创赢未来评分系统 - 初始化提交（移除大文件）

backend/ai_services/bailian_service.py

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+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
+            
+            # 准备章节/时间戳数据以辅助分析发言节奏
+            chapter_context = ""
+            if task.auto_chapters_data:
+                try:
+                    chapters_str = ""
+                    # 处理特定的 AutoChapters 结构
+                    # 格式: {"AutoChapters": [{"Id": 1, "Start": 740, "End": 203436, "Headline": "...", "Summary": "..."}, ...]}
+                    if isinstance(task.auto_chapters_data, dict) and 'AutoChapters' in task.auto_chapters_data:
+                        chapters = task.auto_chapters_data['AutoChapters']
+                        if isinstance(chapters, list):
+                            chapter_lines = []
+                            for ch in chapters:
+                                # 毫秒转 MM:SS
+                                start_ms = ch.get('Start', 0)
+                                end_ms = ch.get('End', 0)
+                                start_str = f"{start_ms // 60000:02d}:{(start_ms // 1000) % 60:02d}"
+                                end_str = f"{end_ms // 60000:02d}:{(end_ms // 1000) % 60:02d}"
+                                
+                                headline = ch.get('Headline', '无标题')
+                                summary = ch.get('Summary', '')
+                                
+                                line = f"- [{start_str} - {end_str}] {headline}"
+                                if summary:
+                                    line += f"\n  摘要: {summary}"
+                                chapter_lines.append(line)
+                            
+                            chapters_str = "\n".join(chapter_lines)
+
+                    # 如果上面的解析为空（或者格式不匹配），回退到通用 JSON dump
+                    if not chapters_str:
+                        if isinstance(task.auto_chapters_data, (dict, list)):
+                            chapters_str = json.dumps(task.auto_chapters_data, ensure_ascii=False, indent=2)
+                        else:
+                            chapters_str = str(task.auto_chapters_data)
+                    
+                    chapter_context = f"\n\n【章节与时间戳信息】\n{chapters_str}\n\n(提示：请结合上述章节时间戳信息，分析发言者的语速、节奏变化及停顿情况。)"
+                except Exception as e:
+                    logger.warning(f"Failed to process auto_chapters_data: {e}")
+
+            # 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}{chapter_context}"}
+            ]
+
+            # 增加重试机制 (最多重试3次)
+            completion = None
+            last_error = None
+            import time
+            
+            for attempt in range(3):
+                try:
+                    completion = self.client.chat.completions.create(
+                        model=evaluation.model_selection,
+                        messages=messages,
+                        response_format={"type": "json_object"} 
+                    )
+                    break # 成功则跳出循环
+                except Exception as e:
+                    last_error = e
+                    logger.warning(f"AI Evaluation attempt {attempt+1}/3 failed for eval {evaluation.id}: {e}")
+                    if attempt < 2:
+                        time.sleep(2 * (attempt + 1)) # 简单的指数退避
+            
+            if not completion:
+                raise last_error or Exception("AI Service call failed after retries")
+
+            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()
+            
+            # 同步结果到参赛项目 (如果关联了)
+            self._sync_evaluation_to_project(evaluation)
+            
+            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 _sync_evaluation_to_project(self, evaluation: AIEvaluation):
+        """
+        将AI评估结果同步到关联的参赛项目（评分和评语）
+        """
+        try:
+            task = evaluation.task
+            if not task.project:
+                return
+
+            project = task.project
+            competition = project.competition
+            
+            # 1. 确定评委身份 (Based on Template)
+            # 用户要求：评委显示的是模板名称
+            template_name = evaluation.template.name if evaluation.template else "AI智能评委"
+            # 使用固定前缀 + template_id 确保唯一性，这样同一个模板在不同项目里是同一个评委
+            openid = f"ai_judge_{evaluation.template.id}" if evaluation.template else "ai_judge_default"
+            
+            # 延迟导入以避免循环依赖
+            from shop.models import WeChatUser
+            from competition.models import CompetitionEnrollment, Score, Comment, ScoreDimension
+
+            # 获取或创建虚拟评委用户
+            user, created = WeChatUser.objects.get_or_create(
+                openid=openid,
+                defaults={
+                    'nickname': template_name,
+                    'avatar_url': 'https://ui-avatars.com/api/?name=AI&background=random&color=fff'
+                }
+            )
+            
+            # 如果名字不匹配（比如模板改名了），更新它
+            if user.nickname != template_name:
+                user.nickname = template_name
+                user.save(update_fields=['nickname'])
+            
+            # 2. 确保评委已报名 (Enrollment)
+            enrollment, _ = CompetitionEnrollment.objects.get_or_create(
+                competition=competition,
+                user=user,
+                defaults={
+                    'role': 'judge',
+                    'status': 'approved'
+                }
+            )
+            
+            # 3. 同步评分 (Score)
+            if evaluation.score is not None:
+                # 尝试找到匹配的维度
+                dimensions = competition.score_dimensions.all()
+                target_dimension = None
+                
+                # 0. 优先使用模板配置的维度
+                if evaluation.template and evaluation.template.score_dimension:
+                    # 检查配置的维度是否属于当前比赛
+                    if evaluation.template.score_dimension.competition_id == competition.id:
+                        target_dimension = evaluation.template.score_dimension
+                    else:
+                        # 如果不属于当前比赛(跨比赛复用模板)，尝试查找同名维度
+                        target_dimension = dimensions.filter(name=evaluation.template.score_dimension.name).first()
+                
+                # 1. 如果未配置或未找到，尝试匹配 "AI Rating" (用户指定默认值)
+                if not target_dimension:
+                    target_dimension = dimensions.filter(name__iexact="AI Rating").first()
+                
+                # 2. 尝试匹配包含 "AI" 的维度
+                if not target_dimension:
+                    for dim in dimensions:
+                        if "AI" in dim.name.upper():
+                            target_dimension = dim
+                            break
+
+                # 3. 尝试匹配模板名称
+                if not target_dimension:
+                    target_dimension = dimensions.filter(name=template_name).first()
+                
+                # 4. 最后兜底：使用第一个维度
+                if not target_dimension and dimensions.exists():
+                    target_dimension = dimensions.first()
+                
+                if target_dimension:
+                    Score.objects.update_or_create(
+                        project=project,
+                        judge=enrollment,
+                        dimension=target_dimension,
+                        defaults={'score': evaluation.score}
+                    )
+                    logger.info(f"Synced AI score {evaluation.score} to project {project.id} dimension {target_dimension.name}")
+            
+            # 4. 同步评语 (Comment)
+            if evaluation.evaluation:
+                # 检查是否已存在该评委的评语，避免重复
+                comment = Comment.objects.filter(project=project, judge=enrollment).first()
+                if comment:
+                    comment.content = evaluation.evaluation
+                    comment.save()
+                else:
+                    Comment.objects.create(
+                        project=project,
+                        judge=enrollment,
+                        content=evaluation.evaluation
+                    )
+                logger.info(f"Synced AI comment to project {project.id}")
+                
+        except Exception as e:
+            logger.error(f"Failed to sync evaluation to project: {e}")
+
+    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}")