mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-11 10:32:49 +00:00
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。
761 lines
28 KiB
Python
761 lines
28 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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# 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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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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provider: Optional[str] = None,
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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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provider: 提供商类型，用于确定API调用格式
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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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self.provider = provider or self._detect_provider()
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# 设置提示词模板
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if custom_prompt:
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self.prompt_template = custom_prompt
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else:
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# 使用新的提示词管理系统
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self.prompt_template = PromptManager.get_prompt(
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category="short_drama_narration",
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name="plot_analysis",
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parameters={}
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)
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# 根据提供商类型确定是否为原生Gemini
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self.is_native_gemini = self.provider.lower() == 'gemini'
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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 _detect_provider(self):
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"""根据配置自动检测提供商类型"""
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return config.app.get('text_llm_provider', 'gemini').lower()
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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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if self.is_native_gemini:
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# 使用原生Gemini API格式
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return self._call_native_gemini_api(prompt)
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else:
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# 使用OpenAI兼容格式
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return self._call_openai_compatible_api(prompt)
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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 _call_native_gemini_api(self, prompt: str) -> Dict[str, Any]:
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"""调用原生Gemini API"""
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try:
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# 构建原生Gemini API请求数据
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payload = {
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"systemInstruction": {
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"parts": [{"text": "你是一位专业的剧本分析师和剧情概括助手。请严格按照要求的格式输出分析结果。"}]
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},
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"contents": [{
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"parts": [{"text": prompt}]
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}],
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"generationConfig": {
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"temperature": self.temperature,
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"topK": 40,
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"topP": 0.95,
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"maxOutputTokens": 4000,
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"candidateCount": 1
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},
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"safetySettings": [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_NONE"
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}
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]
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}
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# 构建请求URL
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url = f"{self.base_url}/models/{self.model}:generateContent?key={self.api_key}"
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# 发送请求
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response = requests.post(
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url,
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json=payload,
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headers={"Content-Type": "application/json", "User-Agent": "NarratoAI/1.0"},
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timeout=120
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)
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if response.status_code == 200:
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response_data = response.json()
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# 检查响应格式
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if "candidates" not in response_data or not response_data["candidates"]:
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return {
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"status": "error",
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"message": "原生Gemini API返回无效响应，可能触发了安全过滤",
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"temperature": self.temperature
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}
