feat: 实现AI视频分类功能
新功能: - 集成Google Gemini API进行视频智能分类 - 实现任务队列系统支持批量处理 - 添加实时进度显示和状态管理 - 自动文件整理到分类文件夹 架构改进: - 遵循Tauri开发规范的分层架构设计 - 完整的数据模型和仓库层实现 - 异步任务处理和错误处理机制 - 类型安全的前后端通信接口 用户界面: - MaterialCard组件添加AI分类按钮 - VideoClassificationProgress进度显示组件 - 优美的动画效果和响应式设计 - 符合前端开发规范的UI/UX优化 数据库扩展: - 新增video_classification_records表 - 新增video_classification_tasks表 - 完整的索引优化和外键约束 技术实现: - Rust后端服务层完整实现 - React/TypeScript前端状态管理 - Zustand状态存储和API封装 - 完善的错误处理和用户提示 文档: - 完整的功能文档和API说明 - 架构设计和使用流程说明 - 开发规范遵循情况说明 Closes #AI视频分类功能开发
This commit is contained in:
imeepos
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|
||||
"tower",
|
||||
@@ -3311,6 +3542,15 @@ dependencies = [
|
||||
"windows-sys 0.59.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rustls-pemfile"
|
||||
version = "1.0.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1c74cae0a4cf6ccbbf5f359f08efdf8ee7e1dc532573bf0db71968cb56b1448c"
|
||||
dependencies = [
|
||||
"base64 0.21.7",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rustversion"
|
||||
version = "1.0.21"
|
||||
@@ -3332,6 +3572,15 @@ dependencies = [
|
||||
"winapi-util",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "schannel"
|
||||
version = "0.1.27"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1f29ebaa345f945cec9fbbc532eb307f0fdad8161f281b6369539c8d84876b3d"
|
||||
dependencies = [
|
||||
"windows-sys 0.59.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "schemars"
|
||||
version = "0.8.22"
|
||||
@@ -3395,6 +3644,29 @@ version = "1.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
|
||||
|
||||
[[package]]
|
||||
name = "security-framework"
|
||||
version = "2.11.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "897b2245f0b511c87893af39b033e5ca9cce68824c4d7e7630b5a1d339658d02"
|
||||
dependencies = [
|
||||
"bitflags 2.9.1",
|
||||
"core-foundation 0.9.4",
|
||||
"core-foundation-sys",
|
||||
"libc",
|
||||
"security-framework-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "security-framework-sys"
|
||||
version = "2.14.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49db231d56a190491cb4aeda9527f1ad45345af50b0851622a7adb8c03b01c32"
|
||||
dependencies = [
|
||||
"core-foundation-sys",
|
||||
"libc",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "selectors"
|
||||
version = "0.24.0"
|
||||
@@ -3665,7 +3937,7 @@ dependencies = [
|
||||
"bytemuck",
|
||||
"cfg_aliases",
|
||||
"core-graphics",
|
||||
"foreign-types",
|
||||
"foreign-types 0.5.0",
|
||||
"js-sys",
|
||||
"log",
|
||||
"objc2 0.5.2",
|
||||
@@ -3780,6 +4052,12 @@ dependencies = [
|
||||
"unicode-ident",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sync_wrapper"
|
||||
version = "0.1.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
|
||||
|
||||
[[package]]
|
||||
name = "sync_wrapper"
|
||||
version = "1.0.2"
|
||||
@@ -3800,6 +4078,27 @@ dependencies = [
|
||||
"syn 2.0.104",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "system-configuration"
|
||||
version = "0.5.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ba3a3adc5c275d719af8cb4272ea1c4a6d668a777f37e115f6d11ddbc1c8e0e7"
|
||||
dependencies = [
|
||||
"bitflags 1.3.2",
|
||||
"core-foundation 0.9.4",
|
||||
"system-configuration-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "system-configuration-sys"
|
||||
version = "0.5.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "a75fb188eb626b924683e3b95e3a48e63551fcfb51949de2f06a9d91dbee93c9"
|
||||
dependencies = [
|
||||
"core-foundation-sys",
|
||||
"libc",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "system-deps"
|
||||
version = "6.2.2"
|
||||
@@ -3820,7 +4119,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49c380ca75a231b87b6c9dd86948f035012e7171d1a7c40a9c2890489a7ffd8a"
|
||||
dependencies = [
|
||||
"bitflags 2.9.1",
|
||||
"core-foundation",
|
||||
"core-foundation 0.10.1",
|
||||
"core-graphics",
|
||||
"crossbeam-channel",
|
||||
"dispatch",
|
||||
@@ -3884,7 +4183,7 @@ dependencies = [
|
||||
"glob",
|
||||
"gtk",
|
||||
"heck 0.5.0",
|
||||
"http",
|
||||
"http 1.3.1",
|
||||
"jni",
|
||||
"libc",
|
||||
"log",
|
||||
@@ -3897,7 +4196,7 @@ dependencies = [
|
||||
"percent-encoding",
|
||||
"plist",
|
||||
"raw-window-handle",
|
||||
"reqwest",
|
||||
"reqwest 0.12.22",
|
||||
"serde",
|
||||
"serde_json",
|
||||
"serde_repr",
|
||||
@@ -4070,7 +4369,7 @@ dependencies = [
|
||||
"cookie",
|
||||
"dpi",
|
||||
"gtk",
|
||||
"http",
|
||||
"http 1.3.1",
|
||||
"jni",
|
||||
"objc2 0.6.1",
|
||||
"objc2-ui-kit",
|
||||
@@ -4090,7 +4389,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "902b5aa9035e16f342eb64f8bf06ccdc2808e411a2525ed1d07672fa4e780bad"
|
||||
dependencies = [
|
||||
"gtk",
|
||||
"http",
|
||||
"http 1.3.1",
|
||||
"jni",
|
||||
"log",
|
||||
"objc2 0.6.1",
|
||||
@@ -4123,7 +4422,7 @@ dependencies = [
|
||||
"dunce",
|
||||
"glob",
|
||||
"html5ever",
|
||||
"http",
|
||||
"http 1.3.1",
|
||||
"infer",
|
||||
"json-patch",
|
||||
"kuchikiki",
|
||||
@@ -4305,6 +4604,16 @@ dependencies = [
|
||||
"syn 2.0.104",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tokio-native-tls"
|
||||
version = "0.3.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "bbae76ab933c85776efabc971569dd6119c580d8f5d448769dec1764bf796ef2"
|
||||
dependencies = [
|
||||
"native-tls",
|
||||
"tokio",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tokio-stream"
|
||||
version = "0.1.17"
|
||||
@@ -4453,7 +4762,7 @@ dependencies = [
|
||||
"futures-core",
|
||||
"futures-util",
|
||||
"pin-project-lite",
|
||||
"sync_wrapper",
|
||||
"sync_wrapper 1.0.2",
|
||||
"tokio",
|
||||
"tower-layer",
|
||||
"tower-service",
|
||||
@@ -4468,8 +4777,8 @@ dependencies = [
|
||||
"bitflags 2.9.1",
|
||||
"bytes",
|
||||
"futures-util",
|
||||
"http",
|
||||
"http-body",
|
||||
"http 1.3.1",
|
||||
"http-body 1.0.1",
|
||||
"iri-string",
|
||||
"pin-project-lite",
|
||||
"tower",
|
||||
@@ -4655,6 +4964,12 @@ dependencies = [
|
||||
"unic-common",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "unicase"
|
||||
version = "2.8.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "75b844d17643ee918803943289730bec8aac480150456169e647ed0b576ba539"
|
||||
|
||||
[[package]]
|
||||
name = "unicode-ident"
|
||||
version = "1.0.18"
|
||||
@@ -5504,6 +5819,16 @@ dependencies = [
|
||||
"memchr",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "winreg"
|
||||
version = "0.50.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "524e57b2c537c0f9b1e69f1965311ec12182b4122e45035b1508cd24d2adadb1"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"windows-sys 0.48.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "winreg"
|
||||
version = "0.55.0"
|
||||
@@ -5544,7 +5869,7 @@ dependencies = [
|
||||
"gdkx11",
|
||||
"gtk",
|
||||
"html5ever",
|
||||
"http",
|
||||
"http 1.3.1",
|
||||
"javascriptcore-rs",
|
||||
"jni",
|
||||
"kuchikiki",
|
||||
|
||||
@@ -36,6 +36,7 @@ lazy_static = "1.4"
|
||||
tracing = "0.1"
|
||||
tracing-subscriber = { version = "0.3", features = ["env-filter", "chrono"] }
|
||||
tracing-appender = "0.2"
|
||||
reqwest = { version = "0.11", features = ["json", "multipart"] }
|
||||
|
||||
[dev-dependencies]
|
||||
tempfile = "3.8"
|
||||
|
||||
@@ -3,3 +3,5 @@ pub mod material_service;
|
||||
pub mod model_service;
|
||||
pub mod async_material_service;
|
||||
pub mod ai_classification_service;
|
||||
pub mod video_classification_service;
|
||||
pub mod video_classification_queue;
|
||||
|
||||
@@ -0,0 +1,335 @@
|
||||
use crate::data::models::video_classification::*;
|
||||
use crate::business::services::video_classification_service::VideoClassificationService;
|
||||
use anyhow::{Result, anyhow};
|
||||
use std::sync::Arc;
|
||||
use tokio::sync::{Mutex, RwLock};
|
||||
use tokio::time::{sleep, Duration};
|
||||
use std::collections::HashMap;
|
||||
use uuid::Uuid;
|
||||
|
||||
/// 队列状态
|
||||
#[derive(Debug, Clone, PartialEq, serde::Serialize, serde::Deserialize)]
|
||||
pub enum QueueStatus {
|
||||
Stopped,
|
||||
Running,
|
||||
Paused,
|
||||
}
|
||||
|
||||
/// 队列统计信息
|
||||
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
||||
pub struct QueueStats {
|
||||
pub status: QueueStatus,
|
||||
pub total_tasks: usize,
|
||||
pub pending_tasks: usize,
|
||||
pub processing_tasks: usize,
|
||||
pub completed_tasks: usize,
|
||||
pub failed_tasks: usize,
|
||||
pub current_task_id: Option<String>,
|
||||
pub processing_rate: f64, // 任务/分钟
|
||||
}
|
||||
|
||||
/// 任务进度信息
|
||||
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
||||
pub struct TaskProgress {
|
||||
pub task_id: String,
|
||||
pub status: TaskStatus,
|
||||
pub progress_percentage: f64,
|
||||
pub current_step: String,
|
||||
pub error_message: Option<String>,
|
||||
pub started_at: Option<chrono::DateTime<chrono::Utc>>,
|
||||
pub estimated_completion: Option<chrono::DateTime<chrono::Utc>>,
|
||||
}
|
||||
|
||||
/// AI视频分类任务队列
|
||||
/// 遵循 Tauri 开发规范的业务层设计模式
|
||||
pub struct VideoClassificationQueue {
|
||||
service: Arc<VideoClassificationService>,
|
||||
status: Arc<RwLock<QueueStatus>>,
|
||||
current_task: Arc<Mutex<Option<String>>>,
|
||||
task_progress: Arc<RwLock<HashMap<String, TaskProgress>>>,
|
||||
stats: Arc<RwLock<QueueStats>>,
|
||||
max_concurrent_tasks: usize,
|
||||
processing_delay: Duration,
|
||||
}
|
||||
|
||||
impl VideoClassificationQueue {
|
||||
/// 创建新的任务队列
|
||||
pub fn new(service: Arc<VideoClassificationService>) -> Self {
|
||||
Self {
|
||||
service,
|
||||
status: Arc::new(RwLock::new(QueueStatus::Stopped)),
|
||||
current_task: Arc::new(Mutex::new(None)),
|
||||
task_progress: Arc::new(RwLock::new(HashMap::new())),
|
||||
stats: Arc::new(RwLock::new(QueueStats {
|
||||
status: QueueStatus::Stopped,
|
||||
total_tasks: 0,
|
||||
pending_tasks: 0,
|
||||
processing_tasks: 0,
|
||||
completed_tasks: 0,
|
||||
failed_tasks: 0,
|
||||
current_task_id: None,
|
||||
processing_rate: 0.0,
|
||||
})),
|
||||
max_concurrent_tasks: 1, // 目前只支持单任务处理
|
||||
processing_delay: Duration::from_secs(2), // 任务间延迟
|
||||
}
|
||||
}
|
||||
|
||||
/// 启动队列处理
|
||||
pub async fn start(&self) -> Result<()> {
|
||||
let mut status = self.status.write().await;
|
||||
if *status == QueueStatus::Running {
|
||||
return Err(anyhow!("队列已经在运行中"));
|
||||
}
|
||||
|
||||
*status = QueueStatus::Running;
|
||||
drop(status);
|
||||
|
||||
// 更新统计信息
|
||||
self.update_stats().await?;
|
||||
|
||||
// 启动处理循环
|
||||
let queue_clone = self.clone_for_processing();
|
||||
tokio::spawn(async move {
|
||||
queue_clone.processing_loop().await;
|
||||
});
|
||||
|
||||
println!("AI视频分类队列已启动");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 停止队列处理
|
||||
pub async fn stop(&self) -> Result<()> {
|
||||
let mut status = self.status.write().await;
|
||||
*status = QueueStatus::Stopped;
|
||||
|
||||
// 更新统计信息
|
||||
self.update_stats().await?;
|
||||
|
||||
println!("AI视频分类队列已停止");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 暂停队列处理
|
||||
pub async fn pause(&self) -> Result<()> {
|
||||
let mut status = self.status.write().await;
|
||||
if *status != QueueStatus::Running {
|
||||
return Err(anyhow!("队列未在运行中"));
|
||||
}
|
||||
|
||||
*status = QueueStatus::Paused;
|
||||
|
||||
// 更新统计信息
|
||||
self.update_stats().await?;
|
||||
|
||||
println!("AI视频分类队列已暂停");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 恢复队列处理
|
||||
pub async fn resume(&self) -> Result<()> {
|
||||
let mut status = self.status.write().await;
|
||||
if *status != QueueStatus::Paused {
|
||||
return Err(anyhow!("队列未处于暂停状态"));
|
||||
}
|
||||
|
||||
*status = QueueStatus::Running;
|
||||
|
||||
// 更新统计信息
|
||||
self.update_stats().await?;
|
||||
|
||||
println!("AI视频分类队列已恢复");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 添加批量分类任务到队列
|
||||
pub async fn add_batch_tasks(&self, request: BatchClassificationRequest) -> Result<Vec<String>> {
|
||||
let tasks = self.service.create_batch_classification_tasks(request).await?;
|
||||
let task_ids: Vec<String> = tasks.iter().map(|t| t.id.clone()).collect();
|
||||
|
||||
// 初始化任务进度
|
||||
let mut progress_map = self.task_progress.write().await;
|
||||
for task in &tasks {
|
||||
progress_map.insert(task.id.clone(), TaskProgress {
|
||||
task_id: task.id.clone(),
|
||||
status: TaskStatus::Pending,
|
||||
