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https://github.com/linyqh/NarratoAI.git
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优化大模型生成脚本逻辑
This commit is contained in:
linyq
parent
990994e9cd
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@ -10,11 +10,12 @@ from openai import AzureOpenAI
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from openai.types.chat import ChatCompletion
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import google.generativeai as gemini
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from googleapiclient.errors import ResumableUploadError
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from google.api_core.exceptions import FailedPrecondition
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from google.generativeai.types import HarmCategory, HarmBlockThreshold
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from google.api_core.exceptions import *
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from google.generativeai.types import *
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import subprocess
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from app.config import config
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from app.utils.utils import clean_model_output
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_max_retries = 5
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@ -105,9 +106,39 @@ Method = """
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"""
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def _generate_response(prompt: str) -> str:
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def handle_exception(err):
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if isinstance(err, PermissionDenied):
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logger.error("403 用户没有权限访问该资源")
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elif isinstance(err, ResourceExhausted):
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logger.error("429 您的配额已用尽。请稍后重试。请考虑设置自动重试来处理这些错误")
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elif isinstance(err, InvalidArgument):
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logger.error("400 参数无效。例如，文件过大，超出了载荷大小限制。另一个事件提供了无效的 API 密钥。")
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elif isinstance(err, AlreadyExists):
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logger.error("409 已存在具有相同 ID 的已调参模型。对新模型进行调参时，请指定唯一的模型 ID。")
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elif isinstance(err, RetryError):
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logger.error("使用不支持 gRPC 的代理时可能会引起此错误。请尝试将 REST 传输与 genai.configure(..., transport=rest) 搭配使用。")
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elif isinstance(err, BlockedPromptException):
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logger.error("400 出于安全原因，该提示已被屏蔽。")
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elif isinstance(err, BrokenResponseError):
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logger.error("500 流式传输响应已损坏。在访问需要完整响应的内容（例如聊天记录）时引发。查看堆栈轨迹中提供的错误详情。")
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elif isinstance(err, IncompleteIterationError):
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logger.error("500 访问需要完整 API 响应但流式响应尚未完全迭代的内容时引发。对响应对象调用 resolve() 以使用迭代器。")
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elif isinstance(err, ConnectionError):
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logger.error("网络连接错误，请检查您的网络连接。")
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else:
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logger.error(f"视频转录失败, 下面是具体报错信息: \n{traceback.format_exc()} \n问题排查指南: https://ai.google.dev/gemini-api/docs/troubleshooting?hl=zh-cn")
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return ""
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def _generate_response(prompt: str, llm_provider: str = None) -> str:
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"""
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调用大模型通用方法
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prompt：
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llm_provider：
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"""
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content = ""
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llm_provider = config.app.get("llm_provider", "openai")
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if not llm_provider:
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llm_provider = config.app.get("llm_provider", "openai")
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logger.info(f"llm provider: {llm_provider}")
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if llm_provider == "g4f":
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model_name = config.app.get("g4f_model_name", "")
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@ -223,46 +254,23 @@ def _generate_response(prompt: str) -> str:
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genai.configure(api_key=api_key, transport="rest")
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generation_config = {
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"temperature": 0.5,
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"top_p": 1,
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"top_k": 1,
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"max_output_tokens": 2048,
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safety_settings = {
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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safety_settings = [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_ONLY_HIGH",
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_ONLY_HIGH",
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_ONLY_HIGH",
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_ONLY_HIGH",
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},
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]
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model = genai.GenerativeModel(
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model_name=model_name,
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generation_config=generation_config,
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safety_settings=safety_settings,
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)
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try:
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response = model.generate_content(prompt)
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candidates = response.candidates
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generated_text = candidates[0].content.parts[0].text
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except (AttributeError, IndexError) as e:
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print("Gemini Error:", e)
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return generated_text
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return response.text
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except Exception as err:
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return handle_exception(err)
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if llm_provider == "cloudflare":
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import requests
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@ -345,6 +353,43 @@ def _generate_response(prompt: str) -> str:
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return content.replace("\n", "")
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def _generate_response_video(prompt: str, llm_provider: str, video_file: str | File) -> str:
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"""
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多模态能力大模型
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"""
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if llm_provider == "gemini":
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api_key = config.app.get("gemini_api_key")
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model_name = config.app.get("gemini_model_name")
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base_url = "***"
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else:
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raise ValueError(
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"llm_provider 未设置，请在 config.toml 文件中进行设置。"
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)
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if llm_provider == "gemini":
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import google.generativeai as genai
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genai.configure(api_key=api_key, transport="rest")
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safety_settings = {
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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model = genai.GenerativeModel(
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model_name=model_name,
