mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-12 03:02:48 +00:00
751d6fbb89
- 注释掉初始化日志输出，降低日志级别 - 更新 API base URL 为 NarratoAI 的生产环境地址 - 修改 API key 和模型名称为示例值 - 删除多余的测试代码
454 lines
16 KiB
Python
454 lines
16 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 : 短剧解说
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@Author : 小林同学
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@Date : 2025/5/9 上午12:36
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'''
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import os
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import json
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import requests
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from typing import Dict, Any, Optional
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from loguru import logger
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from app.config import config
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from app.utils.utils import get_uuid, storage_dir
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from app.services.SDE.prompt import subtitle_plot_analysis_v1, plot_writing
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class SubtitleAnalyzer:
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"""字幕剧情分析器，负责分析字幕内容并提取关键剧情段落"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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model: Optional[str] = None,
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base_url: Optional[str] = None,
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custom_prompt: Optional[str] = None,
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temperature: Optional[float] = 1.0,
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):
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"""
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初始化字幕分析器
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Args:
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api_key: API密钥，如果不提供则从配置中读取
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model: 模型名称，如果不提供则从配置中读取
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base_url: API基础URL，如果不提供则从配置中读取或使用默认值
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custom_prompt: 自定义提示词，如果不提供则使用默认值
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temperature: 模型温度
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"""
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# 使用传入的参数或从配置中获取
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self.api_key = api_key
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self.model = model
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self.base_url = base_url
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self.temperature = temperature
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# 设置提示词模板
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self.prompt_template = custom_prompt or subtitle_plot_analysis_v1
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# 初始化HTTP请求所需的头信息
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self._init_headers()
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def _init_headers(self):
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"""初始化HTTP请求头"""
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try:
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# 基础请求头，包含API密钥和内容类型
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self.headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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# logger.debug(f"初始化成功 - API Key: {self.api_key[:8]}... - Base URL: {self.base_url}")
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except Exception as e:
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logger.error(f"初始化请求头失败: {str(e)}")
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raise
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def analyze_subtitle(self, subtitle_content: str) -> Dict[str, Any]:
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"""
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分析字幕内容
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Args:
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subtitle_content: 字幕内容文本
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Returns:
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Dict[str, Any]: 包含分析结果的字典
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"""
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try:
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# 构建完整提示词
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prompt = f"{self.prompt_template}\n\n{subtitle_content}"
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# 构建请求体数据
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": "你是一位专业的剧本分析师和剧情概括助手。"},
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{"role": "user", "content": prompt}
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],
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"temperature": self.temperature
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}
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# 构建请求地址
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url = f"{self.base_url}/chat/completions"
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# 发送HTTP请求
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response = requests.post(url, headers=self.headers, json=payload)
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# 解析响应
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if response.status_code == 200:
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response_data = response.json()
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# 提取响应内容
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if "choices" in response_data and len(response_data["choices"]) > 0:
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analysis_result = response_data["choices"][0]["message"]["content"]
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logger.debug(f"字幕分析完成，消耗的tokens: {response_data.get('usage', {}).get('total_tokens', 0)}")
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# 返回结果
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return {
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"status": "success",
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"analysis": analysis_result,
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"tokens_used": response_data.get("usage", {}).get("total_tokens", 0),
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"model": self.model,
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"temperature": self.temperature
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}
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else:
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logger.error("字幕分析失败: 未获取到有效响应")
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return {
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"status": "error",
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"message": "未获取到有效响应",
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"temperature": self.temperature
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}
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else:
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error_msg = f"请求失败，状态码: {response.status_code}, 响应: {response.text}"
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logger.error(error_msg)
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return {
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"status": "error",
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"message": error_msg,
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"temperature": self.temperature
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}
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except Exception as e:
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logger.error(f"字幕分析过程中发生错误: {str(e)}")
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return {
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"status": "error",
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"message": str(e),
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"temperature": self.temperature
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}
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def analyze_subtitle_from_file(self, subtitle_file_path: str) -> Dict[str, Any]:
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"""
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从文件读取字幕并分析
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Args:
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subtitle_file_path: 字幕文件的路径
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Returns:
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Dict[str, Any]: 包含分析结果的字典
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(subtitle_file_path):
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return {
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"status": "error",
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"message": f"字幕文件不存在: {subtitle_file_path}",
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"temperature": self.temperature
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}
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# 读取文件内容
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with open(subtitle_file_path, 'r', encoding='utf-8') as f:
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subtitle_content = f.read()
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# 分析字幕
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return self.analyze_subtitle(subtitle_content)
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except Exception as e:
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logger.error(f"从文件读取字幕并分析过程中发生错误: {str(e)}")
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return {
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"status": "error",
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"message": str(e),
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"temperature": self.temperature
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}
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def save_analysis_result(self, analysis_result: Dict[str, Any], output_path: Optional[str] = None) -> str:
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"""
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保存分析结果到文件
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Args:
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analysis_result: 分析结果
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output_path: 输出文件路径，如果不提供则自动生成
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Returns:
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str: 输出文件的路径
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"""
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try:
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# 如果未提供输出路径，则自动生成
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if not output_path:
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output_dir = storage_dir("drama_analysis", create=True)
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output_path = os.path.join(output_dir, f"analysis_{get_uuid(True)}.txt")
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# 确保目录存在
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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# 保存结果
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with open(output_path, 'w', encoding='utf-8') as f:
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if analysis_result["status"] == "success":
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f.write(analysis_result["analysis"])
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else:
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f.write(f"分析失败: {analysis_result['message']}")
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logger.info(f"分析结果已保存到: {output_path}")
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return output_path
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except Exception as e:
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logger.error(f"保存分析结果时发生错误: {str(e)}")
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return ""
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def generate_narration_script(self, plot_analysis: str, temperature: float = 0.7) -> Dict[str, Any]:
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"""
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根据剧情分析生成解说文案
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Args:
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plot_analysis: 剧情分析内容
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temperature: 生成温度，控制创造性，默认0.7
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Returns:
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Dict[str, Any]: 包含生成结果的字典
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"""
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try:
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# 构建完整提示词
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prompt = plot_writing % plot_analysis
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# 构建请求体数据
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": "你是一位专业的短视频解说脚本撰写专家。"},
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{"role": "user", "content": prompt}
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],
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"temperature": temperature
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}
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# 对特定模型添加响应格式设置
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if self.model not in ["deepseek-reasoner"]:
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payload["response_format"] = {"type": "json_object"}
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# 构建请求地址
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url = f"{self.base_url}/chat/completions"
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# 发送HTTP请求
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response = requests.post(url, headers=self.headers, json=payload)
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# 解析响应
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if response.status_code == 200:
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response_data = response.json()
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# 提取响应内容
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if "choices" in response_data and len(response_data["choices"]) > 0:
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narration_script = response_data["choices"][0]["message"]["content"]
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logger.debug(f"解说文案生成完成，消耗的tokens: {response_data.get('usage', {}).get('total_tokens', 0)}")
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# 返回结果
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return {
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"status": "success",
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"narration_script": narration_script,
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"tokens_used": response_data.get("usage", {}).get("total_tokens", 0),
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"model": self.model,
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"temperature": self.temperature
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}
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else:
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logger.error("解说文案生成失败: 未获取到有效响应")
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return {
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"status": "error",
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"message": "未获取到有效响应",
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"temperature": self.temperature
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}
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else:
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error_msg = f"请求失败，状态码: {response.status_code}, 响应: {response.text}"
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logger.error(error_msg)
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return {
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"status": "error",
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"message": error_msg,
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"temperature": self.temperature
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}
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except Exception as e:
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logger.error(f"解说文案生成过程中发生错误: {str(e)}")
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return {
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"status": "error",
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"message": str(e),
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"temperature": self.temperature
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}
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def save_narration_script(self, narration_result: Dict[str, Any], output_path: Optional[str] = None) -> str:
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"""
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保存解说文案到文件
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Args:
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narration_result: 解说文案生成结果
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output_path: 输出文件路径，如果不提供则自动生成
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Returns:
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str: 输出文件的路径
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"""
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try:
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# 如果未提供输出路径，则自动生成
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if not output_path:
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output_dir = storage_dir("narration_scripts", create=True)
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output_path = os.path.join(output_dir, f"narration_{get_uuid(True)}.json")
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# 确保目录存在
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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# 保存结果
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with open(output_path, 'w', encoding='utf-8') as f:
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if narration_result["status"] == "success":
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f.write(narration_result["narration_script"])
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else:
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f.write(f"生成失败: {narration_result['message']}")
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logger.info(f"解说文案已保存到: {output_path}")
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return output_path
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except Exception as e:
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logger.error(f"保存解说文案时发生错误: {str(e)}")
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return ""
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def analyze_subtitle(
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subtitle_content: str = None,
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subtitle_file_path: str = None,
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api_key: Optional[str] = None,
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model: Optional[str] = None,
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base_url: Optional[str] = None,
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custom_prompt: Optional[str] = None,
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temperature: float = 1.0,
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save_result: bool = False,
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output_path: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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分析字幕内容的便捷函数
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Args:
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subtitle_content: 字幕内容文本
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subtitle_file_path: 字幕文件路径
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custom_prompt: 自定义提示词
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api_key: API密钥
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model: 模型名称
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base_url: API基础URL
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temperature: 模型温度
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save_result: 是否保存结果到文件
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output_path: 输出文件路径
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Returns:
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Dict[str, Any]: 包含分析结果的字典
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"""
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# 初始化分析器
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analyzer = SubtitleAnalyzer(
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temperature=temperature,
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api_key=api_key,
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model=model,
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base_url=base_url,
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custom_prompt=custom_prompt
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)
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# 分析字幕
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if subtitle_content:
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result = analyzer.analyze_subtitle(subtitle_content)
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elif subtitle_file_path:
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result = analyzer.analyze_subtitle_from_file(subtitle_file_path)
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else:
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return {
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"status": "error",
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"message": "必须提供字幕内容或字幕文件路径",
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"temperature": temperature
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}
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# 保存结果
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if save_result and result["status"] == "success":
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result["output_path"] = analyzer.save_analysis_result(result, output_path)
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return result
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def generate_narration_script(
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plot_analysis: str = None,
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api_key: Optional[str] = None,
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model: Optional[str] = None,
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base_url: Optional[str] = None,
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temperature: float = 1.0,
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save_result: bool = False,
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output_path: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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根据剧情分析生成解说文案的便捷函数
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Args:
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plot_analysis: 剧情分析内容，直接提供
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api_key: API密钥
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model: 模型名称
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base_url: API基础URL
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temperature: 生成温度，控制创造性
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save_result: 是否保存结果到文件
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output_path: 输出文件路径
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Returns:
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Dict[str, Any]: 包含生成结果的字典
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"""
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# 初始化分析器
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analyzer = SubtitleAnalyzer(
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temperature=temperature,
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api_key=api_key,
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model=model,
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base_url=base_url
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)
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# 生成解说文案
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result = analyzer.generate_narration_script(plot_analysis, temperature)
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# 保存结果
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if save_result and result["status"] == "success":
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result["output_path"] = analyzer.save_narration_script(result, output_path)
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return result
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if __name__ == '__main__':
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text_api_key = "skxxxx"
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text_model = "gemini-2.0-flash"
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text_base_url = "https://api.narratoai.cn/v1/chat/completions" # 确保URL不以斜杠结尾，便于后续拼接
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subtitle_path = "/Users/apple/Desktop/home/NarratoAI/resource/srt/家里家外1-5.srt"
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# 示例用法
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if subtitle_path:
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# 分析字幕总结剧情
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analysis_result = analyze_subtitle(
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subtitle_file_path=subtitle_path,
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api_key=text_api_key,
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model=text_model,
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base_url=text_base_url,
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save_result=True
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)
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if analysis_result["status"] == "success":
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print("字幕分析成功！")
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print("分析结果：")
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print(analysis_result["analysis"])
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# 根据剧情生成解说文案
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narration_result = generate_narration_script(
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plot_analysis=analysis_result["analysis"],
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api_key=text_api_key,
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model=text_model,
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base_url=text_base_url,
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save_result=True
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)
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if narration_result["status"] == "success":
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print("\n解说文案生成成功！")
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print("解说文案：")
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print(narration_result["narration_script"])
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else:
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print(f"\n解说文案生成失败: {narration_result['message']}")
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else:
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print(f"分析失败: {analysis_result['message']}")
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