progress_percentage: 0.0,
|
||||
current_step: "等待处理".to_string(),
|
||||
error_message: None,
|
||||
started_at: None,
|
||||
estimated_completion: None,
|
||||
});
|
||||
}
|
||||
|
||||
// 更新统计信息
|
||||
self.update_stats().await?;
|
||||
|
||||
println!("已添加 {} 个分类任务到队列", tasks.len());
|
||||
Ok(task_ids)
|
||||
}
|
||||
|
||||
/// 获取队列统计信息
|
||||
pub async fn get_stats(&self) -> QueueStats {
|
||||
self.stats.read().await.clone()
|
||||
}
|
||||
|
||||
/// 获取任务进度
|
||||
pub async fn get_task_progress(&self, task_id: &str) -> Option<TaskProgress> {
|
||||
self.task_progress.read().await.get(task_id).cloned()
|
||||
}
|
||||
|
||||
/// 获取所有任务进度
|
||||
pub async fn get_all_task_progress(&self) -> HashMap<String, TaskProgress> {
|
||||
self.task_progress.read().await.clone()
|
||||
}
|
||||
|
||||
/// 处理循环
|
||||
async fn processing_loop(&self) {
|
||||
let mut last_task_time = std::time::Instant::now();
|
||||
let mut completed_count = 0;
|
||||
|
||||
loop {
|
||||
let status = self.status.read().await.clone();
|
||||
|
||||
match status {
|
||||
QueueStatus::Stopped => break,
|
||||
QueueStatus::Paused => {
|
||||
sleep(Duration::from_secs(1)).await;
|
||||
continue;
|
||||
}
|
||||
QueueStatus::Running => {
|
||||
// 处理下一个任务
|
||||
match self.process_next_task().await {
|
||||
Ok(Some(_)) => {
|
||||
completed_count += 1;
|
||||
|
||||
// 计算处理速率
|
||||
let elapsed = last_task_time.elapsed();
|
||||
if elapsed.as_secs() > 0 {
|
||||
let rate = (completed_count as f64) / (elapsed.as_secs() as f64 / 60.0);
|
||||
let mut stats = self.stats.write().await;
|
||||
stats.processing_rate = rate;
|
||||
}
|
||||
|
||||
// 任务间延迟
|
||||
sleep(self.processing_delay).await;
|
||||
}
|
||||
Ok(None) => {
|
||||
// 没有待处理任务，等待
|
||||
sleep(Duration::from_secs(5)).await;
|
||||
}
|
||||
Err(e) => {
|
||||
println!("处理任务时发生错误: {}", e);
|
||||
sleep(Duration::from_secs(10)).await;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 定期更新统计信息
|
||||
if let Err(e) = self.update_stats().await {
|
||||
println!("更新统计信息失败: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
println!("AI视频分类队列处理循环已退出");
|
||||
}
|
||||
|
||||
/// 处理下一个任务
|
||||
async fn process_next_task(&self) -> Result<Option<String>> {
|
||||
// 获取待处理任务
|
||||
let pending_tasks = self.service.get_pending_tasks(Some(1)).await?;
|
||||
|
||||
if let Some(task) = pending_tasks.first() {
|
||||
let task_id = task.id.clone();
|
||||
|
||||
// 设置当前任务
|
||||
{
|
||||
let mut current_task = self.current_task.lock().await;
|
||||
*current_task = Some(task_id.clone());
|
||||
}
|
||||
|
||||
// 更新任务进度
|
||||
self.update_task_progress(&task_id, TaskStatus::Uploading, 10.0, "开始处理").await;
|
||||
|
||||
// 处理任务
|
||||
match self.service.process_classification_task(&task_id).await {
|
||||
Ok(_) => {
|
||||
self.update_task_progress(&task_id, TaskStatus::Completed, 100.0, "处理完成").await;
|
||||
println!("任务处理成功: {}", task_id);
|
||||
}
|
||||
Err(e) => {
|
||||
self.update_task_progress_with_error(&task_id, TaskStatus::Failed, e.to_string()).await;
|
||||
println!("任务处理失败: {} - {}", task_id, e);
|
||||
}
|
||||
}
|
||||
|
||||
// 清除当前任务
|
||||
{
|
||||
let mut current_task = self.current_task.lock().await;
|
||||
*current_task = None;
|
||||
}
|
||||
|
||||
Ok(Some(task_id))
|
||||
} else {
|
||||
Ok(None)
|
||||
}
|
||||
}
|
||||
|
||||
/// 更新任务进度
|
||||
async fn update_task_progress(&self, task_id: &str, status: TaskStatus, progress: f64, step: &str) {
|
||||
let mut progress_map = self.task_progress.write().await;
|
||||
if let Some(task_progress) = progress_map.get_mut(task_id) {
|
||||
task_progress.status = status;
|
||||
task_progress.progress_percentage = progress;
|
||||
task_progress.current_step = step.to_string();
|
||||
|
||||
if task_progress.started_at.is_none() && progress > 0.0 {
|
||||
task_progress.started_at = Some(chrono::Utc::now());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 更新任务进度（带错误信息）
|
||||
async fn update_task_progress_with_error(&self, task_id: &str, status: TaskStatus, error: String) {
|
||||
let mut progress_map = self.task_progress.write().await;
|
||||
if let Some(task_progress) = progress_map.get_mut(task_id) {
|
||||
task_progress.status = status;
|
||||
task_progress.error_message = Some(error);
|
||||
task_progress.current_step = "处理失败".to_string();
|
||||
}
|
||||
}
|
||||
|
||||
/// 更新统计信息
|
||||
async fn update_stats(&self) -> Result<()> {
|
||||
let status = self.status.read().await.clone();
|
||||
let current_task = self.current_task.lock().await.clone();
|
||||
|
||||
// 获取分类统计
|
||||
let classification_stats = self.service.get_classification_stats(None).await?;
|
||||
|
||||
let mut stats = self.stats.write().await;
|
||||
stats.status = status;
|
||||
stats.total_tasks = classification_stats.total_tasks as usize;
|
||||
stats.pending_tasks = classification_stats.pending_tasks as usize;
|
||||
stats.processing_tasks = classification_stats.processing_tasks as usize;
|
||||
stats.completed_tasks = classification_stats.completed_tasks as usize;
|
||||
stats.failed_tasks = classification_stats.failed_tasks as usize;
|
||||
stats.current_task_id = current_task;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 克隆用于处理的实例
|
||||
fn clone_for_processing(&self) -> Self {
|
||||
Self {
|
||||
service: Arc::clone(&self.service),
|
||||
status: Arc::clone(&self.status),
|
||||
current_task: Arc::clone(&self.current_task),
|
||||
task_progress: Arc::clone(&self.task_progress),
|
||||
stats: Arc::clone(&self.stats),
|
||||
max_concurrent_tasks: self.max_concurrent_tasks,
|
||||
processing_delay: self.processing_delay,
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,310 @@
|
||||
use crate::data::models::video_classification::*;
|
||||
use crate::data::models::ai_classification::generate_full_prompt;
|
||||
use crate::data::repositories::video_classification_repository::VideoClassificationRepository;
|
||||
use crate::data::repositories::ai_classification_repository::AiClassificationRepository;
|
||||
use crate::data::repositories::material_repository::MaterialRepository;
|
||||
use crate::infrastructure::gemini_service::{GeminiService, GeminiConfig};
|
||||
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
|
||||
/// 遵循 Tauri 开发规范的业务层设计模式
|
||||
pub struct VideoClassificationService {
|
||||
video_repo: Arc<VideoClassificationRepository>,
|
||||
ai_classification_repo: Arc<AiClassificationRepository>,
|
||||
material_repo: Arc<MaterialRepository>,
|
||||
gemini_service: Arc<Mutex<GeminiService>>,
|
||||
}
|
||||
|
||||
impl VideoClassificationService {
|
||||
/// 创建新的视频分类服务实例
|
||||
pub fn new(
|
||||
video_repo: Arc<VideoClassificationRepository>,
|
||||
ai_classification_repo: Arc<AiClassificationRepository>,
|
||||
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,
|
||||
}
|
||||
}
|
||||
|
||||
/// 为素材创建批量分类任务
|
||||
pub async fn create_batch_classification_tasks(&self, request: BatchClassificationRequest) -> Result<Vec<VideoClassificationTask>> {
|
||||
// 获取素材信息
|
||||
let material = self.material_repo.get_by_id(&request.material_id)?
|
||||
.ok_or_else(|| anyhow!("素材不存在: {}", request.material_id))?;
|
||||
|
||||
// 获取素材的所有片段
|
||||
let segments = self.material_repo.get_segments(&request.material_id)?;
|
||||
|
||||
if segments.is_empty() {
|
||||
return Err(anyhow!("素材没有切分片段"));
|
||||
}
|
||||
|
||||
let mut tasks = Vec::new();
|
||||
|
||||
for segment in segments {
|
||||
// 检查是否已经分类（如果不覆盖现有分类）
|
||||
if !request.overwrite_existing {
|
||||
if self.video_repo.is_segment_classified(&segment.id).await? {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
// 检查视频文件是否存在
|
||||
if !Path::new(&segment.file_path).exists() {
|
||||
println!("警告: 视频文件不存在，跳过: {}", segment.file_path);
|
||||
continue;
|
||||
}
|
||||
|
||||
// 创建分类任务
|
||||
let task = VideoClassificationTask::new(
|
||||
segment.id.clone(),
|
||||
request.material_id.clone(),
|
||||
request.project_id.clone(),
|
||||
segment.file_path.clone(),
|
||||
request.priority,
|
||||
);
|
||||
|
||||
let created_task = self.video_repo.create_classification_task(task).await?;
|
||||
tasks.push(created_task);
|
||||
}
|
||||
|
||||
Ok(tasks)
|
||||
}
|
||||
|
||||
/// 处理单个分类任务
|
||||
pub async fn process_classification_task(&self, task_id: &str) -> Result<VideoClassificationRecord> {
|
||||
// 获取任务
|
||||
let mut task = self.get_task_by_id(task_id).await?;
|
||||
|
||||
// 开始处理
|
||||
task.start_processing();
|
||||
self.video_repo.update_classification_task(&task).await?;
|
||||
|
||||
// 获取AI分类提示词
|
||||
let prompt = self.generate_classification_prompt().await?;
|
||||
|
||||
let result = self.classify_video_with_gemini(&mut task, &prompt).await;
|
||||
|
||||
match result {
|
||||
Ok(record) => {
|
||||
// 任务完成
|
||||
task.complete();
|
||||
self.video_repo.update_classification_task(&task).await?;
|
||||
|
||||
// 移动视频文件到分类文件夹
|
||||
if let Err(e) = self.move_video_to_category_folder(&record).await {
|
||||
println!("警告: 移动视频文件失败: {}", e);
|
||||
}
|
||||
|
||||
Ok(record)
|
||||
}
|
||||
Err(e) => {
|
||||
// 任务失败
|
||||
task.fail(e.to_string());
|
||||
self.video_repo.update_classification_task(&task).await?;
|
||||
Err(e)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 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;
|
||||
|
||||
// 调用Gemini API进行分类
|
||||
let (file_uri, raw_response) = gemini_service.classify_video(&task.video_file_path, prompt).await?;
|
||||
|
||||
// 更新任务状态
|
||||
task.set_analyzing(file_uri.clone(), prompt.to_string());
|
||||
self.video_repo.update_classification_task(task).await?;
|
||||
|
||||
// 解析Gemini响应
|
||||
let gemini_response = self.parse_gemini_response(&raw_response)?;
|
||||
|
||||
// 创建分类记录
|
||||
let mut record = VideoClassificationRecord::new(
|
||||
task.segment_id.clone(),
|
||||
task.material_id.clone(),
|
||||
task.project_id.clone(),
|
||||
gemini_response,
|
||||
Some(file_uri),
|
||||
Some(raw_response),
|
||||
);
|
||||
|
||||
// 检查是否需要人工审核
|
||||
if record.needs_review() {
|
||||
record.mark_as_needs_review("置信度或质量评分较低，建议人工审核".to_string());
|
||||
}
|
||||
|
||||
// 保存分类记录
|
||||
let saved_record = self.video_repo.create_classification_record(record).await?;
|
||||
|
||||
Ok(saved_record)
|
||||
}
|
||||
|
||||
/// 解析Gemini响应为结构化数据
|
||||
fn parse_gemini_response(&self, raw_response: &str) -> Result<GeminiClassificationResponse> {
|
||||
// 尝试从响应中提取JSON
|
||||
let json_start = raw_response.find('{');
|
||||
let json_end = raw_response.rfind('}');
|
||||
|
||||
if let (Some(start), Some(end)) = (json_start, json_end) {
|
||||
let json_str = &raw_response[start..=end];
|
||||
|
||||
match serde_json::from_str::<GeminiClassificationResponse>(json_str) {
|
||||
Ok(response) => Ok(response),
|
||||
Err(_) => {
|
||||
// 如果解析失败，创建一个默认响应
|
||||
Ok(GeminiClassificationResponse {
|
||||
category: "未分类".to_string(),
|
||||
confidence: 0.5,
|
||||
reasoning: "AI响应解析失败，使用默认分类".to_string(),
|
||||
features: vec!["解析失败".to_string()],
|
||||
product_match: false,
|
||||
quality_score: 0.5,
|
||||
})
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// 没有找到JSON格式，创建默认响应
|
||||
Ok(GeminiClassificationResponse {
|
||||
category: "未分类".to_string(),
|
||||
confidence: 0.3,
|
||||
reasoning: format!("AI响应格式异常: {}", raw_response),
|
||||
features: vec!["格式异常".to_string()],
|
||||
product_match: false,
|
||||
quality_score: 0.3,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// 生成分类提示词
|
||||
async fn generate_classification_prompt(&self) -> Result<String> {
|
||||
// 获取激活的AI分类
|
||||
let classifications = self.ai_classification_repo.get_all(Some(
|
||||
crate::data::models::ai_classification::AiClassificationQuery {
|
||||
active_only: Some(true),
|
||||
sort_by: Some("sort_order".to_string()),
|
||||
sort_order: Some("ASC".to_string()),
|
||||
..Default::default()
|
||||
}
|
||||
)).await?;
|
||||
|
||||
if classifications.is_empty() {
|
||||
return Err(anyhow!("没有激活的AI分类，无法生成提示词"));
|
||||
}
|
||||
|
||||
Ok(generate_full_prompt(&classifications))
|
||||
}
|
||||
|
||||
/// 移动视频文件到分类文件夹
|
||||
async fn move_video_to_category_folder(&self, record: &VideoClassificationRecord) -> Result<()> {
|
||||
// 获取项目信息
|
||||
let project = self.material_repo.get_project_by_id(&record.project_id).await?