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safety_settings=safety_settings,
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)
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try:
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response = model.generate_content([prompt, video_file])
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return response.text
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except Exception as err:
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return handle_exception(err)
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def compress_video(input_path: str, output_path: str):
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"""
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压缩视频文件
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@ -353,7 +398,7 @@ def compress_video(input_path: str, output_path: str):
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output_path: 输出压缩后的视频文件路径
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"""
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# 指定 ffmpeg 的完整路径
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ffmpeg_path = os.getenv("FFMPEG_PATH") or config.app.get("ffmpeg_path")
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ffmpeg_path = os.getenv("FFMPEG_PATH") or config.app.get("ffmpeg_path") or "ffmpeg"
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# 如果压缩后的视频文件已经存在，则直接使用
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if os.path.exists(output_path):
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@ -370,6 +415,7 @@ def compress_video(input_path: str, output_path: str):
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"-b:a", "128k",
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output_path
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]
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logger.info(f"执行命令: {' '.join(command)}")
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subprocess.run(command, check=True)
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except subprocess.CalledProcessError as e:
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logger.error(f"视频压缩失败: {e}")
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@ -396,7 +442,13 @@ def generate_script(
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compress_video(video_path, compressed_video_path)
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# 2. 转录视频
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transcription = gemini_video_transcription(video_name=video_name, video_path=compressed_video_path, language=language, progress_text=progress_text)
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transcription = gemini_video_transcription(
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video_name=video_name,
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video_path=compressed_video_path,
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language=language,
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progress_text=progress_text,
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llm_provider="gemini"
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)
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# # 清理压缩后的视频文件
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# try:
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@ -406,13 +458,16 @@ def generate_script(
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# 3. 编写解说文案
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progress_text.text("解说文案中...")
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script = writing_short_play(video_plot, video_name)
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script = writing_short_play(video_plot, video_name, "openai")
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# 4. 文案匹配画面
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progress_text.text("画面匹配中...")
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matched_script = screen_matching(huamian=transcription, wenan=script)
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if transcription != "":
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progress_text.text("画面匹配中...")
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matched_script = screen_matching(huamian=transcription, wenan=script, llm_provider="openai")
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return matched_script
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return matched_script
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else:
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return ""
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def generate_terms(video_subject: str, video_script: str, amount: int = 5) -> List[str]:
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@ -565,57 +620,52 @@ def gemini_video2json(video_origin_name: str, video_origin_path: str, video_plot
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return response
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def gemini_video_transcription(video_name: str, video_path: str, language: str, progress_text: st.empty = ""):
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def gemini_video_transcription(video_name: str, video_path: str, language: str, llm_provider: str, progress_text: st.empty = ""):
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'''
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使用 gemini-1.5-xxx 进行视频画面转录
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'''
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api_key = config.app.get("gemini_api_key")
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model_name = config.app.get("gemini_model_name")
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gemini.configure(api_key=api_key)
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model = gemini.GenerativeModel(model_name=model_name)
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prompt = """
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Please transcribe the audio, include timestamps, and provide visual descriptions, then output in JSON format.
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Please use %s output
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Use this JSON schema:
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Graphics = {"timestamp": "MM:SS-MM:SS", "picture": "str", "quotes": "str"(If no one says anything, use an empty string instead.)}
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Return: list[Graphics]
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""" % language
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请转录音频，包括时间戳，并提供视觉描述，然后以 JSON 格式输出，当前视频中使用的语言为 %s。
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在转录视频时，请通过确保以下条件来完成转录：
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1. 画面描述使用语言: %s 进行输出。
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2. 同一个画面合并为一个转录记录。
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3. 使用以下 JSON schema:
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Graphics = {"timestamp": "MM:SS-MM:SS"(时间戳格式), "picture": "str"(画面描述), "speech": "str"(台词，如果没有人说话，则使用空字符串。)}
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Return: list[Graphics]
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4. 请以严格的 JSON 格式返回数据，不要包含任何注释、标记或其他字符。数据应符合 JSON 语法，可以被 json.loads() 函数直接解析， 不要添加 ```json 或其他标记。
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""" % (language, language)
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logger.debug(f"视频名称: {video_name}")
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try:
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progress_text.text("上传视频中...")
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gemini_video_file = gemini.upload_file(video_path)
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logger.debug(f"上传视频至 Google cloud 成功: {gemini_video_file.name}")
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logger.debug(f"视频 {gemini_video_file.name} 上传至 Google cloud 成功, 开始解析...")
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while gemini_video_file.state.name == "PROCESSING":
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import time
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time.sleep(1)
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gemini_video_file = gemini.get_file(gemini_video_file.name)
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progress_text.text(f"解析视频中, 当前状态: {gemini_video_file.state.name}")
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# logger.debug(f"视频当前状态(ACTIVE才可用): {gemini_video_file.state.name}")
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if gemini_video_file.state.name == "FAILED":
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raise ValueError(gemini_video_file.state.name)
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elif gemini_video_file.state.name == "ACTIVE":
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progress_text.text("解析完成")
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logger.debug("解析完成, 开始转录...")
