tingwu_new

backend/ai_services/services.py

@@ -272,63 +272,8 @@ class AliyunTingwuService:
         
         # 提取文本 (MindMapSummary)
         # 结构: {"MindMapSummary": [{"Title": "...", "Topic": [...]}]}
-        summary_text = []
+        # 移除了原先的 summary_text 拼接逻辑
         
-        # 1. 优先提取段落标题和摘要
-        if 'ParagraphTitle' in summarization:
-             summary_text.append(f"### {summarization['ParagraphTitle']}")
-        if 'ParagraphSummary' in summarization:
-             summary_text.append(summarization['ParagraphSummary'])
-             summary_text.append("") # 空行分隔
-
-        # 2. 提取思维导图作为大纲
-        def parse_mindmap_topic(topic_list, level=0):
-            indent = "  " * level
-            for topic in topic_list:
-                title = topic.get('Title', '')
-                if title:
-                    summary_text.append(f"{indent}- {title}")
-                
-                sub_topics = topic.get('Topic', [])
-                if sub_topics:
-                    parse_mindmap_topic(sub_topics, level + 1)
-
-        if 'MindMapSummary' in summarization:
-            summary_text.append("### 内容大纲")
-            parse_mindmap_topic(summarization['MindMapSummary'])
-            summary_text.append("")
-
-        # 3. 提取对话总结 (ConversationalSummary)
-        if 'ConversationalSummary' in summarization and isinstance(summarization['ConversationalSummary'], list):
-            summary_text.append("### 对话总结")
-            for conv in summarization['ConversationalSummary']:
-                speaker = conv.get('SpeakerName', '发言人')
-                summary = conv.get('Summary', '')
-                if summary:
-                    summary_text.append(f"- **{speaker}**: {summary}")
-            summary_text.append("")
-
-        # 4. 提取问答总结 (QuestionsAnsweringSummary)
-        if 'QuestionsAnsweringSummary' in summarization and isinstance(summarization['QuestionsAnsweringSummary'], list):
-            summary_text.append("### 问答回顾")
-            for qa in summarization['QuestionsAnsweringSummary']:
-                question = qa.get('Question', '')
-                answer = qa.get('Answer', '')
-                if question and answer:
-                    summary_text.append(f"**Q: {question}**")
-                    summary_text.append(f"A: {answer}")
-                    summary_text.append("")
-
-        # 兼容旧逻辑：如果上述都为空，尝试 Text 或 Headline
-        if not summary_text:
-            if 'Text' in summarization:
-                summary_text.append(summarization['Text'])
-            elif 'Headline' in summarization:
-                summary_text.append(summarization['Headline'])
-             
-        if summary_text:
-            task.summary = "\n".join(summary_text)
-
         # --- C. 处理章节 (AutoChapters) ---
         auto_chapters = get_data_field(task_result, 'AutoChapters') or get_data_field(data_obj, 'AutoChapters') or []
         
@@ -346,34 +291,18 @@ class AliyunTingwuService:
         # 保存原始数据
         task.auto_chapters_data = auto_chapters
         
-        # 将章节信息追加到 summary
-        if auto_chapters and isinstance(auto_chapters, list):
-            if summary_text:
-                summary_text.append("\n\n### 章节速览")
-            else:
-                summary_text.append("### 章节速览")
-                
-            for chapter in auto_chapters:
-                headline = chapter.get('Headline', '')
-                summary = chapter.get('Summary', '')
-                start_time = chapter.get('Start', 0)
-                
-                # 格式化时间戳 (毫秒 -> HH:MM:SS)
-                seconds = int(start_time / 1000)
-                m, s = divmod(seconds, 60)
-                h, m = divmod(m, 60)
-                time_str = f"{h:02d}:{m:02d}:{s:02d}"
-                
-                chapter_text = f"- [{time_str}] {headline}"
-                if summary:
-                    chapter_text += f"\n  {summary}"
-                summary_text.append(chapter_text)
-
-        if summary_text:
-            task.summary = "\n".join(summary_text)
-        
+        # 保存任务，确保原始数据已写入数据库
         task.save()
 
+        # 调用大模型生成总结 (如果 summary_data 或 auto_chapters_data 存在)
+        if task.summary_data or task.auto_chapters_data:
+            try:
+                from .bailian_service import BailianService
+                bailian_service = BailianService()
+                bailian_service.summarize_task(task)
+            except Exception as e:
+                logger.error(f"Failed to trigger AI summarization: {e}")
+
         # 4. 自动触发 AI 评估 (如果任务首次成功且有启用的模板)
         if previous_status != 'SUCCEEDED' and task.status == 'SUCCEEDED' and task.transcription:
             self.trigger_ai_evaluations(task)