mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-11 10:32:49 +00:00
642 lines
25 KiB
Python
642 lines
25 KiB
Python
import os
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import json
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import traceback
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from loguru import logger
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# import tiktoken
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from typing import List, Dict
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from datetime import datetime
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from openai import OpenAI
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import requests
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import time
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class BaseGenerator:
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def __init__(self, model_name: str, api_key: str, prompt: str):
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self.model_name = model_name
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self.api_key = api_key
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self.base_prompt = prompt
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self.conversation_history = []
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self.chunk_overlap = 50
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self.last_chunk_ending = ""
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self.default_params = {
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"temperature": 0.7,
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"max_tokens": 500,
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"top_p": 0.9,
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"frequency_penalty": 0.3,
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"presence_penalty": 0.5
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}
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def _try_generate(self, messages: list, params: dict = None) -> str:
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max_attempts = 3
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tolerance = 5
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for attempt in range(max_attempts):
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try:
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response = self._generate(messages, params or self.default_params)
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return self._process_response(response)
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except Exception as e:
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if attempt == max_attempts - 1:
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raise
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logger.warning(f"Generation attempt {attempt + 1} failed: {str(e)}")
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continue
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return ""
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def _generate(self, messages: list, params: dict) -> any:
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raise NotImplementedError
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def _process_response(self, response: any) -> str:
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return response
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def generate_script(self, scene_description: str, word_count: int) -> str:
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"""生成脚本的通用方法"""
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prompt = f"""{self.base_prompt}
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上一段文案的结尾：{self.last_chunk_ending if self.last_chunk_ending else "这是第一段，无需考虑上文"}
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当前画面描述：{scene_description}
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请确保新生成的文案与上文自然衔接，保持叙事的连贯性和趣味性。
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不要出现除了文案以外的其他任何内容；
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严格字数要求：{word_count}字，允许误差±5字。"""
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messages = [
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{"role": "system", "content": self.base_prompt},
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{"role": "user", "content": prompt}
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]
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try:
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generated_script = self._try_generate(messages, self.default_params)
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# 更新上下文
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if generated_script:
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self.last_chunk_ending = generated_script[-self.chunk_overlap:] if len(
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generated_script) > self.chunk_overlap else generated_script
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return generated_script
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except Exception as e:
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logger.error(f"Script generation failed: {str(e)}")
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raise
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class OpenAIGenerator(BaseGenerator):
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"""OpenAI API 生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str):
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super().__init__(model_name, api_key, prompt)
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base_url = base_url or f"https://api.openai.com/v1"
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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self.max_tokens = 5000
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# OpenAI特定参数
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self.default_params = {
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**self.default_params,
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"stream": False,
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"user": "script_generator"
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}
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# # 初始化token计数器
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# try:
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# self.encoding = tiktoken.encoding_for_model(self.model_name)
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# except KeyError:
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# logger.warning(f"未找到模型 {self.model_name} 的专用编码器，使用默认编码器")
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# self.encoding = tiktoken.get_encoding("cl100k_base")
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def _generate(self, messages: list, params: dict) -> any:
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"""实现OpenAI特定的生成逻辑"""
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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**params
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)
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return response
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except Exception as e:
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logger.error(f"OpenAI generation error: {str(e)}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理OpenAI的响应"""
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if not response or not response.choices:
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raise ValueError("Invalid response from OpenAI API")
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return response.choices[0].message.content.strip()
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def _count_tokens(self, messages: list) -> int:
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"""计算token数量"""
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num_tokens = 0
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for message in messages:
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num_tokens += 3
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for key, value in message.items():
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num_tokens += len(self.encoding.encode(str(value)))
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if key == "role":
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num_tokens += 1
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num_tokens += 3
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return num_tokens
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class GeminiGenerator(BaseGenerator):