|
||||
.ok_or_else(|| anyhow!("项目不存在: {}", record.project_id))?;
|
||||
|
||||
// 获取片段信息
|
||||
let segment = self.material_repo.get_segment_by_id(&record.segment_id).await?
|
||||
.ok_or_else(|| anyhow!("片段不存在: {}", record.segment_id))?;
|
||||
|
||||
// 构建目标目录路径
|
||||
let category_dir = Path::new(&project.path)
|
||||
.join("assets")
|
||||
.join(&record.category);
|
||||
|
||||
// 创建分类目录
|
||||
FileSystemService::create_directory_if_not_exists(category_dir.to_str().unwrap())?;
|
||||
|
||||
// 构建目标文件路径
|
||||
let source_path = Path::new(&segment.file_path);
|
||||
let file_name = source_path.file_name()
|
||||
.ok_or_else(|| anyhow!("无法获取文件名"))?;
|
||||
let target_path = category_dir.join(file_name);
|
||||
|
||||
// 移动文件
|
||||
FileSystemService::move_file(&segment.file_path, target_path.to_str().unwrap())?;
|
||||
|
||||
println!("视频文件已移动到分类文件夹: {} -> {}", segment.file_path, target_path.display());
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 获取待处理的任务
|
||||
pub async fn get_pending_tasks(&self, limit: Option<i32>) -> Result<Vec<VideoClassificationTask>> {
|
||||
self.video_repo.get_pending_tasks(limit).await
|
||||
}
|
||||
|
||||
/// 获取分类统计信息
|
||||
pub async fn get_classification_stats(&self, project_id: Option<&str>) -> Result<ClassificationStats> {
|
||||
self.video_repo.get_classification_stats(project_id).await
|
||||
}
|
||||
|
||||
/// 根据素材ID获取分类记录
|
||||
pub async fn get_classifications_by_material(&self, material_id: &str) -> Result<Vec<VideoClassificationRecord>> {
|
||||
self.video_repo.get_by_material_id(material_id).await
|
||||
}
|
||||
|
||||
/// 获取任务详情
|
||||
async fn get_task_by_id(&self, task_id: &str) -> Result<VideoClassificationTask> {
|
||||
self.video_repo.get_task_by_id(task_id).await?
|
||||
.ok_or_else(|| anyhow!("任务不存在: {}", task_id))
|
||||
}
|
||||
|
||||
/// 取消分类任务
|
||||
pub async fn cancel_task(&self, task_id: &str) -> Result<()> {
|
||||
self.video_repo.delete_task(task_id).await
|
||||
}
|
||||
|
||||
/// 重试失败的任务
|
||||
pub async fn retry_failed_task(&self, task_id: &str) -> Result<()> {
|
||||
// 这里需要实现重试逻辑
|
||||
// 暂时返回错误，后续实现
|
||||
Err(anyhow!("重试任务功能待实现"))
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_parse_gemini_response() {
|
||||
let service = create_test_service();
|
||||
|
||||
let json_response = r#"
|
||||
这是一些前置文本
|
||||
{
|
||||
"category": "全身",
|
||||
"confidence": 0.85,
|
||||
"reasoning": "视频显示完整的人体轮廓",
|
||||
"features": ["全身可见", "清晰度高"],
|
||||
"product_match": true,
|
||||
"quality_score": 0.9
|
||||
}
|
||||
这是一些后置文本
|
||||
"#;
|
||||
|
||||
let result = service.parse_gemini_response(json_response).unwrap();
|
||||
assert_eq!(result.category, "全身");
|
||||
assert_eq!(result.confidence, 0.85);
|
||||
assert!(result.product_match);
|
||||
}
|
||||
|
||||
fn create_test_service() -> VideoClassificationService {
|
||||
// 这里需要创建测试用的service实例
|
||||
// 暂时使用空实现
|
||||
todo!("实现测试用的service创建")
|
||||
}
|
||||
}
|
||||
@@ -2,3 +2,4 @@ pub mod project;
|
||||
pub mod material;
|
||||
pub mod model;
|
||||
pub mod ai_classification;
|
||||
pub mod video_classification;
|
||||
|
||||
307
apps/desktop/src-tauri/src/data/models/video_classification.rs
Normal file
307
apps/desktop/src-tauri/src/data/models/video_classification.rs
Normal file
@@ -0,0 +1,307 @@
|
||||
use serde::{Deserialize, Serialize};
|
||||
use chrono::{DateTime, Utc};
|
||||
|
||||
/// AI视频分类记录数据模型
|
||||
/// 遵循 Tauri 开发规范的数据模型设计原则
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct VideoClassificationRecord {
|
||||
/// 分类记录唯一标识符
|
||||
pub id: String,
|
||||
/// 素材片段ID
|
||||
pub segment_id: String,
|
||||
/// 素材ID
|
||||
pub material_id: String,
|
||||
/// 项目ID
|
||||
pub project_id: String,
|
||||
/// 分类结果
|
||||
pub category: String,
|
||||
/// 置信度 (0.0 - 1.0)
|
||||
pub confidence: f64,
|
||||
/// 分类理由
|
||||
pub reasoning: String,
|
||||
/// 关键特征
|
||||
pub features: Vec<String>,
|
||||
/// 商品匹配度
|
||||
pub product_match: bool,
|
||||
/// 质量评分 (0.0 - 1.0)
|
||||
pub quality_score: f64,
|
||||
/// Gemini文件URI
|
||||
pub gemini_file_uri: Option<String>,
|
||||
/// 原始AI响应
|
||||
pub raw_response: Option<String>,
|
||||
/// 分类状态
|
||||
pub status: ClassificationStatus,
|
||||
/// 错误信息
|
||||
pub error_message: Option<String>,
|
||||
/// 创建时间
|
||||
pub created_at: DateTime<Utc>,
|
||||
/// 更新时间
|
||||
pub updated_at: DateTime<Utc>,
|
||||
}
|
||||
|
||||
/// AI视频分类任务数据模型
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct VideoClassificationTask {
|
||||
/// 任务唯一标识符
|
||||
pub id: String,
|
||||
/// 素材片段ID
|
||||
pub segment_id: String,
|
||||
/// 素材ID
|
||||
pub material_id: String,
|
||||
/// 项目ID
|
||||
pub project_id: String,
|
||||
/// 视频文件路径
|
||||
pub video_file_path: String,
|
||||
/// 任务状态
|
||||
pub status: TaskStatus,
|
||||
/// 任务优先级
|
||||
pub priority: i32,
|
||||
/// 重试次数
|
||||
pub retry_count: i32,
|
||||
/// 最大重试次数
|
||||
pub max_retries: i32,
|
||||
/// Gemini文件URI（上传后获得）
|
||||
pub gemini_file_uri: Option<String>,
|
||||
/// 使用的提示词
|
||||
pub prompt_text: Option<String>,
|
||||
/// 错误信息
|
||||
pub error_message: Option<String>,
|
||||
/// 开始处理时间
|
||||
pub started_at: Option<DateTime<Utc>>,
|
||||
/// 完成时间
|
||||
pub completed_at: Option<DateTime<Utc>>,
|
||||
/// 创建时间
|
||||
pub created_at: DateTime<Utc>,
|
||||
/// 更新时间
|
||||
pub updated_at: DateTime<Utc>,
|
||||
}
|
||||
|
||||
/// 分类状态枚举
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
|
||||
pub enum ClassificationStatus {
|
||||
/// 已分类
|
||||
Classified,
|
||||
/// 分类失败
|
||||
Failed,
|
||||
/// 需要人工审核
|
||||
NeedsReview,
|
||||
}
|
||||
|
||||
/// 任务状态枚举
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
|
||||
pub enum TaskStatus {
|
||||
/// 等待处理
|
||||
Pending,
|
||||
/// 上传中
|
||||
Uploading,
|
||||
/// 分析中
|
||||
Analyzing,
|
||||
/// 已完成
|
||||
Completed,
|
||||
/// 失败
|
||||
Failed,
|
||||
/// 已取消
|
||||
Cancelled,
|
||||
}
|
||||
|
||||
/// 批量分类请求
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct BatchClassificationRequest {
|
||||
/// 素材ID
|
||||
pub material_id: String,
|
||||
/// 项目ID
|
||||
pub project_id: String,
|
||||
/// 是否覆盖已有分类
|
||||
pub overwrite_existing: bool,
|
||||
/// 任务优先级
|
||||
pub priority: Option<i32>,
|
||||
}
|
||||
|
||||
/// 分类任务查询参数
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
|
||||
pub struct ClassificationTaskQuery {
|
||||
/// 项目ID过滤
|
||||
pub project_id: Option<String>,
|
||||
/// 素材ID过滤
|
||||
pub material_id: Option<String>,
|
||||
/// 状态过滤
|
||||
pub status: Option<TaskStatus>,
|
||||
/// 分页大小
|
||||
pub limit: Option<i32>,
|
||||
/// 分页偏移
|
||||
pub offset: Option<i32>,
|
||||
}
|
||||
|
||||
/// 分类统计信息
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct ClassificationStats {
|
||||
/// 总任务数
|
||||
pub total_tasks: i32,
|
||||
/// 等待中任务数
|
||||
pub pending_tasks: i32,
|
||||
/// 处理中任务数
|
||||
pub processing_tasks: i32,
|
||||
/// 已完成任务数
|
||||
pub completed_tasks: i32,
|
||||
/// 失败任务数
|
||||
pub failed_tasks: i32,
|
||||
/// 总分类记录数
|
||||
pub total_classifications: i32,
|
||||
/// 平均置信度
|
||||
pub average_confidence: f64,
|
||||
/// 平均质量评分
|
||||
pub average_quality_score: f64,
|
||||
}
|
||||
|
||||
/// Gemini API响应结构
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct GeminiClassificationResponse {
|
||||
/// 分类结果
|
||||
pub category: String,
|
||||
/// 置信度
|
||||
pub confidence: f64,
|
||||
/// 分类理由
|
||||
pub reasoning: String,
|
||||
/// 关键特征
|
||||
pub features: Vec<String>,
|
||||
/// 商品匹配度
|
||||
pub product_match: bool,
|
||||
/// 质量评分
|
||||
pub quality_score: f64,
|
||||
}
|
||||
|
||||
impl VideoClassificationRecord {
|
||||
/// 创建新的分类记录
|
||||
pub fn new(
|
||||
segment_id: String,
|
||||
material_id: String,
|
||||
project_id: String,
|
||||
gemini_response: GeminiClassificationResponse,
|
||||
gemini_file_uri: Option<String>,
|
||||
raw_response: Option<String>,
|
||||
) -> Self {
|
||||
Self {
|
||||
id: uuid::Uuid::new_v4().to_string(),
|
||||
segment_id,
|
||||
material_id,
|
||||
project_id,
|
||||
category: gemini_response.category,
|
||||
confidence: gemini_response.confidence,
|
||||
reasoning: gemini_response.reasoning,
|
||||
features: gemini_response.features,
|
||||
product_match: gemini_response.product_match,
|
||||
quality_score: gemini_response.quality_score,
|
||||
gemini_file_uri,
|
||||
raw_response,
|
||||
status: ClassificationStatus::Classified,
|
||||
error_message: None,
|
||||
created_at: Utc::now(),
|
||||
updated_at: Utc::now(),
|
||||
}
|
||||
}
|
||||
|
||||
/// 标记为失败
|
||||
pub fn mark_as_failed(&mut self, error_message: String) {
|
||||
self.status = ClassificationStatus::Failed;
|
||||
self.error_message = Some(error_message);
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 标记为需要审核
|
||||
pub fn mark_as_needs_review(&mut self, reason: String) {
|
||||
self.status = ClassificationStatus::NeedsReview;
|
||||
self.error_message = Some(reason);
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 是否需要人工审核（低置信度或低质量）
|
||||
pub fn needs_review(&self) -> bool {
|
||||
self.confidence < 0.7 || self.quality_score < 0.6
|
||||
}
|
||||
}
|
||||
|
||||
impl VideoClassificationTask {
|
||||
/// 创建新的分类任务
|
||||
pub fn new(
|
||||
segment_id: String,
|
||||
material_id: String,
|
||||
project_id: String,
|
||||
video_file_path: String,
|
||||
priority: Option<i32>,
|
||||
) -> Self {
|
||||
Self {
|
||||
id: uuid::Uuid::new_v4().to_string(),
|
||||
segment_id,
|
||||
material_id,
|
||||
project_id,
|
||||
video_file_path,
|
||||
status: TaskStatus::Pending,
|
||||
priority: priority.unwrap_or(0),
|
||||
retry_count: 0,
|
||||
max_retries: 3,
|
||||
gemini_file_uri: None,
|
||||
prompt_text: None,
|
||||
error_message: None,
|
||||
started_at: None,
|
||||
completed_at: None,
|
||||
created_at: Utc::now(),
|
||||
updated_at: Utc::now(),
|
||||
}
|
||||
}
|
||||
|
||||
/// 开始处理任务
|
||||
pub fn start_processing(&mut self) {
|
||||
self.status = TaskStatus::Uploading;
|
||||
self.started_at = Some(Utc::now());
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 设置为分析状态
|
||||
pub fn set_analyzing(&mut self, gemini_file_uri: String, prompt_text: String) {
|
||||
self.status = TaskStatus::Analyzing;
|
||||
self.gemini_file_uri = Some(gemini_file_uri);
|
||||
self.prompt_text = Some(prompt_text);
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 完成任务
|
||||
pub fn complete(&mut self) {
|
||||
self.status = TaskStatus::Completed;
|
||||
self.completed_at = Some(Utc::now());
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 任务失败
|
||||
pub fn fail(&mut self, error_message: String) {
|
||||
self.status = TaskStatus::Failed;
|
||||
self.error_message = Some(error_message);
|
||||
self.retry_count += 1;
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
|
||||
/// 是否可以重试
|
||||
pub fn can_retry(&self) -> bool {
|
||||
self.retry_count < self.max_retries && self.status == TaskStatus::Failed
|
||||
}
|
||||
|
||||
/// 重置为等待状态（用于重试）
|
||||
pub fn reset_for_retry(&mut self) {
|
||||
self.status = TaskStatus::Pending;
|
||||
self.error_message = None;
|
||||
self.started_at = None;
|
||||
self.completed_at = None;
|
||||
self.updated_at = Utc::now();
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for ClassificationStatus {
|
||||
fn default() -> Self {
|
||||
ClassificationStatus::Classified
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for TaskStatus {
|
||||
fn default() -> Self {
|
||||
TaskStatus::Pending
|
||||
}
|
||||
}
|
||||
@@ -450,4 +450,41 @@ impl MaterialRepository {
|
||||
|
||||
Ok(materials)
|
||||
}
|
||||
|
||||
/// 根据项目ID获取项目信息
|
||||
pub async fn get_project_by_id(&self, project_id: &str) -> Result<Option<crate::data::models::project::Project>> {
|
||||
// 这里需要引用项目仓库，暂时返回None
|
||||
// 在实际实现中，应该通过依赖注入获取项目仓库
|
||||
Ok(None)
|
||||
}
|
||||
|
||||
/// 根据片段ID获取片段信息
|
||||
pub async fn get_segment_by_id(&self, segment_id: &str) -> Result<Option<MaterialSegment>> {
|
||||
let conn = self.connection.lock().unwrap();
|
||||
|
||||
let mut stmt = conn.prepare(
|
||||
"SELECT id, material_id, segment_index, start_time, end_time, duration,
|
||||
file_path, file_size, created_at
|
||||
FROM material_segments WHERE id = ?1"
|
||||
)?;
|
||||
|
||||
let mut rows = stmt.query_map([segment_id], |row| {
|
||||
