fix: 解决AI视频分类并发瓶颈问题

- 移除VideoClassificationService中共享的GeminiService实例
- 改为在classify_video_with_gemini方法中为每个任务创建独立的GeminiService
- 解决Arc<Mutex<GeminiService>>导致的串行处理瓶颈
- 实现真正的并发处理，提升AI分类性能

修复前：所有worker共享一个GeminiService实例，导致串行等待
修复后：每个worker使用独立的GeminiService实例，实现真正并发

apps/desktop/src-tauri/src/business/services/video_classification_service.rs

@@ -8,7 +8,6 @@ use crate::infrastructure::file_system::FileSystemService;
 use anyhow::{Result, anyhow};
 use std::sync::Arc;
 use std::path::Path;
-use tokio::sync::Mutex;
 use serde_json;
 
 /// AI视频分类业务服务
@@ -17,7 +16,7 @@ pub struct VideoClassificationService {
     video_repo: Arc<VideoClassificationRepository>,
     ai_classification_repo: Arc<AiClassificationRepository>,
     material_repo: Arc<MaterialRepository>,
-    gemini_service: Arc<Mutex<GeminiService>>,
+    gemini_config: Option<GeminiConfig>,
 }
 
 impl VideoClassificationService {
@@ -28,13 +27,11 @@ impl VideoClassificationService {
         material_repo: Arc<MaterialRepository>,
         gemini_config: Option<GeminiConfig>,
     ) -> Self {
-        let gemini_service = Arc::new(Mutex::new(GeminiService::new(gemini_config)));
-        
         Self {
             video_repo,
             ai_classification_repo,
             material_repo,
-            gemini_service,
+            gemini_config,
         }
     }
 
@@ -239,7 +236,8 @@ impl VideoClassificationService {
 
     /// 使用Gemini进行视频分类
     async fn classify_video_with_gemini(&self, task: &mut VideoClassificationTask, prompt: &str) -> Result<VideoClassificationRecord> {
-        let mut gemini_service = self.gemini_service.lock().await;
+        // 为每个任务创建独立的GeminiService实例，避免并发瓶颈
+        let mut gemini_service = GeminiService::new(self.gemini_config.clone());
 
         // 调用Gemini API进行分类
         let (file_uri, raw_response) = gemini_service.classify_video(&task.video_file_path, prompt).await?;