mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-12 03:02:48 +00:00
636 lines
27 KiB
Python
636 lines
27 KiB
Python
import os
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import glob
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import json
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import time
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import asyncio
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import traceback
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import requests
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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.models.schema import VideoClipParams
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from app.services import llm
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from app.utils import utils, check_script, vision_analyzer, video_processor
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from webui.utils import file_utils
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def render_script_panel(tr):
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"""渲染脚本配置面板"""
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with st.container(border=True):
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st.write(tr("Video Script Configuration"))
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params = VideoClipParams()
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# 渲染脚本文件选择
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render_script_file(tr, params)
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# 渲染视频文件选择
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render_video_file(tr, params)
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# 渲染视频主题和提示词
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render_video_details(tr)
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# 渲染脚本操作按钮
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render_script_buttons(tr, params)
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def render_script_file(tr, params):
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"""渲染脚本文件选择"""
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script_list = [(tr("None"), ""), (tr("Auto Generate"), "auto")]
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# 获取已有脚本文件
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suffix = "*.json"
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script_dir = utils.script_dir()
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files = glob.glob(os.path.join(script_dir, suffix))
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file_list = []
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for file in files:
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file_list.append({
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"name": os.path.basename(file),
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"file": file,
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"ctime": os.path.getctime(file)
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})
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file_list.sort(key=lambda x: x["ctime"], reverse=True)
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for file in file_list:
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display_name = file['file'].replace(config.root_dir, "")
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script_list.append((display_name, file['file']))
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# 找到保存的脚本文件在列表中的索引
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saved_script_path = st.session_state.get('video_clip_json_path', '')
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selected_index = 0
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for i, (_, path) in enumerate(script_list):
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if path == saved_script_path:
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selected_index = i
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break
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selected_script_index = st.selectbox(
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tr("Script Files"),
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index=selected_index, # 使用找到的索引
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options=range(len(script_list)),
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format_func=lambda x: script_list[x][0]
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)
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script_path = script_list[selected_script_index][1]
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st.session_state['video_clip_json_path'] = script_path
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params.video_clip_json_path = script_path
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def render_video_file(tr, params):
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"""渲染视频文件选择"""
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video_list = [(tr("None"), ""), (tr("Upload Local Files"), "local")]
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# 获取已有视频文件
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for suffix in ["*.mp4", "*.mov", "*.avi", "*.mkv"]:
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video_files = glob.glob(os.path.join(utils.video_dir(), suffix))
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for file in video_files:
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display_name = file.replace(config.root_dir, "")
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video_list.append((display_name, file))
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selected_video_index = st.selectbox(
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tr("Video File"),
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index=0,
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options=range(len(video_list)),
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format_func=lambda x: video_list[x][0]
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)
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video_path = video_list[selected_video_index][1]
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st.session_state['video_origin_path'] = video_path
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params.video_origin_path = video_path
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if video_path == "local":
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uploaded_file = st.file_uploader(
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tr("Upload Local Files"),
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type=["mp4", "mov", "avi", "flv", "mkv"],
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accept_multiple_files=False,
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)
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if uploaded_file is not None:
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video_file_path = os.path.join(utils.video_dir(), uploaded_file.name)
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file_name, file_extension = os.path.splitext(uploaded_file.name)
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if os.path.exists(video_file_path):
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timestamp = time.strftime("%Y%m%d%H%M%S")
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file_name_with_timestamp = f"{file_name}_{timestamp}"
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video_file_path = os.path.join(utils.video_dir(), file_name_with_timestamp + file_extension)
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with open(video_file_path, "wb") as f:
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f.write(uploaded_file.read())
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st.success(tr("File Uploaded Successfully"))
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st.session_state['video_origin_path'] = video_file_path
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params.video_origin_path = video_file_path
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time.sleep(1)
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st.rerun()
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def render_video_details(tr):
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"""渲染视频主题和提示词"""
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video_theme = st.text_input(tr("Video Theme"))
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prompt = st.text_area(
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tr("Generation Prompt"),
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value=st.session_state.get('video_plot', ''),
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help=tr("Custom prompt for LLM, leave empty to use default prompt"),
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height=180
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)
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st.session_state['video_name'] = video_theme
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st.session_state['video_plot'] = prompt
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return video_theme, prompt
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def render_script_buttons(tr, params):
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"""渲染脚本操作按钮"""
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# 生成/加载按钮
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script_path = st.session_state.get('video_clip_json_path', '')
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if script_path == "auto":
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button_name = tr("Generate Video Script")