Ok(MaterialSegment {
|
||||
id: row.get(0)?,
|
||||
material_id: row.get(1)?,
|
||||
segment_index: row.get(2)?,
|
||||
start_time: row.get(3)?,
|
||||
end_time: row.get(4)?,
|
||||
duration: row.get(5)?,
|
||||
file_path: row.get(6)?,
|
||||
file_size: row.get(7)?,
|
||||
created_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(8)?).map_err(|_| rusqlite::Error::InvalidColumnType(8, "created_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&chrono::Utc),
|
||||
})
|
||||
})?;
|
||||
|
||||
match rows.next() {
|
||||
Some(row) => Ok(Some(row?)),
|
||||
None => Ok(None),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2,3 +2,4 @@ pub mod project_repository;
|
||||
pub mod material_repository;
|
||||
pub mod model_repository;
|
||||
pub mod ai_classification_repository;
|
||||
pub mod video_classification_repository;
|
||||
|
||||
@@ -0,0 +1,388 @@
|
||||
use crate::data::models::video_classification::*;
|
||||
use crate::infrastructure::database::Database;
|
||||
use anyhow::{Result, anyhow};
|
||||
use rusqlite::params;
|
||||
use std::sync::Arc;
|
||||
use uuid::Uuid;
|
||||
use chrono::Utc;
|
||||
|
||||
/// AI视频分类数据仓库
|
||||
/// 遵循 Tauri 开发规范的数据访问层设计模式
|
||||
pub struct VideoClassificationRepository {
|
||||
database: Arc<Database>,
|
||||
}
|
||||
|
||||
impl VideoClassificationRepository {
|
||||
/// 创建新的视频分类仓库实例
|
||||
pub fn new(database: Arc<Database>) -> Self {
|
||||
Self { database }
|
||||
}
|
||||
|
||||
/// 创建分类记录
|
||||
pub async fn create_classification_record(&self, record: VideoClassificationRecord) -> Result<VideoClassificationRecord> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
conn.execute(
|
||||
"INSERT INTO video_classification_records (
|
||||
id, segment_id, material_id, project_id, category, confidence, reasoning,
|
||||
features, product_match, quality_score, gemini_file_uri, raw_response,
|
||||
status, error_message, created_at, updated_at
|
||||
) VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16)",
|
||||
params![
|
||||
record.id,
|
||||
record.segment_id,
|
||||
record.material_id,
|
||||
record.project_id,
|
||||
record.category,
|
||||
record.confidence,
|
||||
record.reasoning,
|
||||
serde_json::to_string(&record.features)?,
|
||||
record.product_match,
|
||||
record.quality_score,
|
||||
record.gemini_file_uri,
|
||||
record.raw_response,
|
||||
serde_json::to_string(&record.status)?,
|
||||
record.error_message,
|
||||
record.created_at.to_rfc3339(),
|
||||
record.updated_at.to_rfc3339()
|
||||
],
|
||||
)?;
|
||||
|
||||
Ok(record)
|
||||
}
|
||||
|
||||
/// 根据片段ID获取分类记录
|
||||
pub async fn get_by_segment_id(&self, segment_id: &str) -> Result<Option<VideoClassificationRecord>> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let mut stmt = conn.prepare(
|
||||
"SELECT id, segment_id, material_id, project_id, category, confidence, reasoning,
|
||||
features, product_match, quality_score, gemini_file_uri, raw_response,
|
||||
status, error_message, created_at, updated_at
|
||||
FROM video_classification_records WHERE segment_id = ?1"
|
||||
)?;
|
||||
|
||||
let mut rows = stmt.query_map([segment_id], |row| {
|
||||
let features_json: String = row.get(7)?;
|
||||
let features: Vec<String> = serde_json::from_str(&features_json).unwrap_or_default();
|
||||
|
||||
let status_json: String = row.get(12)?;
|
||||
let status: ClassificationStatus = serde_json::from_str(&status_json).unwrap_or_default();
|
||||
|
||||
Ok(VideoClassificationRecord {
|
||||
id: row.get(0)?,
|
||||
segment_id: row.get(1)?,
|
||||
material_id: row.get(2)?,
|
||||
project_id: row.get(3)?,
|
||||
category: row.get(4)?,
|
||||
confidence: row.get(5)?,
|
||||
reasoning: row.get(6)?,
|
||||
features,
|
||||
product_match: row.get(8)?,
|
||||
quality_score: row.get(9)?,
|
||||
gemini_file_uri: row.get(10)?,
|
||||
raw_response: row.get(11)?,
|
||||
status,
|
||||
error_message: row.get(13)?,
|
||||
created_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(14)?).map_err(|e| rusqlite::Error::InvalidColumnType(14, "created_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
updated_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(15)?).map_err(|e| rusqlite::Error::InvalidColumnType(15, "updated_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
})
|
||||
})?;
|
||||
|
||||
match rows.next() {
|
||||
Some(row) => Ok(Some(row?)),
|
||||
None => Ok(None),
|
||||
}
|
||||
}
|
||||
|
||||
/// 根据素材ID获取所有分类记录
|
||||
pub async fn get_by_material_id(&self, material_id: &str) -> Result<Vec<VideoClassificationRecord>> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let mut stmt = conn.prepare(
|
||||
"SELECT id, segment_id, material_id, project_id, category, confidence, reasoning,
|
||||
features, product_match, quality_score, gemini_file_uri, raw_response,
|
||||
status, error_message, created_at, updated_at
|
||||
FROM video_classification_records WHERE material_id = ?1 ORDER BY created_at DESC"
|
||||
)?;
|
||||
|
||||
let rows = stmt.query_map([material_id], |row| {
|
||||
let features_json: String = row.get(7)?;
|
||||
let features: Vec<String> = serde_json::from_str(&features_json).unwrap_or_default();
|
||||
|
||||
let status_json: String = row.get(12)?;
|
||||
let status: ClassificationStatus = serde_json::from_str(&status_json).unwrap_or_default();
|
||||
|
||||
Ok(VideoClassificationRecord {
|
||||
id: row.get(0)?,
|
||||
segment_id: row.get(1)?,
|
||||
material_id: row.get(2)?,
|
||||
project_id: row.get(3)?,
|
||||
category: row.get(4)?,
|
||||
confidence: row.get(5)?,
|
||||
reasoning: row.get(6)?,
|
||||
features,
|
||||
product_match: row.get(8)?,
|
||||
quality_score: row.get(9)?,
|
||||
gemini_file_uri: row.get(10)?,
|
||||
raw_response: row.get(11)?,
|
||||
status,
|
||||
error_message: row.get(13)?,
|
||||
created_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(14)?).map_err(|e| rusqlite::Error::InvalidColumnType(14, "created_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
updated_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(15)?).map_err(|e| rusqlite::Error::InvalidColumnType(15, "updated_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
})
|
||||
})?;
|
||||
|
||||
let mut records = Vec::new();
|
||||
for row in rows {
|
||||
records.push(row?);
|
||||
}
|
||||
|
||||
Ok(records)
|
||||
}
|
||||
|
||||
/// 创建分类任务
|
||||
pub async fn create_classification_task(&self, task: VideoClassificationTask) -> Result<VideoClassificationTask> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
conn.execute(
|
||||
"INSERT INTO video_classification_tasks (
|
||||
id, segment_id, material_id, project_id, video_file_path, status, priority,
|
||||
retry_count, max_retries, gemini_file_uri, prompt_text, error_message,
|
||||
started_at, completed_at, created_at, updated_at
|
||||
) VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16)",
|
||||
params![
|
||||
task.id,
|
||||
task.segment_id,
|
||||
task.material_id,
|
||||
task.project_id,
|
||||
task.video_file_path,
|
||||
serde_json::to_string(&task.status)?,
|
||||
task.priority,
|
||||
task.retry_count,
|
||||
task.max_retries,
|
||||
task.gemini_file_uri,
|
||||
task.prompt_text,
|
||||
task.error_message,
|
||||
task.started_at.map(|dt| dt.to_rfc3339()),
|
||||
task.completed_at.map(|dt| dt.to_rfc3339()),
|
||||
task.created_at.to_rfc3339(),
|
||||
task.updated_at.to_rfc3339()
|
||||
],
|
||||
)?;
|
||||
|
||||
Ok(task)
|
||||
}
|
||||
|
||||
/// 更新分类任务
|
||||
pub async fn update_classification_task(&self, task: &VideoClassificationTask) -> Result<()> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
conn.execute(
|
||||
"UPDATE video_classification_tasks SET
|
||||
status = ?1, retry_count = ?2, gemini_file_uri = ?3, prompt_text = ?4,
|
||||
error_message = ?5, started_at = ?6, completed_at = ?7, updated_at = ?8
|
||||
WHERE id = ?9",
|
||||
params![
|
||||
serde_json::to_string(&task.status)?,
|
||||
task.retry_count,
|
||||
task.gemini_file_uri,
|
||||
task.prompt_text,
|
||||
task.error_message,
|
||||
task.started_at.map(|dt| dt.to_rfc3339()),
|
||||
task.completed_at.map(|dt| dt.to_rfc3339()),
|
||||
task.updated_at.to_rfc3339(),
|
||||
task.id
|
||||
],
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 获取待处理的任务（按优先级排序）
|
||||
pub async fn get_pending_tasks(&self, limit: Option<i32>) -> Result<Vec<VideoClassificationTask>> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let sql = if let Some(limit) = limit {
|
||||
format!(
|
||||
"SELECT id, segment_id, material_id, project_id, video_file_path, status, priority,
|
||||
retry_count, max_retries, gemini_file_uri, prompt_text, error_message,
|
||||
started_at, completed_at, created_at, updated_at
|
||||
FROM video_classification_tasks
|
||||
WHERE status = '\"Pending\"'
|
||||
ORDER BY priority DESC, created_at ASC
|
||||
LIMIT {}", limit
|
||||
)
|
||||
} else {
|
||||
"SELECT id, segment_id, material_id, project_id, video_file_path, status, priority,
|
||||
retry_count, max_retries, gemini_file_uri, prompt_text, error_message,
|
||||
started_at, completed_at, created_at, updated_at
|
||||
FROM video_classification_tasks
|
||||
WHERE status = '\"Pending\"'
|
||||
ORDER BY priority DESC, created_at ASC".to_string()
|
||||
};
|
||||
|
||||
let mut stmt = conn.prepare(&sql)?;
|
||||
let rows = stmt.query_map([], |row| {
|
||||
let status_json: String = row.get(5)?;
|
||||
let status: TaskStatus = serde_json::from_str(&status_json).unwrap_or_default();
|
||||
|
||||
Ok(VideoClassificationTask {
|
||||
id: row.get(0)?,
|
||||
segment_id: row.get(1)?,
|
||||
material_id: row.get(2)?,
|
||||
project_id: row.get(3)?,
|
||||
video_file_path: row.get(4)?,
|
||||
status,
|
||||
priority: row.get(6)?,
|
||||
retry_count: row.get(7)?,
|
||||
max_retries: row.get(8)?,
|
||||
gemini_file_uri: row.get(9)?,
|
||||
prompt_text: row.get(10)?,
|
||||
error_message: row.get(11)?,
|
||||
started_at: row.get::<_, Option<String>>(12)?.and_then(|s| chrono::DateTime::parse_from_rfc3339(&s).ok()).map(|dt| dt.with_timezone(&Utc)),
|
||||
completed_at: row.get::<_, Option<String>>(13)?.and_then(|s| chrono::DateTime::parse_from_rfc3339(&s).ok()).map(|dt| dt.with_timezone(&Utc)),
|
||||
created_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(14)?).map_err(|e| rusqlite::Error::InvalidColumnType(14, "created_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
updated_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(15)?).map_err(|e| rusqlite::Error::InvalidColumnType(15, "updated_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
})
|
||||
})?;
|
||||
|
||||
let mut tasks = Vec::new();
|
||||
for row in rows {
|
||||
tasks.push(row?);
|
||||
}
|
||||
|
||||
Ok(tasks)
|
||||
}
|
||||
|
||||
/// 获取分类统计信息
|
||||
pub async fn get_classification_stats(&self, project_id: Option<&str>) -> Result<ClassificationStats> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let (task_where_clause, record_where_clause) = if let Some(project_id) = project_id {
|
||||
(format!(" WHERE project_id = '{}'", project_id), format!(" WHERE project_id = '{}'", project_id))
|
||||
} else {
|
||||
("".to_string(), "".to_string())
|
||||
};
|
||||
|
||||
// 获取任务统计
|
||||
let mut stmt = conn.prepare(&format!(
|
||||
"SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN status = '\"Pending\"' THEN 1 ELSE 0 END) as pending,
|
||||
SUM(CASE WHEN status IN ('\"Uploading\"', '\"Analyzing\"') THEN 1 ELSE 0 END) as processing,
|
||||
SUM(CASE WHEN status = '\"Completed\"' THEN 1 ELSE 0 END) as completed,
|
||||
SUM(CASE WHEN status = '\"Failed\"' THEN 1 ELSE 0 END) as failed
|
||||
FROM video_classification_tasks{}", task_where_clause
|
||||
))?;
|
||||
|
||||
let task_stats = stmt.query_row([], |row| {
|
||||
Ok((
|
||||
row.get::<_, i32>(0)?,
|
||||
row.get::<_, i32>(1)?,
|
||||
row.get::<_, i32>(2)?,
|
||||
row.get::<_, i32>(3)?,
|
||||
row.get::<_, i32>(4)?,
|
||||
))
|
||||
})?;
|
||||
|
||||
// 获取分类记录统计
|
||||
let mut stmt = conn.prepare(&format!(
|
||||
"SELECT
|
||||
COUNT(*) as total,
|
||||
AVG(confidence) as avg_confidence,
|
||||
AVG(quality_score) as avg_quality
|
||||
FROM video_classification_records{}", record_where_clause
|
||||
))?;
|
||||
|
||||
let record_stats = stmt.query_row([], |row| {
|
||||
Ok((
|
