#!/usr/bin/env python # -*- coding: UTF-8 -*- ''' @Project: NarratoAI @File : 短剧解说 @Author : 小林同学 @Date : 2025/5/9 上午12:36 ''' import os import json import requests from typing import Dict, Any, Optional from loguru import logger from app.config import config from app.utils.utils import get_uuid, storage_dir from app.services.SDE.prompt import subtitle_plot_analysis_v1, plot_writing class SubtitleAnalyzer: """字幕剧情分析器，负责分析字幕内容并提取关键剧情段落""" def __init__( self, api_key: Optional[str] = None, model: Optional[str] = None, base_url: Optional[str] = None, custom_prompt: Optional[str] = None, temperature: Optional[float] = 1.0, provider: Optional[str] = None, ): """ 初始化字幕分析器 Args: api_key: API密钥，如果不提供则从配置中读取 model: 模型名称，如果不提供则从配置中读取 base_url: API基础URL，如果不提供则从配置中读取或使用默认值 custom_prompt: 自定义提示词，如果不提供则使用默认值 temperature: 模型温度 provider: 提供商类型，用于确定API调用格式 """ # 使用传入的参数或从配置中获取 self.api_key = api_key self.model = model self.base_url = base_url self.temperature = temperature self.provider = provider or self._detect_provider() # 设置提示词模板 self.prompt_template = custom_prompt or subtitle_plot_analysis_v1 # 根据提供商类型确定是否为原生Gemini self.is_native_gemini = self.provider.lower() == 'gemini' # 初始化HTTP请求所需的头信息 self._init_headers() def _detect_provider(self): """根据配置自动检测提供商类型""" return config.app.get('text_llm_provider', 'gemini').lower() def _init_headers(self): """初始化HTTP请求头""" try: # 基础请求头，包含API密钥和内容类型 self.headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}" } # logger.debug(f"初始化成功 - API Key: {self.api_key[:8]}... - Base URL: {self.base_url}") except Exception as e: logger.error(f"初始化请求头失败: {str(e)}") raise def analyze_subtitle(self, subtitle_content: str) -> Dict[str, Any]: """ 分析字幕内容 Args: subtitle_content: 字幕内容文本 Returns: Dict[str, Any]: 包含分析结果的字典 """ try: # 构建完整提示词 prompt = f"{self.prompt_template}\n\n{subtitle_content}" if self.is_native_gemini: # 使用原生Gemini API格式 return self._call_native_gemini_api(prompt) else: # 使用OpenAI兼容格式 return self._call_openai_compatible_api(prompt) except Exception as e: logger.error(f"字幕分析过程中发生错误: {str(e)}") return { "status": "error", "message": str(e), "temperature": self.temperature } def _call_native_gemini_api(self, prompt: str) -> Dict[str, Any]: """调用原生Gemini API""" try: # 构建原生Gemini API请求数据 payload = { "systemInstruction": { "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。"}] }, "contents": [{ "parts": [{"text": prompt}] }], "generationConfig": { "temperature": self.temperature, "topK": 40, "topP": 0.95, "maxOutputTokens": 4000, "candidateCount": 1 }, "safetySettings": [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE" } ] } # 构建请求URL url = f"{self.base_url}/models/{self.model}:generateContent?key={self.api_key}" # 发送请求 response = requests.post( url, json=payload, headers={"Content-Type": "application/json", "User-Agent": "NarratoAI/1.0"}, timeout=120 ) if response.status_code == 200: response_data = response.json() # 检查响应格式 if "candidates" not in response_data or not response_data["candidates"]: return { "status": "error", "message": "原生Gemini API返回无效响应，可能触发了安全过滤", "temperature": self.temperature } candidate = response_data["candidates"][0] # 检查是否被安全过滤阻止 if "finishReason" in candidate and candidate["finishReason"] == "SAFETY": return { "status": "error", "message": "内容被Gemini安全过滤器阻止", "temperature": self.temperature } if "content" not in candidate or "parts" not in candidate["content"]: return { "status": "error", "message": "原生Gemini API返回内容格式错误", "temperature": self.temperature } # 提取文本内容 analysis_result = "" for part in candidate["content"]["parts"]: if "text" in part: analysis_result += part["text"] if not analysis_result.strip(): return { "status": "error", "message": "原生Gemini API返回空内容", "temperature": self.temperature } logger.debug(f"原生Gemini字幕分析完成") return { "status": "success", "analysis": analysis_result, "tokens_used": response_data.get("usage", {}).get("total_tokens", 0), "model": self.model, "temperature": self.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": self.temperature } except Exception as e: logger.error(f"原生Gemini API调用失败: {str(e)}") return { "status": "error", "message": f"原生Gemini API调用失败: {str(e)}", "temperature": self.temperature } def _call_openai_compatible_api(self, prompt: str) -> Dict[str, Any]: """调用OpenAI兼容的API""" try: # 构建OpenAI格式的请求数据 payload = { "model": self.model, "messages": [ {"role": "system", "content": "你是一位专业的剧本分析师和剧情概括助手。"