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elif script_path:
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button_name = tr("Load Video Script")
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else:
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button_name = tr("Please Select Script File")
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if st.button(button_name, key="script_action", disabled=not script_path):
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if script_path == "auto":
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generate_script(tr, params)
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else:
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load_script(tr, script_path)
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# 视频脚本编辑区
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video_clip_json_details = st.text_area(
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tr("Video Script"),
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value=json.dumps(st.session_state.get('video_clip_json', []), indent=2, ensure_ascii=False),
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height=180
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)
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# 操作按钮行
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button_cols = st.columns(3)
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with button_cols[0]:
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if st.button(tr("Check Format"), key="check_format", use_container_width=True):
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check_script_format(tr, video_clip_json_details)
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with button_cols[1]:
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if st.button(tr("Save Script"), key="save_script", use_container_width=True):
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save_script(tr, video_clip_json_details)
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with button_cols[2]:
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script_valid = st.session_state.get('script_format_valid', False)
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if st.button(tr("Crop Video"), key="crop_video", disabled=not script_valid, use_container_width=True):
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crop_video(tr, params)
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def check_script_format(tr, script_content):
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"""检查脚本格式"""
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try:
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result = check_script.check_format(script_content)
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if result.get('success'):
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st.success(tr("Script format check passed"))
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st.session_state['script_format_valid'] = True
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else:
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st.error(f"{tr('Script format check failed')}: {result.get('message')}")
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st.session_state['script_format_valid'] = False
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except Exception as e:
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st.error(f"{tr('Script format check error')}: {str(e)}")
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st.session_state['script_format_valid'] = False
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def load_script(tr, script_path):
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"""加载脚本文件"""
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try:
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with open(script_path, 'r', encoding='utf-8') as f:
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script = f.read()
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script = utils.clean_model_output(script)
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st.session_state['video_clip_json'] = json.loads(script)
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st.success(tr("Script loaded successfully"))
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st.rerun()
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except Exception as e:
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st.error(f"{tr('Failed to load script')}: {str(e)}")
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def generate_script(tr, params):
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"""生成视频脚本"""
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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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progress_bar.progress(progress)
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if message:
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status_text.text(f"{progress}% - {message}")
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else:
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status_text.text(f"进度: {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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st.stop()
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return
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# ===================提取键帧===================
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update_progress(10, "正在提取关键帧...")
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# 创建临时目录用于存储关键帧
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keyframes_dir = os.path.join(utils.temp_dir(), "keyframes")
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video_hash = utils.md5(params.video_origin_path + str(os.path.getmtime(params.video_origin_path)))
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video_keyframes_dir = os.path.join(keyframes_dir, video_hash)
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# 检查是否已经提取过关键帧
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keyframe_files = []
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if os.path.exists(video_keyframes_dir):
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# 获取已有的关键帧文件
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for filename in sorted(os.listdir(video_keyframes_dir)):
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if filename.endswith('.jpg'):
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keyframe_files.append(os.path.join(video_keyframes_dir, filename))
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if keyframe_files:
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logger.info(f"使用已缓存的关键帧: {video_keyframes_dir}")
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st.info(f"使用已缓存的关键帧，如需重新提取请删除目录: {video_keyframes_dir}")
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update_progress(20, f"使用已缓存关键帧，共 {len(keyframe_files)} 帧")
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# 如果没有缓存的关键帧，则进行提取
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if not keyframe_files:
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try:
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# 确保目录存在
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os.makedirs(video_keyframes_dir, exist_ok=True)
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# 初始化视频处理器
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processor = video_processor.VideoProcessor(params.video_origin_path)
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# 处理视频并提取关键帧
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processor.process_video(
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output_dir=video_keyframes_dir,
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skip_seconds=0
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)
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# 获取所有关键帧文件路径
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for filename in sorted(os.listdir(video_keyframes_dir)):
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if filename.endswith('.jpg'):
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keyframe_files.append(os.path.join(video_keyframes_dir, filename))
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if not keyframe_files:
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raise Exception("未提取到任何关键帧")
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update_progress(20, f"关键帧提取完成，共 {len(keyframe_files)} 帧")
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except Exception as e:
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# 如果提取失败，清理创建的目录
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try:
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if os.path.exists(video_keyframes_dir):
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import shutil
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shutil.rmtree(video_keyframes_dir)
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except Exception as cleanup_err:
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logger.error(f"清理失败的关键帧目录时出错: {cleanup_err}")
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raise Exception(f"关键帧提取失败: {str(e)}")
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# 根据不同的 LLM 提供商处理
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video_llm_provider = st.session_state.get('video_llm_providers', 'Gemini').lower()
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if video_llm_provider == 'gemini':
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try:
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# ===================初始化视觉分析器===================
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update_progress(30, "正在初始化视觉分析器...")