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"""原生Gemini API 生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str = None):
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super().__init__(model_name, api_key, prompt)
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self.base_url = base_url or "https://generativelanguage.googleapis.com/v1beta"
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self.client = None
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# 原生Gemini API参数
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self.default_params = {
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"temperature": self.default_params["temperature"],
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"topP": self.default_params["top_p"],
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"topK": 40,
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"maxOutputTokens": 4000,
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"candidateCount": 1,
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"stopSequences": []
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}
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class GeminiOpenAIGenerator(BaseGenerator):
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"""OpenAI兼容的Gemini代理生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str = None):
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super().__init__(model_name, api_key, prompt)
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if not base_url:
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raise ValueError("OpenAI兼容的Gemini代理必须提供base_url")
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self.base_url = base_url.rstrip('/')
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# 使用OpenAI兼容接口
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from openai import OpenAI
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self.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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# OpenAI兼容接口参数
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self.default_params = {
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"temperature": self.default_params["temperature"],
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"max_tokens": 4000,
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"stream": False
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}
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def _generate(self, messages: list, params: dict) -> any:
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"""实现OpenAI兼容Gemini代理的生成逻辑"""
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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**params
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)
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return response
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except Exception as e:
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logger.error(f"OpenAI兼容Gemini代理生成错误: {str(e)}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理OpenAI兼容接口的响应"""
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if not response or not response.choices:
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raise ValueError("OpenAI兼容Gemini代理返回无效响应")
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return response.choices[0].message.content.strip()
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def _generate(self, messages: list, params: dict) -> any:
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"""实现原生Gemini API的生成逻辑"""
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max_retries = 3
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for attempt in range(max_retries):
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try:
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# 转换消息格式为Gemini格式
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prompt = "\n".join([m["content"] for m in messages])
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# 构建请求数据
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request_data = {
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"contents": [{
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"parts": [{"text": prompt}]
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}],
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"generationConfig": params,
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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_name}: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=request_data,
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headers={
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"Content-Type": "application/json",
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"User-Agent": "NarratoAI/1.0"
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},
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timeout=120
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)
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if response.status_code == 429:
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# 处理限流
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wait_time = 65 if attempt == 0 else 30
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logger.warning(f"原生Gemini API 触发限流，等待{wait_time}秒后重试...")
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time.sleep(wait_time)
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continue
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if response.status_code == 400:
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raise Exception(f"请求参数错误: {response.text}")
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elif response.status_code == 403:
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raise Exception(f"API密钥无效或权限不足: {response.text}")
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elif response.status_code != 200:
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raise Exception(f"原生Gemini API请求失败: {response.status_code} - {response.text}")
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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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if attempt < max_retries - 1:
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logger.warning("原生Gemini API 返回无效响应，等待30秒后重试...")
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time.sleep(30)
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continue
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else:
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raise Exception("原生Gemini API返回无效响应，可能触发了安全过滤")
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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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raise Exception("内容被Gemini安全过滤器阻止")
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# 创建兼容的响应对象
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class CompatibleResponse:
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def __init__(self, data):
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self.data = data
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candidate = data["candidates"][0]
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if "content" in candidate and "parts" in candidate["content"]:
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self.text = ""
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for part in candidate["content"]["parts"]:
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if "text" in part:
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self.text += part["text"]
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else:
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self.text = ""
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return CompatibleResponse(response_data)
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except requests.exceptions.RequestException as e:
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if attempt < max_retries - 1:
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logger.warning(f"网络请求失败，等待30秒后重试: {str(e)}")
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time.sleep(30)
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continue
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else:
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logger.error(f"原生Gemini API请求失败: {str(e)}")
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raise
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except Exception as e:
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if attempt < max_retries - 1 and "429" in str(e):
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logger.warning("原生Gemini API 触发限流，等待65秒后重试...")