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candidate = response_data["candidates"][0]
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# 检查是否被安全过滤阻止
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if "finishReason" in candidate and candidate["finishReason"] == "SAFETY":
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return {
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"status": "error",
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"message": "内容被Gemini安全过滤器阻止",
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"temperature": self.temperature
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}
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if "content" not in candidate or "parts" not in candidate["content"]:
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return {
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"status": "error",
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"message": "原生Gemini API返回内容格式错误",
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"temperature": self.temperature
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}
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# 提取文本内容
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analysis_result = ""
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for part in candidate["content"]["parts"]:
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if "text" in part:
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analysis_result += part["text"]
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if not analysis_result.strip():
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return {
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"status": "error",
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"message": "原生Gemini API返回空内容",
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"temperature": self.temperature
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}
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logger.debug(f"原生Gemini字幕分析完成")
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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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error_msg = f"原生Gemini API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"原生Gemini API调用失败: {str(e)}")
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return {
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"status": "error",
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"message": f"原生Gemini API调用失败: {str(e)}",
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"temperature": self.temperature
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}
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def _call_openai_compatible_api(self, prompt: str) -> Dict[str, Any]:
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"""调用OpenAI兼容的API"""
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try:
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# 构建OpenAI格式的请求数据
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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, timeout=120)
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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"OpenAI兼容API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("OpenAI兼容API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"OpenAI兼容API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"OpenAI兼容API调用失败: {str(e)}")
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return {
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"status": "error",
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"message": f"OpenAI兼容API调用失败: {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, short_name:str, 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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short_name: 短剧名称
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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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# 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="short_drama_narration",
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name="script_generation",
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parameters={
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"drama_name": short_name,
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"plot_analysis": plot_analysis
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}
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)
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if self.is_native_gemini:
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# 使用原生Gemini API格式
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return self._generate_narration_with_native_gemini(prompt, temperature)
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else:
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# 使用OpenAI兼容格式
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return self._generate_narration_with_openai_compatible(prompt, temperature)
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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 _generate_narration_with_native_gemini(self, prompt: str, temperature: float) -> Dict[str, Any]:
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"""使用原生Gemini API生成解说文案"""
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try:
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# 构建原生Gemini API请求数据
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# 为了确保JSON输出，在提示词中添加更强的约束
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enhanced_prompt = f"{prompt}\n\n请确保输出严格的JSON格式，不要包含任何其他文字或标记。"
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payload = {
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"systemInstruction": {
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"parts": [{"text": "你是一位专业的短视频解说脚本撰写专家。你必须严格按照JSON格式输出，不能包含任何其他文字、说明或代码块标记。"}]