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# 从配置中获取 Gemini 相关配置
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vision_api_key = st.session_state.get('vision_gemini_api_key')
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vision_model = st.session_state.get('vision_gemini_model_name')
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vision_base_url = st.session_state.get('vision_gemini_base_url')
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if not vision_api_key or not vision_model:
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raise ValueError("未配置 Gemini API Key 或者 模型，请在基础设置中配置")
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analyzer = vision_analyzer.VisionAnalyzer(
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model_name=vision_model,
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api_key=vision_api_key
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)
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update_progress(40, "正在分析关键帧...")
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# ===================创建异步事件循环===================
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# 执行异步分析
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results = loop.run_until_complete(
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analyzer.analyze_images(
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images=keyframe_files,
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prompt=config.app.get('vision_analysis_prompt'),
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batch_size=config.app.get("vision_batch_size", 5)
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)
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)
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loop.close()
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# ===================处理分析结果===================
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update_progress(60, "正在整理分析结果...")
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# 合并所有批次的析结果
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frame_analysis = ""
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for result in results:
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if 'error' in result:
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logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}")
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continue
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# 获取当前批次的图片文件
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batch_start = result['batch_index'] * config.app.get("vision_batch_size", 5)
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batch_end = min(batch_start + config.app.get("vision_batch_size", 5), len(keyframe_files))
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batch_files = keyframe_files[batch_start:batch_end]
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# 提取首帧和尾帧的时间戳
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first_frame = os.path.basename(batch_files[0])
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last_frame = os.path.basename(batch_files[-1])
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# 从文件名中提取时间信息
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first_time = first_frame.split('_')[2].replace('.jpg', '') # 000002
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last_time = last_frame.split('_')[2].replace('.jpg', '') # 000005
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# 转换为分:秒格式
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def format_timestamp(time_str):
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seconds = int(time_str)
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minutes = seconds // 60
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seconds = seconds % 60
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return f"{minutes:02d}:{seconds:02d}"
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first_timestamp = format_timestamp(first_time)
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last_timestamp = format_timestamp(last_time)
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# 添加带时间戳的分析结果
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frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
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frame_analysis += result['response']
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frame_analysis += "\n"
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if not frame_analysis.strip():
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raise Exception("未能生成有效的帧分析结果")
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# 保存分析结果
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analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
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with open(analysis_path, 'w', encoding='utf-8') as f:
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f.write(frame_analysis)
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update_progress(70, "正在生成脚本...")
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# 构建完整的上下文
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context = {
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'video_name': st.session_state.get('video_name', ''),
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'video_plot': st.session_state.get('video_plot', ''),
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'frame_analysis': frame_analysis,
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'total_frames': len(keyframe_files)
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}
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# 从配置中获取文本生成相关配置
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text_provider = config.app.get('text_llm_provider', 'gemini').lower()
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text_api_key = config.app.get(f'text_{text_provider}_api_key')
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text_model = config.app.get(f'text_{text_provider}_model_name', 'gemini-1.5-pro')
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text_base_url = config.app.get(f'text_{text_provider}_base_url')
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# 构建帧内容列表
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frame_content_list = []
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for i, result in enumerate(results):
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if 'error' in result:
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continue
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# 获取当前批次的图片文件