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time.sleep(65)
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continue
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else:
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logger.error(f"原生Gemini 生成文案错误: {str(e)}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理原生Gemini API的响应"""
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if not response or not response.text:
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raise ValueError("原生Gemini API返回无效响应")
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return response.text.strip()
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class QwenGenerator(BaseGenerator):
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"""阿里云千问 API 生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str):
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super().__init__(model_name, api_key, prompt)
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self.client = OpenAI(
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api_key=api_key,
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base_url=base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# Qwen特定参数
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self.default_params = {
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**self.default_params,
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"stream": False,
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"user": "script_generator"
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}
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def _generate(self, messages: list, params: dict) -> any:
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"""实现千问特定的生成逻辑"""
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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**params
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)
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return response
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except Exception as e:
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logger.error(f"Qwen generation error: {str(e)}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理千问的响应"""
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if not response or not response.choices:
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raise ValueError("Invalid response from Qwen API")
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return response.choices[0].message.content.strip()
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class MoonshotGenerator(BaseGenerator):
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"""Moonshot API 生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str):
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super().__init__(model_name, api_key, prompt)
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self.client = OpenAI(
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api_key=api_key,
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base_url=base_url or "https://api.moonshot.cn/v1"
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)
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# Moonshot特定参数
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self.default_params = {
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**self.default_params,
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"stream": False,
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"stop": None,
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"user": "script_generator",
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"tools": None
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}
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def _generate(self, messages: list, params: dict) -> any:
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"""实现Moonshot特定的生成逻辑，包含429误重试机制"""
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while True:
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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**params
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)
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return response
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except Exception as e:
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error_str = str(e)
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if "Error code: 429" in error_str:
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logger.warning("Moonshot API 触发限流，等待65秒后重试...")
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time.sleep(65) # 等待65秒后重试
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continue
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else:
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logger.error(f"Moonshot generation error: {error_str}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理Moonshot的响应"""
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if not response or not response.choices:
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raise ValueError("Invalid response from Moonshot API")
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return response.choices[0].message.content.strip()
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class DeepSeekGenerator(BaseGenerator):