||||
row.get::<_, i32>(0)?,
|
||||
row.get::<_, Option<f64>>(1)?.unwrap_or(0.0),
|
||||
row.get::<_, Option<f64>>(2)?.unwrap_or(0.0),
|
||||
))
|
||||
})?;
|
||||
|
||||
Ok(ClassificationStats {
|
||||
total_tasks: task_stats.0,
|
||||
pending_tasks: task_stats.1,
|
||||
processing_tasks: task_stats.2,
|
||||
completed_tasks: task_stats.3,
|
||||
failed_tasks: task_stats.4,
|
||||
total_classifications: record_stats.0,
|
||||
average_confidence: record_stats.1,
|
||||
average_quality_score: record_stats.2,
|
||||
})
|
||||
}
|
||||
|
||||
/// 检查片段是否已分类
|
||||
pub async fn is_segment_classified(&self, segment_id: &str) -> Result<bool> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let mut stmt = conn.prepare(
|
||||
"SELECT COUNT(*) FROM video_classification_records WHERE segment_id = ?1"
|
||||
)?;
|
||||
|
||||
let count: i32 = stmt.query_row([segment_id], |row| row.get(0))?;
|
||||
Ok(count > 0)
|
||||
}
|
||||
|
||||
/// 删除分类任务
|
||||
pub async fn delete_task(&self, task_id: &str) -> Result<()> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
conn.execute("DELETE FROM video_classification_tasks WHERE id = ?1", [task_id])?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 根据ID获取分类任务
|
||||
pub async fn get_task_by_id(&self, task_id: &str) -> Result<Option<VideoClassificationTask>> {
|
||||
let conn = self.database.get_connection();
|
||||
let conn = conn.lock().unwrap();
|
||||
|
||||
let mut stmt = conn.prepare(
|
||||
"SELECT id, segment_id, material_id, project_id, video_file_path, status, priority,
|
||||
retry_count, max_retries, gemini_file_uri, prompt_text, error_message,
|
||||
started_at, completed_at, created_at, updated_at
|
||||
FROM video_classification_tasks WHERE id = ?1"
|
||||
)?;
|
||||
|
||||
let mut rows = stmt.query_map([task_id], |row| {
|
||||
let status_json: String = row.get(5)?;
|
||||
let status: TaskStatus = serde_json::from_str(&status_json).unwrap_or_default();
|
||||
|
||||
Ok(VideoClassificationTask {
|
||||
id: row.get(0)?,
|
||||
segment_id: row.get(1)?,
|
||||
material_id: row.get(2)?,
|
||||
project_id: row.get(3)?,
|
||||
video_file_path: row.get(4)?,
|
||||
status,
|
||||
priority: row.get(6)?,
|
||||
retry_count: row.get(7)?,
|
||||
max_retries: row.get(8)?,
|
||||
gemini_file_uri: row.get(9)?,
|
||||
prompt_text: row.get(10)?,
|
||||
error_message: row.get(11)?,
|
||||
started_at: row.get::<_, Option<String>>(12)?.and_then(|s| chrono::DateTime::parse_from_rfc3339(&s).ok()).map(|dt| dt.with_timezone(&Utc)),
|
||||
completed_at: row.get::<_, Option<String>>(13)?.and_then(|s| chrono::DateTime::parse_from_rfc3339(&s).ok()).map(|dt| dt.with_timezone(&Utc)),
|
||||
created_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(14)?).map_err(|e| rusqlite::Error::InvalidColumnType(14, "created_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
updated_at: chrono::DateTime::parse_from_rfc3339(&row.get::<_, String>(15)?).map_err(|e| rusqlite::Error::InvalidColumnType(15, "updated_at".to_string(), rusqlite::types::Type::Text))?.with_timezone(&Utc),
|
||||
})
|
||||
})?;
|
||||
|
||||
match rows.next() {
|
||||
Some(row) => Ok(Some(row?)),
|
||||
None => Ok(None),
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -207,6 +207,58 @@ impl Database {
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建视频分类记录表
|
||||
conn.execute(
|
||||
"CREATE TABLE IF NOT EXISTS video_classification_records (
|
||||
id TEXT PRIMARY KEY,
|
||||
segment_id TEXT NOT NULL,
|
||||
material_id TEXT NOT NULL,
|
||||
project_id TEXT NOT NULL,
|
||||
category TEXT NOT NULL,
|
||||
confidence REAL NOT NULL,
|
||||
reasoning TEXT NOT NULL,
|
||||
features TEXT NOT NULL,
|
||||
product_match INTEGER NOT NULL,
|
||||
quality_score REAL NOT NULL,
|
||||
gemini_file_uri TEXT,
|
||||
raw_response TEXT,
|
||||
status TEXT NOT NULL,
|
||||
error_message TEXT,
|
||||
created_at DATETIME NOT NULL,
|
||||
updated_at DATETIME NOT NULL,
|
||||
FOREIGN KEY (segment_id) REFERENCES material_segments (id) ON DELETE CASCADE,
|
||||
FOREIGN KEY (material_id) REFERENCES materials (id) ON DELETE CASCADE,
|
||||
FOREIGN KEY (project_id) REFERENCES projects (id) ON DELETE CASCADE
|
||||
)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建视频分类任务表
|
||||
conn.execute(
|
||||
"CREATE TABLE IF NOT EXISTS video_classification_tasks (
|
||||
id TEXT PRIMARY KEY,
|
||||
segment_id TEXT NOT NULL,
|
||||
material_id TEXT NOT NULL,
|
||||
project_id TEXT NOT NULL,
|
||||
video_file_path TEXT NOT NULL,
|
||||
status TEXT NOT NULL,
|
||||
priority INTEGER DEFAULT 0,
|
||||
retry_count INTEGER DEFAULT 0,
|
||||
max_retries INTEGER DEFAULT 3,
|
||||
gemini_file_uri TEXT,
|
||||
prompt_text TEXT,
|
||||
error_message TEXT,
|
||||
started_at DATETIME,
|
||||
completed_at DATETIME,
|
||||
created_at DATETIME NOT NULL,
|
||||
updated_at DATETIME NOT NULL,
|
||||
FOREIGN KEY (segment_id) REFERENCES material_segments (id) ON DELETE CASCADE,
|
||||
FOREIGN KEY (material_id) REFERENCES materials (id) ON DELETE CASCADE,
|
||||
FOREIGN KEY (project_id) REFERENCES projects (id) ON DELETE CASCADE
|
||||
)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建性能监控表
|
||||
conn.execute(
|
||||
"CREATE TABLE IF NOT EXISTS performance_metrics (
|
||||
@@ -298,6 +350,53 @@ impl Database {
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建视频分类记录表索引
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_records_segment_id ON video_classification_records (segment_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_records_material_id ON video_classification_records (material_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_records_project_id ON video_classification_records (project_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_records_category ON video_classification_records (category)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建视频分类任务表索引
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_tasks_segment_id ON video_classification_tasks (segment_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_tasks_material_id ON video_classification_tasks (material_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_tasks_project_id ON video_classification_tasks (project_id)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_tasks_status ON video_classification_tasks (status)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_video_classification_tasks_priority ON video_classification_tasks (priority)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
// 创建AI分类表索引
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_ai_classifications_name ON ai_classifications (name)",
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
use std::path::Path;
|
||||
use anyhow::Result;
|
||||
use anyhow::{Result, anyhow};
|
||||
|
||||
/// 文件系统操作工具
|
||||
/// 遵循 Tauri 开发规范的文件系统安全操作
|
||||
@@ -87,6 +87,66 @@ impl FileSystemService {
|
||||
let project_file = Path::new(path).join("mixvideo.project.json");
|
||||
project_file.exists()
|
||||
}
|
||||
|
||||
/// 创建目录（如果不存在）
|
||||
pub fn create_directory_if_not_exists(path: &str) -> Result<()> {
|
||||
let dir_path = Path::new(path);
|
||||
if !dir_path.exists() {
|
||||
std::fs::create_dir_all(dir_path)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 移动文件
|
||||
pub fn move_file(source: &str, destination: &str) -> Result<()> {
|
||||
let source_path = Path::new(source);
|
||||
let dest_path = Path::new(destination);
|
||||
|
||||
// 确保源文件存在
|
||||
if !source_path.exists() {
|
||||
return Err(anyhow!("源文件不存在: {}", source));
|
||||
}
|
||||
|
||||
// 确保目标目录存在
|
||||
if let Some(parent) = dest_path.parent() {
|
||||
std::fs::create_dir_all(parent)?;
|
||||
}
|
||||
|
||||
// 移动文件
|
||||
std::fs::rename(source_path, dest_path)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 复制文件
|
||||
pub fn copy_file(source: &str, destination: &str) -> Result<()> {
|
||||
let source_path = Path::new(source);
|
||||
let dest_path = Path::new(destination);
|
||||
|
||||
// 确保源文件存在
|
||||
if !source_path.exists() {
|
||||
return Err(anyhow!("源文件不存在: {}", source));
|
||||
}
|
||||
|
||||
// 确保目标目录存在
|
||||
if let Some(parent) = dest_path.parent() {
|
||||
std::fs::create_dir_all(parent)?;
|
||||
}
|
||||
|
||||
// 复制文件
|
||||
std::fs::copy(source_path, dest_path)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 获取文件大小
|
||||
pub fn get_file_size(path: &str) -> Result<u64> {
|
||||
let metadata = std::fs::metadata(path)?;
|
||||
Ok(metadata.len())
|
||||
}
|
||||
|
||||
/// 检查文件是否存在
|
||||
pub fn file_exists(path: &str) -> bool {
|
||||
Path::new(path).exists()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
|
||||
312
apps/desktop/src-tauri/src/infrastructure/gemini_service.rs
Normal file
312
apps/desktop/src-tauri/src/infrastructure/gemini_service.rs
Normal file
@@ -0,0 +1,312 @@
|
||||
use anyhow::{Result, anyhow};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::collections::HashMap;
|
||||
use std::path::Path;
|
||||
use std::time::{SystemTime, UNIX_EPOCH};
|
||||
use tokio::fs;
|
||||
use reqwest::multipart;
|
||||
|
||||
/// Gemini API配置
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GeminiConfig {
|
||||
pub base_url: String,
|
||||
pub bearer_token: String,
|
||||
pub timeout: u64,
|
||||
}
|
||||
|
||||
impl Default for GeminiConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
base_url: "https://bowongai-dev--bowong-ai-video-gemini-fastapi-webapp.modal.run".to_string(),
|
||||
bearer_token: "bowong7777".to_string(),
|
||||
timeout: 120,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Gemini访问令牌响应
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct TokenResponse {
|
||||
access_token: String,
|
||||
expires_in: u64,
|
||||
}
|
||||
|
||||
/// Gemini上传响应
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct UploadResponse {
|
||||
file_uri: String,
|
||||
}
|
||||
|
||||
/// Gemini内容生成请求
|
||||
#[derive(Debug, Serialize)]
|
||||
struct GenerateContentRequest {
|
||||
contents: Vec<ContentPart>,
|
||||
#[serde(rename = "generationConfig")]
|
||||
generation_config: GenerationConfig,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
struct ContentPart {
|
||||
role: String,
|
||||
parts: Vec<Part>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
#[serde(untagged)]
|
||||
enum Part {
|
||||
Text { text: String },
|
||||
FileData {
|
||||
#[serde(rename = "fileData")]
|
||||
file_data: FileData
|
||||
},
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
struct FileData {
|
||||
#[serde(rename = "mimeType")]
|
||||
mime_type: String,
|
||||
#[serde(rename = "fileUri")]
|
||||
file_uri: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
struct GenerationConfig {
|
||||
temperature: f32,
|
||||
#[serde(rename = "topK")]
|
||||
top_k: u32,
|
||||
#[serde(rename = "topP")]
|
||||
top_p: f32,
|
||||
#[serde(rename = "maxOutputTokens")]
|
||||
max_output_tokens: u32,
|
||||
}
|
||||
|
||||
/// Gemini响应结构
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct GeminiResponse {
|
||||
candidates: Vec<Candidate>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct Candidate {
|
||||
content: Content,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct Content {
|
||||
parts: Vec<ResponsePart>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct ResponsePart {
|
||||
text: String,
|
||||
}
|
||||
|
||||
/// Gemini API服务
|
||||
/// 遵循 Tauri 开发规范的基础设施层设计模式
|
||||
pub struct GeminiService {
|
||||
config: GeminiConfig,
|
||||
client: reqwest::Client,
|
||||
access_token: Option<String>,
|
||||
token_expires_at: Option<u64>,
|
||||
}
|
||||
|
||||
impl GeminiService {
|
||||
/// 创建新的Gemini服务实例
|
||||
pub fn new(config: Option<GeminiConfig>) -> Self {
|
||||
let client = reqwest::Client::builder()
|
||||
.timeout(std::time::Duration::from_secs(config.as_ref().map(|c| c.timeout).unwrap_or(120)))
|
||||
.build()
|
||||
.expect("Failed to create HTTP client");
|
||||
|
||||
Self {
|
||||
config: config.unwrap_or_default(),
|
||||
client,
|
||||
access_token: None,
|
||||
token_expires_at: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// 获取Google访问令牌
|
||||
async fn get_access_token(&mut self) -> Result<String> {
|
||||
// 检查缓存的令牌是否仍然有效
|
||||
let current_time = SystemTime::now()
|
||||
.duration_since(UNIX_EPOCH)?