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},
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"contents": [{
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"parts": [{"text": enhanced_prompt}]
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}],
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"generationConfig": {
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"temperature": temperature,
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"topK": 40,
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"topP": 0.95,
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"maxOutputTokens": 4000,
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"candidateCount": 1,
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"stopSequences": ["```", "注意", "说明"]
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},
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"safetySettings": [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_NONE"
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}
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]
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}
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# 构建请求URL
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url = f"{self.base_url}/models/{self.model}:generateContent?key={self.api_key}"
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# 发送请求
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response = requests.post(
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url,
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json=payload,
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headers={"Content-Type": "application/json", "User-Agent": "NarratoAI/1.0"},
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timeout=120
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)
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if response.status_code == 200:
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response_data = response.json()
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# 检查响应格式
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if "candidates" not in response_data or not response_data["candidates"]:
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return {
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"status": "error",
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"message": "原生Gemini API返回无效响应，可能触发了安全过滤",
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"temperature": temperature
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}
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candidate = response_data["candidates"][0]
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# 检查是否被安全过滤阻止
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if "finishReason" in candidate and candidate["finishReason"] == "SAFETY":
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return {
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"status": "error",
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"message": "内容被Gemini安全过滤器阻止",
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"temperature": temperature
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}
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if "content" not in candidate or "parts" not in candidate["content"]:
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return {
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"status": "error",
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"message": "原生Gemini API返回内容格式错误",
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"temperature": temperature
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}
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# 提取文本内容
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narration_script = ""
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for part in candidate["content"]["parts"]:
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if "text" in part:
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narration_script += part["text"]
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if not narration_script.strip():
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return {
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"status": "error",
|
||
"message": "原生Gemini API返回空内容",
|
||
"temperature": temperature
|
||
}
|
||
|
||
logger.debug(f"原生Gemini解说文案生成完成")
|
||
|
||
return {
|
||
"status": "success",
|
||
"narration_script": narration_script,
|
||
"tokens_used": response_data.get("usage", {}).get("total_tokens", 0),
|
||
"model": self.model,
|
||
"temperature": temperature
|
||
}
|
||
else:
|
||
error_msg = f"原生Gemini API请求失败，状态码: {response.status_code}, 响应: {response.text}"
|
||
logger.error(error_msg)
|
||
return {
|
||
"status": "error",
|
||
"message": error_msg,
|
||
"temperature": temperature
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"原生Gemini API解说文案生成失败: {str(e)}")
|
||
return {
|
||
"status": "error",
|
||
"message": f"原生Gemini API解说文案生成失败: {str(e)}",
|
||
"temperature": temperature
|
||
}
|
||
|
||
def _generate_narration_with_openai_compatible(self, prompt: str, temperature: float) -> Dict[str, Any]:
|
||
"""使用OpenAI兼容API生成解说文案"""
|
||
try:
|
||
# 构建OpenAI格式的请求数据
|
||
payload = {
|
||
"model": self.model,
|
||
"messages": [
|
||
{"role": "system", "content": "你是一位专业的短视频解说脚本撰写专家。"},
|
||
{"role": "user", "content": prompt}
|
||
],
|
||
"temperature": temperature
|
||
}
|
||
|
||
# 对特定模型添加响应格式设置
|
||
if self.model not in ["deepseek-reasoner"]:
|
||
payload["response_format"] = {"type": "json_object"}
|
||
|
||
# 构建请求地址
|
||
url = f"{self.base_url}/chat/completions"
|
||
|
||
# 发送HTTP请求
|
||
response = requests.post(url, headers=self.headers, json=payload, timeout=120)
|
||
|
||
# 解析响应
|
||
if response.status_code == 200:
|
||
response_data = response.json()
|
||
|
||
# 提取响应内容
|