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"""DeepSeek API 生成器实现"""
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def __init__(self, model_name: str, api_key: str, prompt: str, base_url: str):
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super().__init__(model_name, api_key, prompt)
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self.client = OpenAI(
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api_key=api_key,
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base_url=base_url or "https://api.deepseek.com"
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)
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# DeepSeek特定参数
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self.default_params = {
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**self.default_params,
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"stream": False,
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"user": "script_generator"
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}
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def _generate(self, messages: list, params: dict) -> any:
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"""实现DeepSeek特定的生成逻辑"""
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try:
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response = self.client.chat.completions.create(
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model=self.model_name, # deepseek-chat 或 deepseek-coder
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messages=messages,
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**params
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)
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return response
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except Exception as e:
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logger.error(f"DeepSeek generation error: {str(e)}")
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raise
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def _process_response(self, response: any) -> str:
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"""处理DeepSeek的响应"""
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if not response or not response.choices:
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raise ValueError("Invalid response from DeepSeek API")
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return response.choices[0].message.content.strip()
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class ScriptProcessor:
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def __init__(self, model_name: str, api_key: str = None, base_url: str = None, prompt: str = None, video_theme: str = ""):
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self.model_name = model_name
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self.api_key = api_key
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self.base_url = base_url
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self.video_theme = video_theme
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self.prompt = prompt or self._get_default_prompt()
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# 根据模型名称选择对应的生成器
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logger.info(f"文本 LLM 提供商: {model_name}")
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if 'gemini' in model_name.lower():
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self.generator = GeminiGenerator(model_name, self.api_key, self.prompt, self.base_url)
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elif 'qwen' in model_name.lower():
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self.generator = QwenGenerator(model_name, self.api_key, self.prompt, self.base_url)
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elif 'moonshot' in model_name.lower():
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self.generator = MoonshotGenerator(model_name, self.api_key, self.prompt, self.base_url)
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elif 'deepseek' in model_name.lower():
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self.generator = DeepSeekGenerator(model_name, self.api_key, self.prompt, self.base_url)
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else:
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self.generator = OpenAIGenerator(model_name, self.api_key, self.prompt, self.base_url)
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def _get_default_prompt(self) -> str:
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return f"""
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你是一位极具幽默感的短视频脚本创作大师，擅长用"温和的违反"制造笑点，让主题为 《{self.video_theme}》 的视频既有趣又富有传播力。
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你的任务是将视频画面描述转化为能在社交平台疯狂传播的爆款口播文案。
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目标受众：热爱生活、追求独特体验的18-35岁年轻人
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文案风格：基于HKRR理论 + 段子手精神
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主题：{self.video_theme}
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【创作核心理念】
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1. 敢于用"温和的违反"制造笑点，但不能过于冒犯
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2. 巧妙运用中国式幽默，让观众会心一笑
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3. 保持轻松愉快的叙事基调
