#!/usr/bin/env python # -*- coding: UTF-8 -*- ''' @Project: NarratoAI @File : 生成介绍文案 @Author : Viccy同学 @Date : 2025/5/8 上午11:33 ''' import json import os import traceback import asyncio from openai import OpenAI from loguru import logger # 导入新的LLM服务模块 - 确保提供商被注册 import app.services.llm # 这会触发提供商注册 from app.services.llm.migration_adapter import generate_narration as generate_narration_new # 导入新的提示词管理系统 from app.services.prompts import PromptManager def parse_frame_analysis_to_markdown(json_file_path): """ 解析视频帧分析JSON文件并转换为Markdown格式 :param json_file_path: JSON文件路径 :return: Markdown格式的字符串 """ # 检查文件是否存在 if not os.path.exists(json_file_path): return f"错误: 文件 {json_file_path} 不存在" try: # 读取JSON文件 with open(json_file_path, 'r', encoding='utf-8') as file: data = json.load(file) # 初始化Markdown字符串 markdown = "" # 获取总结和帧观察数据 summaries = data.get('overall_activity_summaries', []) frame_observations = data.get('frame_observations', []) # 按批次组织数据 batch_frames = {} for frame in frame_observations: batch_index = frame.get('batch_index') if batch_index not in batch_frames: batch_frames[batch_index] = [] batch_frames[batch_index].append(frame) # 生成Markdown内容 for i, summary in enumerate(summaries, 1): batch_index = summary.get('batch_index') time_range = summary.get('time_range', '') batch_summary = summary.get('summary', '') markdown += f"## 片段 {i}\n" markdown += f"- 时间范围：{time_range}\n" # 添加片段描述 markdown += f"- 片段描述：{batch_summary}\n" if batch_summary else f"- 片段描述：\n" markdown += "- 详细描述：\n" # 添加该批次的帧观察详情 frames = batch_frames.get(batch_index, []) for frame in frames: timestamp = frame.get('timestamp', '') observation = frame.get('observation', '') # 直接使用原始文本，不进行分割 markdown += f" - {timestamp}: {observation}\n" if observation else f" - {timestamp}: \n" markdown += "\n" return markdown except Exception as e: return f"处理JSON文件时出错: {traceback.format_exc()}" def generate_narration(markdown_content, api_key, base_url, model): """ 调用大模型API根据视频帧分析的Markdown内容生成解说文案 - 已重构为使用新的LLM服务架构 :param markdown_content: Markdown格式的视频帧分析内容 :param api_key: API密钥 :param base_url: API基础URL :param model: 使用的模型名称 :return: 生成的解说文案 """ try: # 优先使用新的LLM服务架构 logger.info("使用新的LLM服务架构生成解说文案") result = generate_narration_new(markdown_content, api_key, base_url, model) return result except Exception as e: logger.warning(f"使用新LLM服务失败，回退到旧实现: {str(e)}") # 回退到旧的实现以确保兼容性 return _generate_narration_legacy(markdown_content, api_key, base_url, model) def _generate_narration_legacy(markdown_content, api_key, base_url, model): """ 旧的解说文案生成实现 - 保留作为备用方案 :param markdown_content: Markdown格式的视频帧分析内容 :param api_key: API密钥 :param base_url: API基础URL :param model: 使用的模型名称 :return: 生成的解说文案 """ try: # 使用新的提示词管理系统构建提示词 prompt = PromptManager.get_prompt( category="documentary", name="narration_generation", parameters={ "video_frame_description": markdown_content } ) # 使用OpenAI SDK初始化客户端 client = OpenAI( api_key=api_key, base_url=base_url ) # 使用SDK发送请求 if model not in ["deepseek-reasoner"]: # deepseek-reasoner 不支持 json 输出 response = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": "你是一名专业的短视频解说文案撰写专家。"}, {"role": "user", "content": prompt} ], temperature=1.5, response_format={"type": "json_object"}, ) # 提取生成的文案 if response.choices and len(response.choices) > 0: narration_script = response.choices[0].message.content # 打印消耗的tokens logger.debug(f"消耗的tokens: {response.usage.total_tokens}") return narration_script else: return "生成解说文案失败: 未获取到有效响应" else: # 不支持 json 输出，需要多一步处理 ```json ``` 的步骤 response = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": "你是一名专业的短视频解说文案撰写专家。"}, {"role": "user", "content": prompt} ], temperature=1.5, ) # 提取生成的文案 if response.choices and len(response.choices) > 0: narration_script = response.choices[0].message.content # 打印消耗的tokens logger.debug(f"文案消耗的tokens: {response.usage.total_tokens}") # 清理 narration_script 字符串前后的 ```json ``` 字符串 narration_script = narration_script.replace("```json", "").replace("```", "") return narration_script else: return "生成解说文案失败: 未获取到有效响应" except Exception as e: return f"调用API生成解说文案时出错: {traceback.format_exc()}" if __name__ == '__main__': text_provider = 'openai' text_api_key = "sk-xxx" text_model = "deepseek-reasoner" text_base_url = "https://api.deepseek.com" video_frame_description_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_1139.json" # 测试新的JSON文件 test_file_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_2258.json" markdown_output = parse_frame_analysis_to_markdown(test_file_path) # print(markdown_output) # 输出到文件以便检查格式 output_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/家里家外1-5.md" with open(output_file, 'w', encoding='utf-8') as f: f.write(markdown_output) # print(f"\n已将Markdown输出保存到: {output_file}") # # 生成解说文案 # narration = generate_narration( # markdown_output, # text_api_key, # base_url=text_base_url, # model=text_model # ) # # # 保存解说文案 # print(narration) # print(type(narration)) # narration_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/final_narration_script.json" # with open(narration_file, 'w', encoding='utf-8') as f: # f.write(narration) # print(f"\n已将解说文案保存到: {narration_file}")