|
||||
.as_secs();
|
||||
|
||||
if let (Some(token), Some(expires_at)) = (&self.access_token, self.token_expires_at) {
|
||||
if current_time < expires_at - 300 { // 提前5分钟刷新
|
||||
return Ok(token.clone());
|
||||
}
|
||||
}
|
||||
|
||||
// 获取新的访问令牌
|
||||
let url = format!("{}/google/auth/token", self.config.base_url);
|
||||
let response = self.client
|
||||
.post(&url)
|
||||
.header("Authorization", format!("Bearer {}", self.config.bearer_token))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
return Err(anyhow!("获取访问令牌失败: {}", response.status()));
|
||||
}
|
||||
|
||||
let token_response: TokenResponse = response.json().await?;
|
||||
|
||||
// 缓存令牌
|
||||
self.access_token = Some(token_response.access_token.clone());
|
||||
self.token_expires_at = Some(current_time + token_response.expires_in);
|
||||
|
||||
Ok(token_response.access_token)
|
||||
}
|
||||
|
||||
/// 上传视频文件到Gemini
|
||||
pub async fn upload_video_file(&mut self, video_path: &str) -> Result<String> {
|
||||
// 获取访问令牌
|
||||
let access_token = self.get_access_token().await?;
|
||||
|
||||
// 读取视频文件
|
||||
let video_data = fs::read(video_path).await?;
|
||||
let file_name = Path::new(video_path)
|
||||
.file_name()
|
||||
.and_then(|n| n.to_str())
|
||||
.unwrap_or("video.mp4");
|
||||
|
||||
// 创建multipart表单
|
||||
let form = multipart::Form::new()
|
||||
.part("file", multipart::Part::bytes(video_data)
|
||||
.file_name(file_name.to_string())
|
||||
.mime_str("video/mp4")?);
|
||||
|
||||
// 上传到Vertex AI
|
||||
let upload_url = format!("{}/google/vertex-ai/upload", self.config.base_url);
|
||||
let response = self.client
|
||||
.post(&upload_url)
|
||||
.header("Authorization", format!("Bearer {}", access_token))
|
||||
.header("x-google-api-key", &access_token)
|
||||
.query(&[
|
||||
("bucket", "dy-media-storage"),
|
||||
("prefix", "video-analysis")
|
||||
])
|
||||
.multipart(form)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
let status = response.status();
|
||||
let error_text = response.text().await.unwrap_or_default();
|
||||
return Err(anyhow!("上传视频失败: {} - {}", status, error_text));
|
||||
}
|
||||
|
||||
let upload_response: UploadResponse = response.json().await?;
|
||||
Ok(upload_response.file_uri)
|
||||
}
|
||||
|
||||
/// 生成内容分析
|
||||
pub async fn generate_content_analysis(&mut self, file_uri: &str, prompt: &str) -> Result<String> {
|
||||
// 获取访问令牌
|
||||
let access_token = self.get_access_token().await?;
|
||||
|
||||
// 格式化GCS URI
|
||||
let formatted_uri = self.format_gcs_uri(file_uri);
|
||||
|
||||
// 准备请求数据
|
||||
let request_data = GenerateContentRequest {
|
||||
contents: vec![ContentPart {
|
||||
role: "user".to_string(),
|
||||
parts: vec![
|
||||
Part::Text { text: prompt.to_string() },
|
||||
Part::FileData {
|
||||
file_data: FileData {
|
||||
mime_type: "video/mp4".to_string(),
|
||||
file_uri: formatted_uri,
|
||||
}
|
||||
}
|
||||
],
|
||||
}],
|
||||
generation_config: GenerationConfig {
|
||||
temperature: 0.1,
|
||||
top_k: 32,
|
||||
top_p: 1.0,
|
||||
max_output_tokens: 2048,
|
||||
},
|
||||
};
|
||||
|
||||
// 发送生成请求
|
||||
let generate_url = format!("{}/google/vertex-ai/generate", self.config.base_url);
|
||||
let response = self.client
|
||||
.post(&generate_url)
|
||||
.header("Authorization", format!("Bearer {}", access_token))
|
||||
.header("x-google-api-key", &access_token)
|
||||
.header("Content-Type", "application/json")
|
||||
.json(&request_data)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
let status = response.status();
|
||||
let error_text = response.text().await.unwrap_or_default();
|
||||
return Err(anyhow!("生成内容分析失败: {} - {}", status, error_text));
|
||||
}
|
||||
|
||||
let gemini_response: GeminiResponse = response.json().await?;
|
||||
|
||||
if let Some(candidate) = gemini_response.candidates.first() {
|
||||
if let Some(part) = candidate.content.parts.first() {
|
||||
return Ok(part.text.clone());
|
||||
}
|
||||
}
|
||||
|
||||
Err(anyhow!("Gemini响应格式无效"))
|
||||
}
|
||||
|
||||
/// 格式化GCS URI
|
||||
fn format_gcs_uri(&self, file_uri: &str) -> String {
|
||||
if file_uri.starts_with("gs://") {
|
||||
file_uri.to_string()
|
||||
} else if file_uri.starts_with("https://storage.googleapis.com/") {
|
||||
// 转换为gs://格式
|
||||
file_uri.replace("https://storage.googleapis.com/", "gs://")
|
||||
} else {
|
||||
// 假设是相对路径，添加默认bucket
|
||||
format!("gs://dy-media-storage/video-analysis/{}", file_uri)
|
||||
}
|
||||
}
|
||||
|
||||
/// 完整的视频分类流程
|
||||
pub async fn classify_video(&mut self, video_path: &str, prompt: &str) -> Result<(String, String)> {
|
||||
// 1. 上传视频
|
||||
println!("正在上传视频到Gemini: {}", video_path);
|
||||
let file_uri = self.upload_video_file(video_path).await?;
|
||||
println!("视频上传成功，URI: {}", file_uri);
|
||||
|
||||
// 2. 生成分析
|
||||
println!("正在进行AI分析...");
|
||||
let analysis_result = self.generate_content_analysis(&file_uri, prompt).await?;
|
||||
println!("AI分析完成");
|
||||
|
||||
Ok((file_uri, analysis_result))
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_format_gcs_uri() {
|
||||
let service = GeminiService::new(None);
|
||||
|
||||
// 测试已经是gs://格式的URI
|
||||
let gs_uri = "gs://bucket/path/file.mp4";
|
||||
assert_eq!(service.format_gcs_uri(gs_uri), gs_uri);
|
||||
|
||||
// 测试https://storage.googleapis.com/格式的URI
|
||||
let https_uri = "https://storage.googleapis.com/bucket/path/file.mp4";
|
||||
assert_eq!(service.format_gcs_uri(https_uri), "gs://bucket/path/file.mp4");
|
||||
|
||||
// 测试相对路径
|
||||
let relative_path = "path/file.mp4";
|
||||
assert_eq!(service.format_gcs_uri(relative_path), "gs://dy-media-storage/video-analysis/path/file.mp4");
|
||||
}
|
||||
}
|
||||
@@ -7,3 +7,4 @@ pub mod event_bus;
|
||||
pub mod ffmpeg;
|
||||
pub mod monitoring;
|
||||
pub mod logging;
|
||||
pub mod gemini_service;
|
||||
|
||||
@@ -103,7 +103,21 @@ pub fn run() {
|
||||
commands::ai_classification_commands::generate_ai_classification_preview,
|
||||
commands::ai_classification_commands::update_ai_classification_sort_orders,
|
||||
commands::ai_classification_commands::toggle_ai_classification_status,
|
||||
commands::ai_classification_commands::validate_ai_classification_name
|
||||
commands::ai_classification_commands::validate_ai_classification_name,
|
||||
// AI视频分类命令
|
||||
commands::video_classification_commands::start_video_classification,
|
||||
commands::video_classification_commands::get_classification_queue_status,
|
||||
commands::video_classification_commands::get_classification_task_progress,
|
||||
commands::video_classification_commands::get_all_classification_task_progress,
|
||||
commands::video_classification_commands::stop_classification_queue,
|
||||
commands::video_classification_commands::pause_classification_queue,
|
||||
commands::video_classification_commands::resume_classification_queue,
|
||||
commands::video_classification_commands::get_material_classification_records,
|
||||
commands::video_classification_commands::get_classification_statistics,
|
||||
commands::video_classification_commands::is_segment_classified,
|
||||
commands::video_classification_commands::cancel_classification_task,
|
||||
commands::video_classification_commands::retry_classification_task,
|
||||
commands::video_classification_commands::test_gemini_connection
|
||||
])
|
||||
.setup(|app| {
|
||||
// 初始化日志系统
|
||||
|
||||
@@ -3,3 +3,4 @@ pub mod system_commands;
|
||||
pub mod material_commands;
|
||||
pub mod model_commands;
|
||||
pub mod ai_classification_commands;
|
||||
pub mod video_classification_commands;
|
||||
|
||||
@@ -0,0 +1,263 @@
|
||||
use crate::app_state::AppState;
|
||||
use crate::data::models::video_classification::*;
|
||||
use crate::data::repositories::video_classification_repository::VideoClassificationRepository;
|
||||
use crate::data::repositories::ai_classification_repository::AiClassificationRepository;
|
||||
use crate::data::repositories::material_repository::MaterialRepository;
|
||||
use crate::business::services::video_classification_service::VideoClassificationService;
|
||||
use crate::business::services::video_classification_queue::{VideoClassificationQueue, QueueStats, TaskProgress};
|
||||
use crate::infrastructure::gemini_service::GeminiConfig;
|
||||
use anyhow::Result;
|
||||
use std::sync::Arc;
|
||||
use tauri::{command, State};
|
||||
use tokio::sync::Mutex;
|
||||
use std::collections::HashMap;
|
||||
|
||||
/// 全局队列实例
|
||||
static QUEUE_INSTANCE: tokio::sync::OnceCell<Arc<VideoClassificationQueue>> = tokio::sync::OnceCell::const_new();
|
||||
|
||||
/// 获取或创建队列实例
|
||||
async fn get_queue_instance(state: &AppState) -> Arc<VideoClassificationQueue> {
|
||||
QUEUE_INSTANCE.get_or_init(|| async {
|
||||
let database = state.get_database();
|
||||
|
||||
let video_repo = Arc::new(VideoClassificationRepository::new(database.clone()));
|
||||
let ai_classification_repo = Arc::new(AiClassificationRepository::new(database.clone()));
|
||||
let material_repo = Arc::new(MaterialRepository::new(state.get_database().get_connection()).unwrap());
|
||||
|
||||
let gemini_config = Some(GeminiConfig::default());
|
||||
let service = Arc::new(VideoClassificationService::new(
|
||||
video_repo,
|
||||
ai_classification_repo,
|
||||
material_repo,
|
||||
gemini_config,
|
||||
));
|
||||
|
||||
Arc::new(VideoClassificationQueue::new(service))
|
||||
}).await.clone()
|
||||
}
|
||||
|
||||
/// 启动AI视频分类
|
||||
/// 遵循 Tauri 开发规范的命令接口设计
|
||||
#[command]
|
||||
pub async fn start_video_classification(
|
||||
request: BatchClassificationRequest,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<Vec<String>, String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
|
||||
// 添加任务到队列
|
||||
let task_ids = queue.add_batch_tasks(request)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
// 启动队列（如果尚未启动）
|
||||
if let Err(e) = queue.start().await {
|
||||
// 如果队列已经在运行，忽略错误
|
||||
if !e.to_string().contains("已经在运行中") {
|
||||
return Err(e.to_string());
|
||||
}
|
||||
}
|
||||
|
||||
Ok(task_ids)
|
||||
}
|
||||
|
||||
/// 获取分类队列状态
|
||||
#[command]
|
||||
pub async fn get_classification_queue_status(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<QueueStats, String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
Ok(queue.get_stats().await)
|
||||
}
|
||||
|
||||
/// 获取任务进度
|
||||
#[command]
|
||||
pub async fn get_classification_task_progress(
|
||||
task_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<Option<TaskProgress>, String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
Ok(queue.get_task_progress(&task_id).await)
|
||||
}
|
||||
|
||||
/// 获取所有任务进度
|
||||
#[command]
|
||||
pub async fn get_all_classification_task_progress(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<HashMap<String, TaskProgress>, String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
Ok(queue.get_all_task_progress().await)
|
||||
}
|
||||
|
||||
/// 停止分类队列
|
||||
#[command]
|
||||
pub async fn stop_classification_queue(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<(), String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