}, {"role": "user", "content": prompt} ], "temperature": self.temperature } # 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: analysis_result = response_data["choices"][0]["message"]["content"] 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)}") # 返回结果 return { "status": "success", "analysis": analysis_result, "tokens_used": response_data.get("usage", {}).get("total_tokens", 0), "model": self.model, "temperature": self.temperature } else: logger.error("OpenAI兼容API字幕分析失败: 未获取到有效响应") return { "status": "error", "message": "未获取到有效响应", "temperature": self.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": self.temperature } except Exception as e: logger.error(f"OpenAI兼容API调用失败: {str(e)}") return { "status": "error", "message": f"OpenAI兼容API调用失败: {str(e)}", "temperature": self.temperature } def analyze_subtitle_from_file(self, subtitle_file_path: str) -> Dict[str, Any]: """ 从文件读取字幕并分析 Args: subtitle_file_path: 字幕文件的路径 Returns: Dict[str, Any]: 包含分析结果的字典 """ try: # 检查文件是否存在 if not os.path.exists(subtitle_file_path): return { "status": "error", "message": f"字幕文件不存在: {subtitle_file_path}", "temperature": self.temperature } # 读取文件内容 with open(subtitle_file_path, 'r', encoding='utf-8') as f: subtitle_content = f.read() # 分析字幕 return self.analyze_subtitle(subtitle_content) except Exception as e: logger.error(f"从文件读取字幕并分析过程中发生错误: {str(e)}") return { "status": "error", "message": str(e), "temperature": self.temperature } def save_analysis_result(self, analysis_result: Dict[str, Any], output_path: Optional[str] = None) -> str: """ 保存分析结果到文件 Args: analysis_result: 分析结果 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("drama_analysis", create=True) output_path = os.path.join(output_dir, f"analysis_{get_uuid(True)}.txt") # 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 analysis_result["status"] == "success": f.write(analysis_result["analysis"]) else: f.write(f"分析失败: {analysis_result['message']}") logger.info(f"分析结果已保存到: {output_path}") return output_path except Exception as e: logger.error(f"保存分析结果时发生错误: {str(e)}") return "" def generate_narration_script(self, short_name:str, plot_analysis: str, temperature: float = 0.7) -> Dict[str, Any]: """ 根据剧情分析生成解说文案 Args: short_name: 短剧名称 plot_analysis: 剧情分析内容 temperature: 生成温度，控制创造性，默认0.7 Returns: Dict[str, Any]: 包含生成结果的字典 """ try: # 构建完整提示词 prompt = plot_writing % (short_name, plot_analysis) if self.is_native_gemini: # 使用原生Gemini API格式 return self._generate_narration_with_native_gemini(prompt, temperature) else: # 使用OpenAI兼容格式 return self._generate_narration_with_openai_compatible(prompt, temperature) except Exception as e: logger.error(f"解说文案生成过程中发生错误: {str(e)}") return { "status": "error", "message": str(e), "temperature": self.temperature } def _generate_narration_with_native_gemini(self, prompt: str, temperature: float) -> Dict[str, Any]: """使用原生Gemini API生成解说文案""" try: # 构建原生Gemini API请求数据 # 为了确保JSON输出，在提示词中添加更强的约束 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。" payload = { "systemInstruction": { "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。"}] }, "contents": [{ "parts": [{"text": enhanced_prompt}] }], "generationConfig": { "temperature": temperature, "topK": 40, "topP": 0.95, "maxOutputTokens": 4000, "candidateCount": 1, "stopSequences": ["```", "注意", "说明"] }, "safetySettings": [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE" }, { "category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE" } ] } # 构建请求URL url = f"{self.base_url}/models/{self.model}:generateContent?key={self.api_key}" # 发送请求 response = requests.post( url, json=payload, headers={"Content-Type": "application/json", "User-Agent": "NarratoAI/1.0"}, timeout=120 ) if response.status_code == 200: response_data = response.json() # 检查响应格式 if "candidates" not in response_data or not response_data["candidates"]: return { "status": "error", "message": "原生Gemini API返回无效响应，可能触发了安全过滤", "temperature": temperature } candidate = response_data["candidates"][0] # 检查是否被安全过滤阻止 if "finishReason" in candidate and candidate["finishReason"] == "SAFETY": return { "status": "error", "message": "内容被Gemini安全过滤器阻止", "temperature": temperature } if "content" not in candidate or "parts" not in candidate["content"]: return { "status": "error", "message": "原生Gemini API返回内容格式错误", "temperature": temperature } # 提取文本内容 narration_script = "" for part in candidate["content"]["parts"]: if "text" in part: narration_script += part["text"] if not narration_script.strip(): return { "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']}")