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【爆款内容四要素】
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【快乐元素 Happy】
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1. 用调侃的语气描述画面
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2. 巧妙植入网络流行梗，增加内容的传播性
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3. 适时自嘲，展现真实且有趣的一面
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【知识价值 Knowledge】
|
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1. 用段子手的方式解释专业知识
|
||
2. 在幽默中传递实用的生活常识
|
||
|
||
【情感共鸣 Resonance】
|
||
1. 描述"真实但夸张"的环境描述
|
||
2. 把对自然的感悟融入俏皮话中
|
||
3. 用接地气的表达方式拉近与观众距离
|
||
|
||
【节奏控制 Rhythm】
|
||
1. 像讲段子一样，注意铺垫和包袱的节奏
|
||
2. 确保每段都有笑点，但不强求
|
||
3. 段落结尾干净利落，不拖泥带水
|
||
|
||
【连贯性要求】
|
||
1. 新生成的内容必须自然衔接上一段文案的结尾
|
||
2. 使用恰当的连接词和过渡语，确保叙事流畅
|
||
3. 保持人物视角和语气的一致性
|
||
4. 避免重复上一段已经提到的信息
|
||
5. 确保情节的逻辑连续性
|
||
|
||
我会按顺序提供多段视频画面描述。请创作既搞笑又能火爆全网的口播文案。
|
||
记住：要敢于用"温和的违反"制造笑点，但要把握好尺度，让观众在轻松愉快中感受到乐趣。"""
|
||
|
||
def calculate_duration_and_word_count(self, time_range: str) -> int:
|
||
"""
|
||
计算时间范围的持续时长并估算合适的字数
|
||
|
||
Args:
|
||
time_range: 时间范围字符串,格式为 "HH:MM:SS,mmm-HH:MM:SS,mmm"
|
||
例如: "00:00:50,100-00:01:21,500"
|
||
|
||
Returns:
|
||
int: 估算的合适字数
|
||
基于经验公式: 每0.35秒可以说一个字
|
||
例如: 10秒可以说约28个字 (10/0.35≈28.57)
|
||
"""
|
||
try:
|
||
start_str, end_str = time_range.split('-')
|
||
|
||
def time_to_seconds(time_str: str) -> float:
|
||
"""
|
||
将时间字符串转换为秒数(带毫秒精度)
|
||
|
||
Args:
|
||
time_str: 时间字符串,格式为 "HH:MM:SS,mmm"
|
||
例如: "00:00:50,100" 表示50.1秒
|
||
|
||
Returns:
|
||
float: 转换后的秒数(带毫秒)
|
||
"""
|
||
try:
|
||
# 处理毫秒部分
|
||
time_part, ms_part = time_str.split(',')
|
||
hours, minutes, seconds = map(int, time_part.split(':'))
|
||
milliseconds = int(ms_part)
|
||
|
||
# 转换为秒
|
||
total_seconds = (hours * 3600) + (minutes * 60) + seconds + (milliseconds / 1000)
|
||
return total_seconds
|
||
|
||
except ValueError as e:
|
||
logger.warning(f"时间格式解析错误: {time_str}, error: {e}")
|
||
return 0.0
|
||
|
||
# 计算开始和结束时间的秒数
|
||
start_seconds = time_to_seconds(start_str)
|
||
end_seconds = time_to_seconds(end_str)
|
||
|
||
# 计算持续时间(秒)
|
||
duration = end_seconds - start_seconds
|
||
|
||
# 根据经验公式计算字数: 每0.5秒一个字
|
||
word_count = int(duration / 0.4)
|
||
|
||
# 确保字数在合理范围内
|
||
word_count = max(10, min(word_count, 500)) # 限制在10-500字之间
|
||
|
||
logger.debug(f"时间范围 {time_range} 的持续时间为 {duration:.3f}秒, 估算字数: {word_count}")
|
||
return word_count
|
||
|
||
except Exception as e:
|
||
logger.warning(f"字数计算错误: {traceback.format_exc()}")
|
||
return 100 # 发生错误时返回默认字数
|
||
|
||
def process_frames(self, frame_content_list: List[Dict]) -> List[Dict]:
|
||
for frame_content in frame_content_list:
|
||
word_count = self.calculate_duration_and_word_count(frame_content["timestamp"])
|
||
script = self.generator.generate_script(frame_content["picture"], word_count)
|
||
frame_content["narration"] = script
|
||
frame_content["OST"] = 2
|
||
logger.info(f"时间范围: {frame_content['timestamp']}, 建议字数: {word_count}")
|
||
logger.info(script)
|
||
|
||
self._save_results(frame_content_list)
|
||
return frame_content_list
|
||
|
||
def _save_results(self, frame_content_list: List[Dict]):
|
||
"""保存处理结果，并添加新的时间戳"""
|
||
try:
|
||
def format_timestamp(seconds: float) -> str:
|
||
"""将秒数转换为 HH:MM:SS,mmm 格式"""
|
||
hours = int(seconds // 3600)
|
||
minutes = int((seconds % 3600) // 60)
|
||
seconds_remainder = seconds % 60
|
||
whole_seconds = int(seconds_remainder)
|
||
milliseconds = int((seconds_remainder - whole_seconds) * 1000)
|
||
|
||
return f"{hours:02d}:{minutes:02d}:{whole_seconds:02d},{milliseconds:03d}"
|
||
|
||
# 计算新的时间戳
|
||
current_time = 0.0 # 当前时间点（秒，包含毫秒）
|
||
|
||
for frame in frame_content_list:
|
||
# 获取原始时间戳的持续时间
|
||
start_str, end_str = frame['timestamp'].split('-')
|
||
|
||
def time_to_seconds(time_str: str) -> float:
|
||
"""将时间字符串转换为秒数（包含毫秒）"""
|
||
try:
|
||
if ',' in time_str:
|
||
time_part, ms_part = time_str.split(',')
|
||
ms = float(ms_part) / 1000
|
||
else:
|
||
time_part = time_str
|
||
ms = 0
|
||
|
||
parts = time_part.split(':')
|
||
if len(parts) == 3: # HH:MM:SS
|
||
h, m, s = map(float, parts)
|
||
seconds = h * 3600 + m * 60 + s
|
||
elif len(parts) == 2: # MM:SS
|
||
m, s = map(float, parts)
|
||
seconds = m * 60 + s
|
||
else: # SS
|
||
seconds = float(parts[0])
|
||
|
||
return seconds + ms
|
||
except Exception as e:
|
||
logger.error(f"时间格式转换错误 {time_str}: {str(e)}")
|
||
return 0.0
|
||
|
||
# 计算当前片段的持续时间
|
||
start_seconds = time_to_seconds(start_str)
|
||
end_seconds = time_to_seconds(end_str)
|
||
duration = end_seconds - start_seconds
|
||
|
||
# 设置新的时间戳
|
||
new_start = format_timestamp(current_time)
|
||
new_end = format_timestamp(current_time + duration)
|
||
frame['new_timestamp'] = f"{new_start}-{new_end}"
|
||
|
||
# 更新当前时间点
|
||
current_time += duration
|
||
|
||
# 保存结果
|
||
file_name = f"storage/json/step2_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||
os.makedirs(os.path.dirname(file_name), exist_ok=True)
|
||
|
||
with open(file_name, 'w', encoding='utf-8') as file:
|
||
json.dump(frame_content_list, file, ensure_ascii=False, indent=4)
|
||
|
||
logger.info(f"保存脚本成功，总时长: {format_timestamp(current_time)}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"保存结果时发生错误: {str(e)}\n{traceback.format_exc()}")
|
||
raise
|