import logging
import json
import os
from django.conf import settings
from openai import OpenAI
from .models import AIEvaluation

logger = logging.getLogger(__name__)

class BailianService:
    def __init__(self):
        self.api_key = getattr(settings, 'DASHSCOPE_API_KEY', None)
        if not self.api_key:
             self.api_key = os.environ.get("DASHSCOPE_API_KEY")
        
        if self.api_key:
            self.client = OpenAI(
                api_key=self.api_key,
                base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
            )
        else:
            self.client = None
            logger.warning("DASHSCOPE_API_KEY not configured.")

    def evaluate_task(self, evaluation: AIEvaluation):
        """
        执行AI评估
        """
        if not self.client:
            evaluation.status = AIEvaluation.Status.FAILED
            evaluation.error_message = "服务未配置 (DASHSCOPE_API_KEY missing)"
            evaluation.save()
            return

        task = evaluation.task
        if not task.transcription:
             evaluation.status = AIEvaluation.Status.FAILED
             evaluation.error_message = "关联任务无逐字稿内容"
             evaluation.save()
             return

        evaluation.status = AIEvaluation.Status.PROCESSING
        evaluation.save()

        try:
            prompt = evaluation.prompt
            content = task.transcription
            
            # 截断过长的内容以防止超出Token限制 (简单处理，取前10000字)
            if len(content) > 10000:
                content = content[:10000] + "...(内容过长已截断)"

            # Construct messages
            messages = [
                {'role': 'system', 'content': 'You are a helpful assistant designed to output JSON.'},
                {'role': 'user', 'content': f"{prompt}\n\n以下是需要评估的内容：\n{content}"}
            ]

            completion = self.client.chat.completions.create(
                model=evaluation.model_selection,
                messages=messages,
                response_format={"type": "json_object"} 
            )
            
            response_content = completion.choices[0].message.content
            # Convert to dict for storage
            raw_response = completion.model_dump()
            
            evaluation.raw_response = raw_response
            
            # Parse JSON
            try:
                result = json.loads(response_content)
                evaluation.score = result.get('score')
                evaluation.evaluation = result.get('evaluation') or result.get('comment')
                
                # 尝试获取推理过程（如果模型返回了）
                evaluation.reasoning = result.get('reasoning') or result.get('analysis')
                
                if not evaluation.reasoning:
                     # 如果JSON里没有，把整个JSON作为推理参考
                     evaluation.reasoning = json.dumps(result, ensure_ascii=False, indent=2)

                evaluation.status = AIEvaluation.Status.COMPLETED
            except json.JSONDecodeError:
                evaluation.status = AIEvaluation.Status.FAILED
                evaluation.error_message = f"无法解析JSON响应: {response_content}"
                evaluation.reasoning = response_content 
            
            evaluation.save()
            return evaluation

        except Exception as e:
            logger.error(f"AI Evaluation failed: {e}")
            evaluation.status = AIEvaluation.Status.FAILED
                evaluation.error_message = str(e)
                evaluation.save()
                return evaluation

    def summarize_task(self, task):
        """
        使用 AI 模型总结转写和章节数据
        """
        if not self.client:
            logger.error("DashScope client not initialized")
            return

        try:
            summary_data = json.dumps(task.summary_data or {}, ensure_ascii=False)
            chapters_data = json.dumps(task.auto_chapters_data or {}, ensure_ascii=False)
            
            prompt = f"""
你是一个专业的会议摘要和内容分析助手。请根据以下提供的“总结原始数据”和“章节原始数据”，生成一个结构清晰、专业且易于阅读的 Markdown 格式总结。

要求：
1. 包含一个总体的“核心摘要”。
2. 包含一个详细的“内容大纲”。
3. 如果有问答或对话信息，请包含“关键问答”或“发言人观点”。
4. 包含一个带有时间戳的“章节回顾”，格式为 [HH:MM:SS] 标题。
5. 语言简练，重点突出。

总结原始数据：
{summary_data}

章节原始数据：
{chapters_data}
"""
            messages = [
                {'role': 'system', 'content': '你是一个专业的文档总结助手。请直接返回 Markdown 格式的内容，不要包含任何引导性文字。'},
                {'role': 'user', 'content': prompt}
            ]

            completion = self.client.chat.completions.create(
                model="qwen-turbo",
                messages=messages,
                temperature=0.7
            )
            
            ai_summary = completion.choices[0].message.content
            if ai_summary:
                task.summary = ai_summary
                task.save()
                logger.info(f"AI summary generated for task {task.id}")
            
        except Exception as e:
            logger.error(f"Failed to generate AI summary: {e}")