(
            module_code="interview",
            prompt_name="generate_question",
            model="gpt-4",
            input_tokens=100,
            output_tokens=200,
            latency_ms=1500
        )
    """
    
    def __init__(
        self,
        tenant_id: int,
        app_code: str,
        platform_url: Optional[str] = None,
        api_key: Optional[str] = None,
        local_only: bool = True
    ):
        self.tenant_id = tenant_id
        self.app_code = app_code
        self.platform_url = platform_url or os.getenv("PLATFORM_URL", "")
        self.api_key = api_key or os.getenv("PLATFORM_API_KEY", "")
        self.local_only = local_only or not self.platform_url
        
        # 批量上报缓冲区
        self._buffer: List[AICallEvent] = []
        self._buffer_size = 10  # 达到此数量时自动上报
    
    def report_ai_call(
        self,
        module_code: str,
        prompt_name: str,
        model: str,
        input_tokens: int = 0,
        output_tokens: int = 0,
        cost: Decimal = Decimal("0"),
        latency_ms: int = 0,
        status: str = "success",
        user_id: Optional[int] = None,
        flush: bool = False
    ) -> AICallEvent:
        """上报AI调用事件
        
        Args:
            module_code: 模块编码
            prompt_name: Prompt名称
            model: 模型名称
            input_tokens: 输入token数
            output_tokens: 输出token数
            cost: 成本
            latency_ms: 延迟毫秒
            status: 状态 (success/error)
            user_id: 用户ID（可选，默认从上下文获取）
            flush: 是否立即发送
        
        Returns:
            创建的事件对象
        """
        event = AICallEvent(
            tenant_id=self.tenant_id,
            app_code=self.app_code,
            module_code=module_code,
            prompt_name=prompt_name,
            model=model,
            input_tokens=input_tokens,
            output_tokens=output_tokens,
            cost=cost,
            latency_ms=latency_ms,
            status=status,
            user_id=user_id
        )
        
        self._buffer.append(event)
        
        if flush or len(self._buffer) >= self._buffer_size:
            self.flush()
        
        return event
    
    def flush(self):
        """发送缓冲区中的所有事件"""
        if not self._buffer:
            return
        
        events = self._buffer.copy()
        self._buffer.clear()
        
        if self.local_only:
            # 本地模式：仅打印
            for event in events:
                print(f"[STATS] {event.app_code}/{event.module_code}: "
                      f"{event.prompt_name} - {event.input_tokens}+{event.output_tokens} tokens")
        else:
            # 远程上报
            self._send_to_platform(events)
    
    def _send_to_platform(self, events: List[AICallEvent]):
        """发送事件到平台（异步，后续实现）"""
        # TODO: 使用httpx异步发送
        pass
    
    def __del__(self):
        """析构时发送剩余事件"""
        if self._buffer:
            self.flush()