||
if "choices" in response_data and len(response_data["choices"]) > 0:
|
||
narration_script = response_data["choices"][0]["message"]["content"]
|
||
logger.debug(f"OpenAI兼容API解说文案生成完成，消耗的tokens: {response_data.get('usage', {}).get('total_tokens', 0)}")
|
||
|
||
# 返回结果
|
||
return {
|
||
"status": "success",
|
||
"narration_script": narration_script,
|
||
"tokens_used": response_data.get("usage", {}).get("total_tokens", 0),
|
||
"model": self.model,
|
||
"temperature": temperature
|
||
}
|
||
else:
|
||
logger.error("OpenAI兼容API解说文案生成失败: 未获取到有效响应")
|
||
return {
|
||
"status": "error",
|
||
"message": "未获取到有效响应",
|
||
"temperature": temperature
|
||
}
|
||
else:
|
||
error_msg = f"OpenAI兼容API请求失败，状态码: {response.status_code}, 响应: {response.text}"
|
||
logger.error(error_msg)
|
||
return {
|
||
"status": "error",
|
||
"message": error_msg,
|
||
"temperature": temperature
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"OpenAI兼容API解说文案生成失败: {str(e)}")
|
||
return {
|
||
"status": "error",
|
||
"message": f"OpenAI兼容API解说文案生成失败: {str(e)}",
|
||
"temperature": temperature
|
||
}
|
||
|
||
def save_narration_script(self, narration_result: Dict[str, Any], output_path: Optional[str] = None) -> str:
|
||
"""
|
||
保存解说文案到文件
|
||
|
||
Args:
|
||
narration_result: 解说文案生成结果
|
||
output_path: 输出文件路径，如果不提供则自动生成
|
||
|
||
Returns:
|
||
str: 输出文件的路径
|
||
"""
|
||
try:
|
||
# 如果未提供输出路径，则自动生成
|
||
if not output_path:
|
||
output_dir = storage_dir("narration_scripts", create=True)
|
||
output_path = os.path.join(output_dir, f"narration_{get_uuid(True)}.json")
|
||
|
||
# 确保目录存在
|
||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||
|
||
# 保存结果
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
if narration_result["status"] == "success":
|
||
f.write(narration_result["narration_script"])
|
||
else:
|
||
f.write(f"生成失败: {narration_result['message']}")
|
||
|
||
logger.info(f"解说文案已保存到: {output_path}")
|
||
return output_path
|
||
|
||
except Exception as e:
|
||
logger.error(f"保存解说文案时发生错误: {str(e)}")
|
||
return ""
|
||
|
||
|
||
def analyze_subtitle(
|
||
subtitle_content: str = None,
|
||
subtitle_file_path: str = None,
|
||
api_key: Optional[str] = None,
|
||
model: Optional[str] = None,
|
||
base_url: Optional[str] = None,
|
||
custom_prompt: Optional[str] = None,
|
||
temperature: float = 1.0,
|
||
save_result: bool = False,
|
||
output_path: Optional[str] = None,
|
||
provider: Optional[str] = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
分析字幕内容的便捷函数
|
||
|
||
Args:
|
||
subtitle_content: 字幕内容文本
|
||
subtitle_file_path: 字幕文件路径
|
||
custom_prompt: 自定义提示词
|
||
api_key: API密钥
|
||
model: 模型名称
|
||
base_url: API基础URL
|
||
temperature: 模型温度
|
||
save_result: 是否保存结果到文件
|
||
output_path: 输出文件路径
|
||
provider: 提供商类型
|
||
|
||
Returns:
|
||
Dict[str, Any]: 包含分析结果的字典
|
||
"""
|
||
# 初始化分析器
|
||
analyzer = SubtitleAnalyzer(
|
||
temperature=temperature,
|
||
api_key=api_key,
|
||
model=model,
|
||
base_url=base_url,
|
||
custom_prompt=custom_prompt,
|
||
provider=provider
|
||
)
|
||
logger.debug(f"使用模型: {analyzer.model} 开始分析, 温度: {analyzer.temperature}")
|
||
# 分析字幕
|
||
if subtitle_content:
|
||
result = analyzer.analyze_subtitle(subtitle_content)
|
||
elif subtitle_file_path:
|
||
result = analyzer.analyze_subtitle_from_file(subtitle_file_path)
|
||
else:
|
||
return {
|
||
"status": "error",
|
||
"message": "必须提供字幕内容或字幕文件路径",
|
||
"temperature": temperature
|
||
}
|
||
|
||
# 保存结果
|
||
if save_result and result["status"] == "success":
|
||
result["output_path"] = analyzer.save_analysis_result(result, output_path)
|
||
|
||
return result
|
||
|
||
|
||
def generate_narration_script(
|
||
short_name: str = None,
|
||
plot_analysis: str = None,
|
||
api_key: Optional[str] = None,
|
||
model: Optional[str] = None,
|
||
base_url: Optional[str] = None,
|
||
temperature: float = 1.0,
|
||
save_result: bool = False,
|
||
output_path: Optional[str] = None,
|
||
provider: Optional[str] = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
根据剧情分析生成解说文案的便捷函数
|
||
|
||
Args:
|
||
short_name: 短剧名称
|
||
plot_analysis: 剧情分析内容，直接提供
|
||
api_key: API密钥
|
||
model: 模型名称
|
||
base_url: API基础URL
|
||
temperature: 生成温度，控制创造性
|
||
save_result: 是否保存结果到文件
|
||
output_path: 输出文件路径
|
||
provider: 提供商类型
|
||
|
||
Returns:
|
||
Dict[str, Any]: 包含生成结果的字典
|
||
"""
|
||
# 初始化分析器
|
||
analyzer = SubtitleAnalyzer(
|
||
temperature=temperature,
|
||
api_key=api_key,
|
||
model=model,
|
||
base_url=base_url,
|
||
provider=provider
|
||
)
|
||
|
||
# 生成解说文案
|
||
result = analyzer.generate_narration_script(short_name, plot_analysis, temperature)
|
||
|
||
# 保存结果
|
||
if save_result and result["status"] == "success":
|
||
result["output_path"] = analyzer.save_narration_script(result, output_path)
|
||
|
||
return result
|
||
|
||
|
||
if __name__ == '__main__':
|
||
text_api_key = "skxxxx"
|
||
text_model = "gemini-2.0-flash"
|
||
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
|
||
subtitle_path = "/Users/apple/Desktop/home/NarratoAI/resource/srt/家里家外1-5.srt"
|
||
|
||
# 示例用法
|
||
if subtitle_path:
|
||
# 分析字幕总结剧情
|
||
analysis_result = analyze_subtitle(
|
||
subtitle_file_path=subtitle_path,
|
||
api_key=text_api_key,
|
||
model=text_model,
|
||
base_url=text_base_url,
|
||
save_result=True
|
||
)
|
||
|
||
if analysis_result["status"] == "success":
|
||
print("字幕分析成功！")
|
||
print("分析结果：")
|
||
print(analysis_result["analysis"])
|
||
|
||
# 根据剧情生成解说文案
|
||
narration_result = generate_narration_script(
|
||
plot_analysis=analysis_result["analysis"],
|
||
api_key=text_api_key,
|
||
model=text_model,
|
||
base_url=text_base_url,
|
||
save_result=True
|
||
)
|
||
|
||
if narration_result["status"] == "success":
|
||
print("\n解说文案生成成功！")
|
||
print("解说文案：")
|
||
print(narration_result["narration_script"])
|
||
else:
|
||
print(f"\n解说文案生成失败: {narration_result['message']}")
|
||
else:
|
||
print(f"分析失败: {analysis_result['message']}")
|