queue.stop().await.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 暂停分类队列
|
||||
#[command]
|
||||
pub async fn pause_classification_queue(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<(), String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
queue.pause().await.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 恢复分类队列
|
||||
#[command]
|
||||
pub async fn resume_classification_queue(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<(), String> {
|
||||
let queue = get_queue_instance(&state).await;
|
||||
queue.resume().await.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 获取素材的分类记录
|
||||
#[command]
|
||||
pub async fn get_material_classification_records(
|
||||
material_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<Vec<VideoClassificationRecord>, String> {
|
||||
let database = state.get_database();
|
||||
let video_repo = Arc::new(VideoClassificationRepository::new(database.clone()));
|
||||
let ai_classification_repo = Arc::new(AiClassificationRepository::new(database.clone()));
|
||||
let material_repo = Arc::new(MaterialRepository::new(database.get_connection()).unwrap());
|
||||
|
||||
let service = VideoClassificationService::new(
|
||||
video_repo,
|
||||
ai_classification_repo,
|
||||
material_repo,
|
||||
Some(GeminiConfig::default()),
|
||||
);
|
||||
|
||||
service.get_classifications_by_material(&material_id)
|
||||
.await
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 获取分类统计信息
|
||||
#[command]
|
||||
pub async fn get_classification_statistics(
|
||||
project_id: Option<String>,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<ClassificationStats, String> {
|
||||
let database = state.get_database();
|
||||
let video_repo = Arc::new(VideoClassificationRepository::new(database.clone()));
|
||||
let ai_classification_repo = Arc::new(AiClassificationRepository::new(database.clone()));
|
||||
let material_repo = Arc::new(MaterialRepository::new(database.get_connection()).unwrap());
|
||||
|
||||
let service = VideoClassificationService::new(
|
||||
video_repo,
|
||||
ai_classification_repo,
|
||||
material_repo,
|
||||
Some(GeminiConfig::default()),
|
||||
);
|
||||
|
||||
service.get_classification_stats(project_id.as_deref())
|
||||
.await
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 检查片段是否已分类
|
||||
#[command]
|
||||
pub async fn is_segment_classified(
|
||||
segment_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<bool, String> {
|
||||
let database = state.get_database();
|
||||
let video_repo = VideoClassificationRepository::new(database);
|
||||
|
||||
video_repo.is_segment_classified(&segment_id)
|
||||
.await
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 取消分类任务
|
||||
#[command]
|
||||
pub async fn cancel_classification_task(
|
||||
task_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<(), String> {
|
||||
let database = state.get_database();
|
||||
let video_repo = Arc::new(VideoClassificationRepository::new(database.clone()));
|
||||
let ai_classification_repo = Arc::new(AiClassificationRepository::new(database.clone()));
|
||||
let material_repo = Arc::new(MaterialRepository::new(database.get_connection()).unwrap());
|
||||
|
||||
let service = VideoClassificationService::new(
|
||||
video_repo,
|
||||
ai_classification_repo,
|
||||
material_repo,
|
||||
Some(GeminiConfig::default()),
|
||||
);
|
||||
|
||||
service.cancel_task(&task_id)
|
||||
.await
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 重试失败的分类任务
|
||||
#[command]
|
||||
pub async fn retry_classification_task(
|
||||
task_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<(), String> {
|
||||
let database = state.get_database();
|
||||
let video_repo = Arc::new(VideoClassificationRepository::new(database.clone()));
|
||||
let ai_classification_repo = Arc::new(AiClassificationRepository::new(database.clone()));
|
||||
let material_repo = Arc::new(MaterialRepository::new(database.get_connection()).unwrap());
|
||||
|
||||
let service = VideoClassificationService::new(
|
||||
video_repo,
|
||||
ai_classification_repo,
|
||||
material_repo,
|
||||
Some(GeminiConfig::default()),
|
||||
);
|
||||
|
||||
service.retry_failed_task(&task_id)
|
||||
.await
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
|
||||
/// 测试Gemini连接
|
||||
#[command]
|
||||
pub async fn test_gemini_connection() -> Result<String, String> {
|
||||
use crate::infrastructure::gemini_service::GeminiService;
|
||||
|
||||
let mut service = GeminiService::new(Some(GeminiConfig::default()));
|
||||
|
||||
// 尝试获取访问令牌来测试连接
|
||||
// 注意：这里需要实现一个公开的测试方法
|
||||
Ok("Gemini连接测试功能待实现".to_string())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::infrastructure::database::Database;
|
||||
|
||||
async fn create_test_state() -> AppState {
|
||||
let database = Arc::new(Database::new_in_memory().unwrap());
|
||||
AppState::new_with_database(database)
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_get_queue_instance() {
|
||||
let state = create_test_state().await;
|
||||
let queue1 = get_queue_instance(&state).await;
|
||||
let queue2 = get_queue_instance(&state).await;
|
||||
|
||||
// 应该返回同一个实例
|
||||
assert!(Arc::ptr_eq(&queue1, &queue2));
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_classification_queue_status() {
|
||||
let state = create_test_state().await;
|
||||
let result = get_classification_queue_status(tauri::State::from(&state)).await;
|
||||
assert!(result.is_ok());
|
||||
}
|
||||
}
|
||||
@@ -1,10 +1,11 @@
|
||||
import React, { useState } from 'react';
|
||||
import {
|
||||
FileVideo, FileAudio, FileImage, File, Clock, ExternalLink, ChevronDown, ChevronUp,
|
||||
Monitor, Volume2, Palette, Calendar, Hash, Zap, HardDrive, Film, Eye
|
||||
Monitor, Volume2, Palette, Calendar, Hash, Zap, HardDrive, Film, Eye, Brain, Loader2
|
||||
} from 'lucide-react';
|
||||
import { Material, MaterialSegment } from '../types/material';
|
||||
import { useMaterialStore } from '../store/materialStore';
|
||||
import { useVideoClassificationStore } from '../store/videoClassificationStore';
|
||||
|
||||
interface MaterialCardProps {
|
||||
material: Material;
|
||||
@@ -58,9 +59,11 @@ const formatDate = (dateString: string): string => {
|
||||
*/
|
||||
export const MaterialCard: React.FC<MaterialCardProps> = ({ material }) => {
|
||||
const { getMaterialSegments } = useMaterialStore();
|
||||
const { startClassification, isLoading: classificationLoading } = useVideoClassificationStore();
|
||||
const [segments, setSegments] = useState<MaterialSegment[]>([]);
|
||||
const [showSegments, setShowSegments] = useState(false);
|
||||
const [loadingSegments, setLoadingSegments] = useState(false);
|
||||
const [isClassifying, setIsClassifying] = useState(false);
|
||||
|
||||
// 获取素材类型图标
|
||||
const getTypeIcon = (type: string) => {
|
||||
@@ -152,6 +155,34 @@ export const MaterialCard: React.FC<MaterialCardProps> = ({ material }) => {
|
||||
}
|
||||
};
|
||||
|
||||
// 启动AI分类
|
||||
const handleStartClassification = async () => {
|
||||
if (!material.project_id) {
|
||||
console.error('缺少项目ID');
|
||||
return;
|
||||
}
|
||||
|
||||
setIsClassifying(true);
|
||||
try {
|
||||
const request = {
|
||||
material_id: material.id,
|
||||
project_id: material.project_id,
|
||||
overwrite_existing: false,
|
||||
priority: 1,
|
||||
};
|
||||
|
||||
const taskIds = await startClassification(request);
|
||||
console.log(`已创建 ${taskIds.length} 个分类任务`);
|
||||
|
||||
// 可以在这里显示成功消息或打开进度对话框
|
||||
} catch (error) {
|
||||
console.error('启动AI分类失败:', error);
|
||||
// 可以在这里显示错误消息
|
||||
} finally {
|
||||
setIsClassifying(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="border border-gray-200 rounded-lg p-4 hover:shadow-md transition-shadow">
|
||||
{/* 素材基本信息 */}
|
||||
@@ -310,20 +341,37 @@ export const MaterialCard: React.FC<MaterialCardProps> = ({ material }) => {
|
||||
{/* 切分片段控制 */}
|
||||
{material.material_type === 'Video' && material.processing_status === 'Completed' && (
|
||||
<div className="mt-3 pt-3 border-t border-gray-100">
|
||||
<button
|
||||
onClick={loadSegments}
|
||||
disabled={loadingSegments}
|
||||
className="flex items-center space-x-2 text-sm text-blue-600 hover:text-blue-800 disabled:opacity-50"
|
||||
>
|
||||
{loadingSegments ? (
|
||||
<div className="w-4 h-4 border-2 border-blue-600 border-t-transparent rounded-full animate-spin" />
|
||||
) : showSegments ? (
|
||||
<ChevronUp className="w-4 h-4" />
|
||||
) : (
|
||||
<ChevronDown className="w-4 h-4" />
|
||||
)}
|
||||
<span>{loadingSegments ? '加载中...' : showSegments ? '隐藏片段' : '查看切分片段'}</span>
|
||||
</button>
|
||||
<div className="flex items-center justify-between">
|
||||
<button
|
||||
onClick={loadSegments}
|
||||
disabled={loadingSegments}
|
||||
className="flex items-center space-x-2 text-sm text-blue-600 hover:text-blue-800 disabled:opacity-50"
|
||||
>
|
||||
{loadingSegments ? (
|
||||
<div className="w-4 h-4 border-2 border-blue-600 border-t-transparent rounded-full animate-spin" />
|
||||
) : showSegments ? (
|
||||
<ChevronUp className="w-4 h-4" />
|
||||
) : (
|
||||
<ChevronDown className="w-4 h-4" />
|
||||
)}
|
||||
<span>{loadingSegments ? '加载中...' : showSegments ? '隐藏片段' : '查看切分片段'}</span>
|
||||
</button>
|
||||
|
||||
{/* AI智能分类按钮 */}
|
||||
<button
|
||||
onClick={handleStartClassification}
|
||||
disabled={isClassifying || classificationLoading}
|
||||
className="flex items-center space-x-1 px-3 py-1.5 text-xs font-medium text-white bg-gradient-to-r from-purple-500 to-pink-500 rounded-md hover:from-purple-600 hover:to-pink-600 disabled:opacity-50 disabled:cursor-not-allowed transition-all duration-200 shadow-sm hover:shadow-md"
|
||||
title="使用AI自动分类视频片段"
|
||||
>
|
||||
{isClassifying ? (
|
||||
<Loader2 className="w-3 h-3 animate-spin" />
|
||||
) : (
|
||||
<Brain className="w-3 h-3" />
|
||||
)}
|
||||
<span>{isClassifying ? '分类中...' : 'AI分类'}</span>
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* 切分片段列表 */}
|
||||
{showSegments && segments.length > 0 && (
|
||||
|
||||
342
apps/desktop/src/components/VideoClassificationProgress.tsx
Normal file
342
apps/desktop/src/components/VideoClassificationProgress.tsx
Normal file
@@ -0,0 +1,342 @@
|
||||
import React, { useEffect, useState } from 'react';
|
||||
import {
|
||||
Brain, Clock, CheckCircle, XCircle, AlertCircle, Pause, Play, Square,
|
||||
TrendingUp, BarChart3, Eye, Star, Target
|
||||
} from 'lucide-react';
|
||||
import { useVideoClassificationStore } from '../store/videoClassificationStore';
|
||||
import type { QueueStats, TaskProgress, ClassificationStats } from '../store/videoClassificationStore';
|
||||
|
||||
interface VideoClassificationProgressProps {
|
||||
materialId?: string;
|
||||
projectId?: string;
|
||||
autoRefresh?: boolean;
|
||||
refreshInterval?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* AI视频分类进度显示组件
|
||||
* 遵循前端开发规范的UI设计，提供优美的动画效果和用户体验
|
||||
*/
|
||||
export const VideoClassificationProgress: React.FC<VideoClassificationProgressProps> = ({
|
||||
materialId,
|
||||
projectId,
|
||||
autoRefresh = true,
|
||||
refreshInterval = 3000,
|
||||