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batch_start = result['batch_index'] * config.app.get("vision_batch_size", 5)
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batch_end = min(batch_start + config.app.get("vision_batch_size", 5), len(keyframe_files))
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batch_files = keyframe_files[batch_start:batch_end]
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# 提取首帧和尾帧的时间戳
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first_frame = os.path.basename(batch_files[0])
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last_frame = os.path.basename(batch_files[-1])
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# 从文件名中提取时间信息
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first_time = first_frame.split('_')[2].replace('.jpg', '') # 000002
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last_time = last_frame.split('_')[2].replace('.jpg', '') # 000005
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# 转换为分:秒格式
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def format_timestamp(time_str):
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seconds = int(time_str)
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minutes = seconds // 60
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seconds = seconds % 60
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return f"{minutes:02d}:{seconds:02d}"
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first_timestamp = format_timestamp(first_time)
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last_timestamp = format_timestamp(last_time)
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# 构建时间戳范围字符串 (MM:SS-MM:SS 格式)
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timestamp_range = f"{first_timestamp}-{last_timestamp}"
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frame_content = {
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"timestamp": timestamp_range, # 使用时间范围格式 "MM:SS-MM:SS"
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"picture": result['response'], # 图片分析结果
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"narration": "", # 将由 ScriptProcessor 生成
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"OST": 2 # 默认值
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}
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frame_content_list.append(frame_content)
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logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
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if not frame_content_list:
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raise Exception("没有有效的帧内容可以处理")
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# 使用 ScriptProcessor 生成脚本
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from app.utils.script_generator import ScriptProcessor
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processor = ScriptProcessor(
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model_name=text_model,
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api_key=text_api_key,
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prompt="" # 使用默认提示词
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)
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# 处理帧内容生成脚本
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script_result = processor.process_frames(frame_content_list)
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# 将结果转换为JSON字符串
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script = json.dumps(script_result, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.exception(f"Gemini 处理过程中发生错误\n{traceback.format_exc()}")
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raise Exception(f"视觉分析失败: {str(e)}")
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else: # NarratoAPI
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try:
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# 创建临时目录
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temp_dir = utils.temp_dir("narrato")
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# 打包关键帧
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update_progress(30, "正在打包关键帧...")
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zip_path = os.path.join(temp_dir, f"keyframes_{int(time.time())}.zip")
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||
|
||
if not file_utils.create_zip(keyframe_files, zip_path):
|
||
raise Exception("打包关键帧失败")
|
||
|
||
# 获取API配置
|
||
api_url = st.session_state.get('narrato_api_url', 'http://127.0.0.1:8000/api/v1/video/analyze')
|
||
api_key = st.session_state.get('narrato_api_key')
|
||
|
||
if not api_key:
|
||
raise ValueError("未配置 Narrato API Key，请在基础设置中配置")
|
||
|
||
# 准备API请求
|
||
headers = {
|
||
'X-API-Key': api_key,
|
||
'accept': 'application/json'
|
||
}
|
||
|
||
api_params = {
|
||
'batch_size': st.session_state.get('narrato_batch_size', 10),
|
||
'use_ai': False,
|
||
'start_offset': 0,
|
||
'vision_model': st.session_state.get('narrato_vision_model', 'gemini-1.5-flash'),
|
||
'vision_api_key': st.session_state.get('narrato_vision_key'),
|
||
'llm_model': st.session_state.get('narrato_llm_model', 'qwen-plus'),
|
||
'llm_api_key': st.session_state.get('narrato_llm_key'),
|
||
'custom_prompt': st.session_state.get('video_plot', '')
|
||
}
|
||
|
||
# 发送API请求
|
||
update_progress(40, "正在上传文件...")
|
||
with open(zip_path, 'rb') as f:
|
||
files = {'file': (os.path.basename(zip_path), f, 'application/x-zip-compressed')}
|
||
try:
|
||
response = requests.post(
|
||
api_url,
|
||
headers=headers,
|
||
params=api_params,
|
||
files=files,
|
||
timeout=30 # 设置超时时间
|
||
)
|
||
response.raise_for_status()
|
||
except requests.RequestException as e:
|
||
raise Exception(f"API请求失败: {str(e)}")
|
||
|
||
task_data = response.json()
|
||
task_id = task_data.get('task_id')
|
||
if not task_id:
|
||
raise Exception(f"无效的API响应: {response.text}")
|
||
|
||
# 轮询任务状态
|
||
update_progress(50, "正在等待分析结果...")
|
||
retry_count = 0
|
||
max_retries = 60 # 最多等待2分钟
|
||
|
||
while retry_count < max_retries:
|
||
try:
|
||
status_response = requests.get(
|
||
f"{api_url}/tasks/{task_id}",
|
||
headers=headers,
|
||
timeout=10
|
||
)
|
||
status_response.raise_for_status()
|
||
task_status = status_response.json()['data']
|
||
|
||
if task_status['status'] == 'SUCCESS':
|
||
script = task_status['result']
|
||
break
|
||
elif task_status['status'] in ['FAILURE', 'RETRY']:
|
||
raise Exception(f"任务失败: {task_status.get('error')}")
|
||
|
||
retry_count += 1
|
||
progress = min(70, 50 + (retry_count * 20 / max_retries))
|
||
update_progress(progress, "正在分析中...")
