mirror of
https://github.com/linyqh/NarratoAI.git
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117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
# 纪录片脚本生成
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import asyncio
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import json
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import time
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import traceback
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import streamlit as st
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from loguru import logger
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from app.config import config
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from app.services.documentary.frame_analysis_service import DocumentaryFrameAnalysisService
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def _normalize_progress_value(progress: float | int) -> int:
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"""Normalize mixed progress inputs to Streamlit's 0-100 integer range."""
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try:
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value = float(progress)
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except (TypeError, ValueError):
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return 0
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if 0.0 <= value <= 1.0:
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value *= 100
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return max(0, min(100, int(round(value))))
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def generate_script_docu(params):
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"""
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生成纪录片视频脚本。
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要求: 原视频无字幕无配音
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适合场景: 纪录片、动物搞笑解说、荒野建造等
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"""
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progress_bar = st.progress(0)
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status_text = st.empty()
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def update_progress(progress: float, message: str = ""):
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normalized_progress = _normalize_progress_value(progress)
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progress_bar.progress(normalized_progress)
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if message:
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status_text.text(f"🎬 {message}")
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else:
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status_text.text(f"📊 进度: {normalized_progress}%")
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try:
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with st.spinner("正在生成脚本..."):
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if not params.video_origin_path:
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st.error("请先选择视频文件")
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return
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vision_llm_provider = (
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st.session_state.get("vision_llm_provider") or config.app.get("vision_llm_provider", "openai")
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).lower()
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vision_api_key = (
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st.session_state.get(f"vision_{vision_llm_provider}_api_key")
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or config.app.get(f"vision_{vision_llm_provider}_api_key")
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)
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vision_model = (
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st.session_state.get(f"vision_{vision_llm_provider}_model_name")
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or config.app.get(f"vision_{vision_llm_provider}_model_name")
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)
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vision_base_url = (
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st.session_state.get(f"vision_{vision_llm_provider}_base_url")
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or config.app.get(f"vision_{vision_llm_provider}_base_url", "")
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)
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if not vision_api_key or not vision_model:
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raise ValueError(
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f"未配置 {vision_llm_provider} 的 API Key 或模型名称。"
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f"请在设置页面配置 vision_{vision_llm_provider}_api_key 和 vision_{vision_llm_provider}_model_name"
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)
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frame_interval_input = st.session_state.get("frame_interval_input") or config.frames.get(
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"frame_interval_input", 3
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)
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vision_batch_size = st.session_state.get("vision_batch_size") or config.frames.get("vision_batch_size", 10)
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vision_max_concurrency = st.session_state.get("vision_max_concurrency") or config.frames.get(
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"vision_max_concurrency", 2
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)
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update_progress(10, "正在提取关键帧...")
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service = DocumentaryFrameAnalysisService()
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script_items = asyncio.run(
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service.generate_documentary_script(
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video_path=params.video_origin_path,
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video_theme=st.session_state.get("video_theme", ""),
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custom_prompt=st.session_state.get("custom_prompt", ""),
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frame_interval_input=frame_interval_input,
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vision_batch_size=vision_batch_size,
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vision_llm_provider=vision_llm_provider,
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progress_callback=update_progress,
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vision_api_key=vision_api_key,
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vision_model_name=vision_model,
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vision_base_url=vision_base_url,
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max_concurrency=vision_max_concurrency,
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)
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)
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logger.info(f"纪录片解说脚本生成完成，共 {len(script_items)} 个片段")
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script = json.dumps(script_items, ensure_ascii=False, indent=2)
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if isinstance(script, list):
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st.session_state["video_clip_json"] = script
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elif isinstance(script, str):
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st.session_state["video_clip_json"] = json.loads(script)
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update_progress(100, "脚本生成完成")
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time.sleep(0.1)
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progress_bar.progress(100)
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status_text.text("🎉 脚本生成完成！")
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st.success("✅ 视频脚本生成成功！")
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except Exception as err:
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st.error(f"❌ 生成过程中发生错误: {str(err)}")
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logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
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finally:
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time.sleep(2)
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progress_bar.empty()
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status_text.empty()
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