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except ResumableUploadError as err:
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logger.error(f"上传视频至 Google cloud 失败, 用户的位置信息不支持用于该API; \n{traceback.format_exc()}")
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return ""
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return False
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except FailedPrecondition as err:
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logger.error(f"400 用户位置不支持 Google API 使用。\n{traceback.format_exc()}")
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return ""
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return False
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progress_text.text("视频转录中...")
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response = model.generate_content(
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[prompt, gemini_video_file],
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safety_settings={
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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)
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logger.success("视频转录成功")
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return response.text
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try:
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response = _generate_response_video(prompt=prompt, llm_provider=llm_provider, video_file=gemini_video_file)
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logger.success("视频转录成功")
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return response
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except Exception as err:
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return handle_exception(err)
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def writing_movie(video_plot, video_name):
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@ -640,33 +690,34 @@ def writing_movie(video_plot, video_name):
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3. 仅输出解说文案，不输出任何其他内容。
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4. 不要包含小标题，每个段落以 \n 进行分隔。
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"""
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response = model.generate_content(
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prompt,
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generation_config=gemini.types.GenerationConfig(
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candidate_count=1,
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temperature=1.3,
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),
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safety_settings={
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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)
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logger.debug(response.text)
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logger.debug("字数：", len(response.text))
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return response.text
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try:
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response = model.generate_content(
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prompt,
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generation_config=gemini.types.GenerationConfig(
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candidate_count=1,
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temperature=1.3,
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),
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safety_settings={
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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)
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return response.text
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except Exception as err:
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return handle_exception(err)
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def writing_short_play(video_plot: str, video_name: str):
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def writing_short_play(video_plot: str, video_name: str, llm_provider: str):
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"""
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影视解说（短剧解说）
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"""
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api_key = config.app.get("gemini_api_key")
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model_name = config.app.get("gemini_model_name")
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gemini.configure(api_key=api_key)
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model = gemini.GenerativeModel(model_name)
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# api_key = config.app.get("gemini_api_key")
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# # model_name = config.app.get("gemini_model_name")
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#
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# gemini.configure(api_key=api_key)
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# model = gemini.GenerativeModel(model_name)
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if not video_plot:
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raise ValueError("短剧的简介不能为空")
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@ -686,33 +737,34 @@ def writing_short_play(video_plot: str, video_name: str):
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3. 仅输出解说文案，不输出任何其他内容。
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4. 不要包含小标题，每个段落以 \\n 进行分隔。
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"""
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response = model.generate_content(
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prompt,
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generation_config=gemini.types.GenerationConfig(
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candidate_count=1,
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temperature=1.0,
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),
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safety_settings={
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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)