|
||
time.sleep(2)
|
||
|
||
except requests.RequestException as e:
|
||
logger.warning(f"获取任务状态失败，重试中: {str(e)}")
|
||
retry_count += 1
|
||
time.sleep(2)
|
||
continue
|
||
|
||
if retry_count >= max_retries:
|
||
raise Exception("任务执行超时")
|
||
|
||
except Exception as e:
|
||
logger.exception("NarratoAPI 处理过程中发生错误")
|
||
raise Exception(f"NarratoAPI 处理失败: {str(e)}")
|
||
finally:
|
||
# 清理临时文件
|
||
try:
|
||
if os.path.exists(zip_path):
|
||
os.remove(zip_path)
|
||
except Exception as e:
|
||
logger.warning(f"清理临时文件失败: {str(e)}")
|
||
|
||
if script is None:
|
||
st.error("生成脚本失败，请检查日志")
|
||
st.stop()
|
||
|
||
script = utils.clean_model_output(script)
|
||
st.session_state['video_clip_json'] = json.loads(script)
|
||
update_progress(90, "脚本生成完成")
|
||
|
||
time.sleep(0.5)
|
||
progress_bar.progress(100)
|
||
status_text.text("脚本生成完成！")
|
||
st.success("视频脚本生成成功！")
|
||
|
||
except Exception as err:
|
||
st.error(f"生成过程中发生错误: {str(err)}")
|
||
logger.exception("生成脚本时发生错误")
|
||
finally:
|
||
time.sleep(2)
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
|
||
|
||
def save_script(tr, video_clip_json_details):
|
||
"""保存视频脚本"""
|
||
if not video_clip_json_details:
|
||
st.error(tr("请输入视频脚本"))
|
||
st.stop()
|
||
|
||
with st.spinner(tr("Save Script")):
|
||
script_dir = utils.script_dir()
|
||
timestamp = time.strftime("%Y-%m%d-%H%M%S")
|
||
save_path = os.path.join(script_dir, f"{timestamp}.json")
|
||
|
||
try:
|
||
data = json.loads(video_clip_json_details)
|
||
with open(save_path, 'w', encoding='utf-8') as file:
|
||
json.dump(data, file, ensure_ascii=False, indent=4)
|
||
st.session_state['video_clip_json'] = data
|
||
st.session_state['video_clip_json_path'] = save_path
|
||
|
||
# 更新配置
|
||
config.app["video_clip_json_path"] = save_path
|
||
|
||
# 显示成功消息
|
||
st.success(tr("Script saved successfully"))
|
||
|
||
# 强制重新加载页面<E9A1B5><E99DA2>更新选择框
|
||
time.sleep(0.5) # 给一点时间让用户看到成功消息
|
||
st.rerun()
|
||
|
||
except Exception as err:
|
||
st.error(f"{tr('Failed to save script')}: {str(err)}")
|
||
st.stop()
|
||
|
||
|
||
def crop_video(tr, params):
|
||
"""裁剪视频"""
|
||
progress_bar = st.progress(0)
|
||
status_text = st.empty()
|
||
|
||
def update_progress(progress):
|
||
progress_bar.progress(progress)
|
||
status_text.text(f"剪辑进度: {progress}%")
|
||
|
||
try:
|
||
utils.cut_video(params, update_progress)
|
||
time.sleep(0.5)
|
||
progress_bar.progress(100)
|
||
status_text.text("剪辑完成！")
|
||
st.success("视频剪辑成功完成！")
|
||
except Exception as e:
|
||
st.error(f"剪辑过程中发生错误: {str(e)}")
|
||
finally:
|
||
time.sleep(2)
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
|
||
|
||
def get_script_params():
|
||
"""获取脚本参数"""
|
||
return {
|
||
'video_language': st.session_state.get('video_language', ''),
|
||
'video_clip_json_path': st.session_state.get('video_clip_json_path', ''),
|
||
'video_origin_path': st.session_state.get('video_origin_path', ''),
|
||
'video_name': st.session_state.get('video_name', ''),
|
||
'video_plot': st.session_state.get('video_plot', '')
|
||
}
|