feat: 集成 AI 视频生成功能到 MixVideo V2

🎬 主要功能：
- ✅ 完整的 AI 视频生成模块 (Python)
- ✅ 图片转视频 API 集成 (字节跳动 Seedance)
- ✅ 云存储支持 (腾讯云 COS)
- ✅ 单张图片和批量处理模式
- ✅ 现代化 React 界面组件
- ✅ Tauri 桥接通信

🛠️ 技术实现：
- Python 模块：VideoGenerator, CloudStorage, APIClient
- Rust 命令：generate_ai_video, batch_generate_ai_videos
- React 组件：AIVideoGenerator, AIVideoPage
- 状态管理：useAIVideoStore (Zustand)
- 路由集成：/ai-video 页面

�� 新增文件：
- python_core/ai_video/ - AI 视频生成核心模块
- src/components/AIVideoGenerator.tsx - 主要 UI 组件
- src/pages/AIVideoPage.tsx - AI 视频生成页面
- src/stores/useAIVideoStore.ts - 状态管理

🎯 功能特性：
- 支持 Lite (720p) 和 Pro (1080p) 模型
- 可配置视频时长 (5秒/10秒)
- 实时进度跟踪和任务管理
- 批量处理多张图片
- 云存储自动上传下载
- 错误处理和重试机制

🔗 界面集成：
- 侧边栏导航添加 'AI 视频' 入口
- 首页快速操作卡片
- 完整的用户引导和帮助文档

这是从原始 Tkinter GUI 到现代 Web 应用的完整迁移！

src-tauri/src/commands.rs

@@ -1,4 +1,24 @@
 use serde::{Deserialize, Serialize};
+
+#[derive(Debug, Deserialize)]
+pub struct AIVideoRequest {
+    pub image_path: String,
+    pub prompt: String,
+    pub duration: String,
+    pub model_type: String,
+    pub output_path: Option<String>,
+    pub timeout: Option<u32>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct BatchAIVideoRequest {
+    pub image_folder: String,
+    pub prompts: Vec<String>,
+    pub output_folder: String,
+    pub duration: String,
+    pub model_type: String,
+    pub timeout: Option<u32>,
+}
 use std::process::Command;
 use tauri::State;
 
@@ -165,3 +185,61 @@ pub async fn load_project(project_path: String) -> Result<ProjectInfo, String> {
         Err(format!("Python script error: {}", error))
     }
 }
+
+#[tauri::command]
+pub async fn generate_ai_video(request: AIVideoRequest) -> Result<String, String> {
+    let mut args = vec![
+        "python_core/ai_video/video_generator.py".to_string(),
+        "--action".to_string(),
+        "single".to_string(),
+        "--image".to_string(),
+        request.image_path,
+        "--prompt".to_string(),
+        request.prompt,
+        "--duration".to_string(),
+        request.duration,
+        "--model".to_string(),
+        request.model_type,
+    ];
+
+    if let Some(output_path) = request.output_path {
+        args.push("--output".to_string());
+        args.push(output_path);
+    }
+
+    if let Some(timeout) = request.timeout {
+        args.push("--timeout".to_string());
+        args.push(timeout.to_string());
+    }
+
+    execute_python_command(&args).await
+}
+
+#[tauri::command]
+pub async fn batch_generate_ai_videos(request: BatchAIVideoRequest) -> Result<String, String> {
+    let prompts_json = serde_json::to_string(&request.prompts)
+        .map_err(|e| format!("Failed to serialize prompts: {}", e))?;
+
+    let mut args = vec![
+        "python_core/ai_video/video_generator.py".to_string(),
+        "--action".to_string(),
+        "batch".to_string(),
+        "--folder".to_string(),
+        request.image_folder,
+        "--prompts".to_string(),
+        prompts_json,
+        "--output".to_string(),
+        request.output_folder,
+        "--duration".to_string(),
+        request.duration,
+        "--model".to_string(),
+        request.model_type,
+    ];
+
+    if let Some(timeout) = request.timeout {
+        args.push("--timeout".to_string());
+        args.push(timeout.to_string());
+    }
+
+    execute_python_command(&args).await
+}

src-tauri/src/lib.rs

@@ -24,7 +24,9 @@ pub fn run() {
       commands::analyze_audio,
       commands::get_project_info,
       commands::save_project,
-      commands::load_project
+      commands::load_project,
+      commands::generate_ai_video,
+      commands::batch_generate_ai_videos
     ])
     .run(tauri::generate_context!())
     .expect("error while running tauri application");