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logger.success("解说文案生成成功")
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return response.text
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try:
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# if "gemini" in model_name:
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# response = model.generate_content(
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# prompt,
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# generation_config=gemini.types.GenerationConfig(
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# candidate_count=1,
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# temperature=1.0,
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# ),
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# safety_settings={
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# HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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# HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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# HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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# HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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# }
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# )
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# else:
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response = _generate_response(prompt, llm_provider)
|
||||
logger.success("解说文案生成成功")
|
||||
logger.debug(response)
|
||||
return response
|
||||
except Exception as err:
|
||||
return handle_exception(err)
|
||||
|
||||
|
||||
def screen_matching(huamian: str, wenan: str):
|
||||
def screen_matching(huamian: str, wenan: str, llm_provider: str):
|
||||
"""
|
||||
画面匹配
|
||||
"""
|
||||
api_key = config.app.get("gemini_api_key")
|
||||
model_name = config.app.get("gemini_model_name")
|
||||
|
||||
gemini.configure(api_key=api_key)
|
||||
model = gemini.GenerativeModel(model_name)
|
||||
|
||||
if not huamian:
|
||||
raise ValueError("画面不能为空")
|
||||
if not wenan:
|
||||
@ -731,25 +783,20 @@ def screen_matching(huamian: str, wenan: str):
|
||||
%s
|
||||
</COPYWRITER>
|
||||
|
||||
Use this JSON schema:
|
||||
script = {'picture': str, 'timestamp': str, "narration": str, "OST": bool}
|
||||
Return: list[script]
|
||||
在匹配的过程中，请通过确保以下条件来完成匹配：
|
||||
- 使用以下 JSON schema:
|
||||
script = {'picture': str, 'timestamp': str(时间戳), "narration": str, "OST": bool(是否开启原声)}
|
||||
Return: list[script]
|
||||
- 请以严格的 JSON 格式返回数据，不要包含任何注释、标记或其他字符。数据应符合 JSON 语法，可以被 json.loads() 函数直接解析， 不要添加 ```json 或其他标记。
|
||||
-
|
||||
""" % (huamian, wenan)
|
||||
response = model.generate_content(
|
||||
prompt,
|
||||
generation_config=gemini.types.GenerationConfig(
|
||||
candidate_count=1,
|
||||
temperature=1.0,
|
||||
),
|
||||
safety_settings={
|
||||
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
|
||||
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
|
||||
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
|
||||
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
|
||||
}
|
||||
)
|
||||
logger.success("匹配成功")
|
||||
return response.text
|
||||
try:
|
||||
response = _generate_response(prompt, llm_provider)
|
||||
logger.success("匹配成功")
|
||||
logger.debug(response)
|
||||
return response
|
||||
except Exception as err:
|
||||
return handle_exception(err)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
@ -760,7 +807,7 @@ if __name__ == "__main__":
|
||||
# gemini_video_transcription(video_subject, video_path, language)
|
||||
|
||||
# 2. 解说文案
|
||||
video_path = "E:\\projects\\NarratoAI\\resource\\videos\\2.mp4"
|
||||
video_path = "/Users/apple/Desktop/home/NarratoAI/resource/videos/1.mp4"
|
||||
video_plot = """
|
||||
李自忠拿着儿子李牧名下的存折，去银行取钱给儿子救命，却被要求证明"你儿子是你儿子"。
|
||||
走投无路时碰到银行被抢劫，劫匪给了他两沓钱救命，李自忠却因此被银行以抢劫罪起诉，并顶格判处20年有期徒刑。
|
||||
@ -768,4 +815,9 @@ if __name__ == "__main__":
|
||||
"""
|
||||
res = generate_script(video_path, video_plot, video_name="第二十条之无罪释放")
|
||||
# res = generate_script(video_path, video_plot, video_name="海岸")
|
||||
print("res \n", res)
|
||||
print("脚本生成成功:\n", res)
|
||||
res = clean_model_output(res)
|
||||
aaa = json.loads(res)
|
||||
print(json.dumps(aaa, indent=2, ensure_ascii=False))
|
||||
# response = _generate_response("你好，介绍一下你自己")
|
||||
# print(response)
|
||||
|
||||
@ -365,3 +365,13 @@ def add_new_timestamps(scenes):
|
||||
updated_scenes.append(new_scene)
|
||||
|
||||
return updated_scenes
|
||||
|
||||
|
||||
def clean_model_output(output):
|
||||
"""
|
||||
模型输出包含 ```json 标记时的处理
|
||||
"""
|
||||
if "```json" in output:
|
||||
print("##########")
|
||||
output = output.replace("```json", "").replace("```", "")
|
||||
return output.strip()
|
||||
|
||||
@ -1,20 +1,5 @@
|
||||
[app]
|
||||
project_version="0.1.2"
|
||||
video_source = "pexels" # "pexels" or "pixabay"
|
||||
# Pexels API Key
|
||||
# Register at https://www.pexels.com/api/ to get your API key.
|
||||
# You can use multiple keys to avoid rate limits.
|
||||
# For example: pexels_api_keys = ["123adsf4567adf89","abd1321cd13efgfdfhi"]
|
||||
# 特别注意格式，Key 用英文双引号括起来，多个Key用逗号隔开
|
||||
pexels_api_keys = []
|
||||
|
||||
# Pixabay API Key
|
||||
# Register at https://pixabay.com/api/docs/ to get your API key.
|
||||
# You can use multiple keys to avoid rate limits.
|
||||
# For example: pixabay_api_keys = ["123adsf4567adf89","abd1321cd13efgfdfhi"]
|
||||
# 特别注意格式，Key 用英文双引号括起来，多个Key用逗号隔开
|
||||
pixabay_api_keys = []
|
||||
|
||||
project_version="0.2.0"
|
||||
# 如果你没有 OPENAI API Key，可以使用 g4f 代替，或者使用国内的 Moonshot API
|
||||
# If you don't have an OPENAI API Key, you can use g4f instead
|
||||
|
||||
@ -27,6 +12,8 @@
|
||||
# qwen (通义千问)
|
||||
# gemini
|
||||
llm_provider="openai"
|
||||
# 支持多模态视频理解能力的大模型
|
||||
llm_provider_video="gemini"
|
||||
|
||||
########## Ollama Settings
|
||||
# No need to set it unless you want to use your own proxy
|
||||
@ -184,8 +171,8 @@
|
||||
### Example: "http://user:pass@proxy:1234"
|
||||
### Doc: https://requests.readthedocs.io/en/latest/user/advanced/#proxies
|
||||
|
||||
# http = "http://10.10.1.10:3128"
|
||||
# https = "http://10.10.1.10:1080"
|
||||
http = "http://127.0.0.1:7890"
|
||||
https = "http://127.0.0.1:7890"
|
||||
|
||||
[azure]
|
||||
# Azure Speech API Key
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user