mirror of
https://github.com/linyqh/NarratoAI.git
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dbbb06eda8
更新generate_narration_script.py、short_drama_explanation.py和step1_subtitle_analyzer_openai.py文件，集成新的提示词管理系统，提升解说文案和短剧分析的生成效率与准确性。通过使用PromptManager简化提示词构建过程，增强系统的灵活性和可维护性。
220 lines
7.8 KiB
Python
220 lines
7.8 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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'''
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@Project: NarratoAI
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@File : 生成介绍文案
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@Author : Viccy同学
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@Date : 2025/5/8 上午11:33
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'''
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import json
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import os
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import traceback
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import asyncio
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from openai import OpenAI
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from loguru import logger
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# 导入新的LLM服务模块 - 确保提供商被注册
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import app.services.llm # 这会触发提供商注册
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from app.services.llm.migration_adapter import generate_narration as generate_narration_new
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# 导入新的提示词管理系统
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from app.services.prompts import PromptManager
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def parse_frame_analysis_to_markdown(json_file_path):
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"""
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解析视频帧分析JSON文件并转换为Markdown格式
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:param json_file_path: JSON文件路径
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:return: Markdown格式的字符串
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"""
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# 检查文件是否存在
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if not os.path.exists(json_file_path):
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return f"错误: 文件 {json_file_path} 不存在"
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try:
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# 读取JSON文件
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with open(json_file_path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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# 初始化Markdown字符串
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markdown = ""
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# 获取总结和帧观察数据
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summaries = data.get('overall_activity_summaries', [])
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frame_observations = data.get('frame_observations', [])
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# 按批次组织数据
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batch_frames = {}
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for frame in frame_observations:
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batch_index = frame.get('batch_index')
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if batch_index not in batch_frames:
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batch_frames[batch_index] = []
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batch_frames[batch_index].append(frame)
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# 生成Markdown内容
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for i, summary in enumerate(summaries, 1):
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batch_index = summary.get('batch_index')
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time_range = summary.get('time_range', '')
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batch_summary = summary.get('summary', '')
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markdown += f"## 片段 {i}\n"
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markdown += f"- 时间范围：{time_range}\n"
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# 添加片段描述
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markdown += f"- 片段描述：{batch_summary}\n" if batch_summary else f"- 片段描述：\n"
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markdown += "- 详细描述：\n"
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# 添加该批次的帧观察详情
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frames = batch_frames.get(batch_index, [])
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for frame in frames:
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timestamp = frame.get('timestamp', '')
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observation = frame.get('observation', '')
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# 直接使用原始文本，不进行分割
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markdown += f" - {timestamp}: {observation}\n" if observation else f" - {timestamp}: \n"
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markdown += "\n"
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return markdown
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except Exception as e:
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return f"处理JSON文件时出错: {traceback.format_exc()}"
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def generate_narration(markdown_content, api_key, base_url, model):
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"""
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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
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:param markdown_content: Markdown格式的视频帧分析内容
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:param api_key: API密钥
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:param base_url: API基础URL
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:param model: 使用的模型名称
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:return: 生成的解说文案
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"""
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try:
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# 优先使用新的LLM服务架构
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logger.info("使用新的LLM服务架构生成解说文案")
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result = generate_narration_new(markdown_content, api_key, base_url, model)
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return result
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except Exception as e:
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logger.warning(f"使用新LLM服务失败，回退到旧实现: {str(e)}")
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# 回退到旧的实现以确保兼容性
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return _generate_narration_legacy(markdown_content, api_key, base_url, model)
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def _generate_narration_legacy(markdown_content, api_key, base_url, model):
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"""
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旧的解说文案生成实现 - 保留作为备用方案
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:param markdown_content: Markdown格式的视频帧分析内容
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:param api_key: API密钥
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:param base_url: API基础URL
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:param model: 使用的模型名称
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:return: 生成的解说文案
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"""
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try:
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# 使用新的提示词管理系统构建提示词
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prompt = PromptManager.get_prompt(
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category="documentary",
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name="narration_generation",
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parameters={
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"video_frame_description": markdown_content
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}
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)
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# 使用OpenAI SDK初始化客户端
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client = OpenAI(
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api_key=api_key,
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base_url=base_url
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)
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# 使用SDK发送请求
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if model not in ["deepseek-reasoner"]:
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# deepseek-reasoner 不支持 json 输出
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response = client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": "你是一名专业的短视频解说文案撰写专家。"},
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{"role": "user", "content": prompt}
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],
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temperature=1.5,
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response_format={"type": "json_object"},
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)
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# 提取生成的文案
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if response.choices and len(response.choices) > 0:
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narration_script = response.choices[0].message.content
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# 打印消耗的tokens
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logger.debug(f"消耗的tokens: {response.usage.total_tokens}")
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return narration_script
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else:
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return "生成解说文案失败: 未获取到有效响应"
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else:
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# 不支持 json 输出，需要多一步处理 ```json ``` 的步骤
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response = client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": "你是一名专业的短视频解说文案撰写专家。"},
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{"role": "user", "content": prompt}
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],
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temperature=1.5,
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)
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# 提取生成的文案
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if response.choices and len(response.choices) > 0:
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narration_script = response.choices[0].message.content
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# 打印消耗的tokens
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logger.debug(f"文案消耗的tokens: {response.usage.total_tokens}")
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# 清理 narration_script 字符串前后的 ```json ``` 字符串
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narration_script = narration_script.replace("```json", "").replace("```", "")
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return narration_script
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else:
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return "生成解说文案失败: 未获取到有效响应"
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except Exception as e:
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return f"调用API生成解说文案时出错: {traceback.format_exc()}"
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if __name__ == '__main__':
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text_provider = 'openai'
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text_api_key = "sk-xxx"
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text_model = "deepseek-reasoner"
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text_base_url = "https://api.deepseek.com"
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video_frame_description_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_1139.json"
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# 测试新的JSON文件
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test_file_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_2258.json"
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markdown_output = parse_frame_analysis_to_markdown(test_file_path)
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# print(markdown_output)
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# 输出到文件以便检查格式
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output_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/家里家外1-5.md"
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(markdown_output)
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# print(f"\n已将Markdown输出保存到: {output_file}")
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# # 生成解说文案
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# narration = generate_narration(
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# markdown_output,
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# text_api_key,
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# base_url=text_base_url,
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# model=text_model
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# )
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#
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# # 保存解说文案
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# print(narration)
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# print(type(narration))
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# narration_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/final_narration_script.json"
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# with open(narration_file, 'w', encoding='utf-8') as f:
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# f.write(narration)
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# print(f"\n已将解说文案保存到: {narration_file}")
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