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):
        """
        对转写任务进行总结
        """
        if not self.client:
            logger.warning("BailianService not initialized, skipping summary.")
            return

        if not task.transcription:
            logger.warning(f"Task {task.id} has no transcription, skipping summary.")
            return

        try:
            content = task.transcription
            # 简单截断防止过长
            if len(content) > 15000:
                content = content[:15000] + "...(内容过长已截断)"
            
            # 准备上下文数据
            context_data = ""
            if task.summary_data:
                 context_data += f"\n\n【总结原始数据】\n{json.dumps(task.summary_data, ensure_ascii=False, indent=2)}"
            
            if task.auto_chapters_data:
                 context_data += f"\n\n【章节原始数据】\n{json.dumps(task.auto_chapters_data, ensure_ascii=False, indent=2)}"

            system_prompt = f"""你是一个专业的会议/内容总结助手。请根据提供的【转写文本】、【总结原始数据】和【章节原始数据】，生成一份结构清晰、内容详实的总结报告。

请按照以下结构输出（Markdown格式）：
1. **标题**：基于内容生成一个合适的标题。
2. **核心摘要**：简要概括主要内容。
3. **主要观点/话题**：结合思维导图数据，列出关键话题和层级。
4. **章节速览**：结合章节数据，列出时间点和主要内容。[HH:MM:SS]格式来把章节列出来
5. **问答精选**（如果有）：基于问答总结数据，列出重要问答。

请确保语言通顺，重点突出，能够还原内容的逻辑结构。"""

            user_content = f"以下是需要总结的内容：\n\n【转写文本】\n{content}{context_data}"

            messages = [
                {'role': 'system', 'content': system_prompt},
                {'role': 'user', 'content': user_content}
            ]

            # 使用 qwen-plus 作为默认模型
            completion = self.client.chat.completions.create(
                model="qwen-plus",
                messages=messages
            )
            
            summary_content = completion.choices[0].message.content
            task.summary = summary_content
            task.save(update_fields=['summary'])
            
            logger.info(f"Task {task.id} summary generated successfully.")

        except Exception as e:
            logger.error(f"Failed to generate summary for task {task.id}: {e}")