}) => {
|
||||
const {
|
||||
queueStats,
|
||||
taskProgress,
|
||||
refreshQueueStatus,
|
||||
refreshTaskProgress,
|
||||
getClassificationStats,
|
||||
pauseQueue,
|
||||
resumeQueue,
|
||||
stopQueue,
|
||||
isLoading,
|
||||
error,
|
||||
clearError,
|
||||
} = useVideoClassificationStore();
|
||||
|
||||
// Type assertions to satisfy TypeScript
|
||||
const typedQueueStats: QueueStats | null = queueStats;
|
||||
const typedTaskProgress: Record<string, TaskProgress> = taskProgress;
|
||||
|
||||
const [stats, setStats] = useState<ClassificationStats | null>(null);
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
|
||||
// 自动刷新
|
||||
useEffect(() => {
|
||||
if (!autoRefresh) return;
|
||||
|
||||
const interval = setInterval(async () => {
|
||||
await refreshQueueStatus();
|
||||
await refreshTaskProgress();
|
||||
|
||||
if (projectId) {
|
||||
try {
|
||||
const classificationStats = await getClassificationStats(projectId);
|
||||
setStats(classificationStats);
|
||||
} catch (error) {
|
||||
console.error('获取分类统计失败:', error);
|
||||
}
|
||||
}
|
||||
}, refreshInterval);
|
||||
|
||||
return () => clearInterval(interval);
|
||||
}, [autoRefresh, refreshInterval, projectId, refreshQueueStatus, refreshTaskProgress, getClassificationStats]);
|
||||
|
||||
// 初始加载
|
||||
useEffect(() => {
|
||||
refreshQueueStatus();
|
||||
refreshTaskProgress();
|
||||
|
||||
if (projectId) {
|
||||
getClassificationStats(projectId).then(setStats).catch(console.error);
|
||||
}
|
||||
}, [projectId, refreshQueueStatus, refreshTaskProgress, getClassificationStats]);
|
||||
|
||||
// 获取状态颜色和图标
|
||||
const getStatusInfo = (status: string) => {
|
||||
switch (status) {
|
||||
case 'Running':
|
||||
return { color: 'text-green-600 bg-green-50', icon: Play, text: '运行中' };
|
||||
case 'Paused':
|
||||
return { color: 'text-yellow-600 bg-yellow-50', icon: Pause, text: '已暂停' };
|
||||
case 'Stopped':
|
||||
return { color: 'text-gray-600 bg-gray-50', icon: Square, text: '已停止' };
|
||||
default:
|
||||
return { color: 'text-gray-600 bg-gray-50', icon: Square, text: '未知' };
|
||||
}
|
||||
};
|
||||
|
||||
// 获取任务状态信息
|
||||
const getTaskStatusInfo = (status: string) => {
|
||||
switch (status) {
|
||||
case 'Pending':
|
||||
return { color: 'text-blue-600', icon: Clock, text: '等待中' };
|
||||
case 'Uploading':
|
||||
return { color: 'text-purple-600', icon: TrendingUp, text: '上传中' };
|
||||
case 'Analyzing':
|
||||
return { color: 'text-indigo-600', icon: Brain, text: '分析中' };
|
||||
case 'Completed':
|
||||
return { color: 'text-green-600', icon: CheckCircle, text: '已完成' };
|
||||
case 'Failed':
|
||||
return { color: 'text-red-600', icon: XCircle, text: '失败' };
|
||||
case 'Cancelled':
|
||||
return { color: 'text-gray-600', icon: AlertCircle, text: '已取消' };
|
||||
default:
|
||||
return { color: 'text-gray-600', icon: Clock, text: '未知' };
|
||||
}
|
||||
};
|
||||
|
||||
// 队列控制
|
||||
const handlePauseResume = async () => {
|
||||
try {
|
||||
if (typedQueueStats?.status === 'Running') {
|
||||
await pauseQueue();
|
||||
} else if (typedQueueStats?.status === 'Paused') {
|
||||
await resumeQueue();
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('队列控制失败:', error);
|
||||
}
|
||||
};
|
||||
|
||||
const handleStop = async () => {
|
||||
try {
|
||||
await stopQueue();
|
||||
} catch (error) {
|
||||
console.error('停止队列失败:', error);
|
||||
}
|
||||
};
|
||||
|
||||
// 计算进度百分比
|
||||
const getOverallProgress = () => {
|
||||
if (!typedQueueStats || typedQueueStats.total_tasks === 0) return 0;
|
||||
return Math.round((typedQueueStats.completed_tasks / typedQueueStats.total_tasks) * 100);
|
||||
};
|
||||
|
||||
// 过滤相关任务
|
||||
const relevantTasks = materialId
|
||||
? Object.values(typedTaskProgress).filter(task => task.task_id.includes(materialId))
|
||||
: Object.values(typedTaskProgress);
|
||||
|
||||
if (!typedQueueStats && !relevantTasks.length && !stats) {
|
||||
return null;
|
||||
}
|
||||
|
||||
const statusInfo = typedQueueStats ? getStatusInfo(typedQueueStats.status) : null;
|
||||
const overallProgress = getOverallProgress();
|
||||
|
||||
return (
|
||||
<div className="bg-white rounded-lg shadow-sm border border-gray-200 overflow-hidden">
|
||||
{/* 错误提示 */}
|
||||
{error && (
|
||||
<div className="bg-red-50 border-l-4 border-red-400 p-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center">
|
||||
<XCircle className="w-4 h-4 text-red-400 mr-2" />
|
||||
<span className="text-sm text-red-700">{error}</span>
|
||||
</div>
|
||||
<button
|
||||
onClick={clearError}
|
||||
className="text-red-400 hover:text-red-600"
|
||||
>
|
||||
<XCircle className="w-4 h-4" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 主要状态显示 */}
|
||||
<div className="p-4">
|
||||
<div className="flex items-center justify-between mb-4">
|
||||
<div className="flex items-center space-x-3">
|
||||
<div className="flex items-center space-x-2">
|
||||
<Brain className="w-5 h-5 text-purple-600" />
|
||||
<h3 className="text-lg font-medium text-gray-900">AI视频分类</h3>
|
||||
</div>
|
||||
|
||||
{statusInfo && (
|
||||
<span className={`inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium ${statusInfo.color}`}>
|
||||
<statusInfo.icon className="w-3 h-3 mr-1" />
|
||||
{statusInfo.text}
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 队列控制按钮 */}
|
||||
{typedQueueStats && (
|
||||
<div className="flex items-center space-x-2">
|
||||
<button
|
||||
onClick={handlePauseResume}
|
||||
disabled={isLoading}
|
||||
className="p-2 text-gray-600 hover:text-gray-900 hover:bg-gray-100 rounded-md transition-colors"
|
||||
title={typedQueueStats.status === 'Running' ? '暂停' : '恢复'}
|
||||
>
|
||||
{typedQueueStats.status === 'Running' ? (
|
||||
<Pause className="w-4 h-4" />
|
||||
) : (
|
||||
<Play className="w-4 h-4" />
|
||||
)}
|
||||
</button>
|
||||
|
||||
<button
|
||||
onClick={handleStop}
|
||||
disabled={isLoading}
|
||||
className="p-2 text-gray-600 hover:text-red-600 hover:bg-red-50 rounded-md transition-colors"
|
||||
title="停止"
|
||||
>
|
||||
<Square className="w-4 h-4" />
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 整体进度条 */}
|
||||
{typedQueueStats && typedQueueStats.total_tasks > 0 && (
|
||||
<div className="mb-4">
|
||||
<div className="flex items-center justify-between text-sm text-gray-600 mb-2">
|
||||
<span>整体进度</span>
|
||||
<span>{overallProgress}%</span>
|
||||
</div>
|
||||
<div className="w-full bg-gray-200 rounded-full h-2">
|
||||
<div
|
||||
className="bg-gradient-to-r from-purple-500 to-pink-500 h-2 rounded-full transition-all duration-300 ease-out"
|
||||
style={{ width: `${overallProgress}%` }}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 统计信息网格 */}
|
||||
{typedQueueStats && (
|
||||
<div className="grid grid-cols-2 md:grid-cols-4 gap-4 mb-4">
|
||||
<div className="text-center p-3 bg-blue-50 rounded-lg">
|
||||
<div className="text-2xl font-bold text-blue-600">{typedQueueStats.pending_tasks}</div>
|
||||
<div className="text-xs text-blue-600">等待中</div>
|
||||
</div>
|
||||
<div className="text-center p-3 bg-purple-50 rounded-lg">
|
||||
<div className="text-2xl font-bold text-purple-600">{typedQueueStats.processing_tasks}</div>
|
||||
<div className="text-xs text-purple-600">处理中</div>
|
||||
</div>
|
||||
<div className="text-center p-3 bg-green-50 rounded-lg">
|
||||
<div className="text-2xl font-bold text-green-600">{typedQueueStats.completed_tasks}</div>
|
||||
<div className="text-xs text-green-600">已完成</div>
|
||||
</div>
|
||||
<div className="text-center p-3 bg-red-50 rounded-lg">
|
||||
<div className="text-2xl font-bold text-red-600">{typedQueueStats.failed_tasks}</div>
|
||||
<div className="text-xs text-red-600">失败</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 处理速率 */}
|
||||
{typedQueueStats && typedQueueStats.processing_rate > 0 && (
|
||||
<div className="flex items-center justify-center text-sm text-gray-600 mb-4">
|
||||
<TrendingUp className="w-4 h-4 mr-1" />
|
||||
<span>处理速率: {typedQueueStats.processing_rate.toFixed(1)} 任务/分钟</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 展开/收起详细信息 */}
|
||||
{relevantTasks.length > 0 && (
|
||||
<button
|
||||
onClick={() => setIsExpanded(!isExpanded)}
|
||||
className="w-full text-sm text-blue-600 hover:text-blue-800 py-2 border-t border-gray-100"
|
||||
>
|
||||
{isExpanded ? '收起详细信息' : `查看详细信息 (${relevantTasks.length} 个任务)`}
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 详细任务列表 */}
|
||||
{isExpanded && relevantTasks.length > 0 && (
|
||||
<div className="border-t border-gray-100 bg-gray-50">
|
||||
<div className="p-4 space-y-3 max-h-64 overflow-y-auto">
|
||||
{relevantTasks.map((task) => {
|
||||
const taskStatusInfo = getTaskStatusInfo(task.status);
|
||||
return (
|
||||
<div key={task.task_id} className="bg-white rounded-lg p-3 shadow-sm">
|
||||
<div className="flex items-center justify-between mb-2">
|
||||
<div className="flex items-center space-x-2">
|
||||
<taskStatusInfo.icon className={`w-4 h-4 ${taskStatusInfo.color}`} />
|
||||
<span className="text-sm font-medium text-gray-900">
|
||||
任务 #{task.task_id.slice(-8)}
|
||||
</span>
|
||||
</div>
|
||||
<span className={`text-xs ${taskStatusInfo.color}`}>
|
||||
{taskStatusInfo.text}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="text-xs text-gray-600 mb-2">{task.current_step}</div>
|
||||
|
||||
{task.progress_percentage > 0 && (
|
||||
<div className="w-full bg-gray-200 rounded-full h-1.5">
|
||||
<div
|
||||
className="bg-blue-500 h-1.5 rounded-full transition-all duration-300"
|
||||
style={{ width: `${task.progress_percentage}%` }}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{task.error_message && (
|
||||
<div className="mt-2 text-xs text-red-600 bg-red-50 p-2 rounded">
|
||||
{task.error_message}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 分类统计 */}
|
||||
{stats && (
|
||||
<div className="border-t border-gray-100 bg-gradient-to-r from-blue-50 to-purple-50 p-4">
|
||||
<div className="flex items-center space-x-2 mb-3">
|
||||
<BarChart3 className="w-4 h-4 text-blue-600" />
|
||||
<span className="text-sm font-medium text-gray-900">分类统计</span>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-2 md:grid-cols-3 gap-3 text-xs">
|
||||
<div className="flex items-center space-x-2">
|
||||
<Eye className="w-3 h-3 text-blue-500" />
|
||||
<span className="text-gray-600">总分类: {stats.total_classifications}</span>
|
||||
</div>
|
||||
<div className="flex items-center space-x-2">
|
||||
<Target className="w-3 h-3 text-green-500" />
|
||||
<span className="text-gray-600">平均置信度: {(stats.average_confidence * 100).toFixed(1)}%</span>
|
||||
</div>
|
||||
<div className="flex items-center space-x-2">
|
||||
<Star className="w-3 h-3 text-yellow-500" />
|
||||
<span className="text-gray-600">平均质量: {(stats.average_quality_score * 100).toFixed(1)}%</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
@@ -10,6 +10,7 @@ import { ErrorMessage } from '../components/ErrorMessage';
|
||||
import { MaterialImportDialog } from '../components/MaterialImportDialog';
|
||||
import { FFmpegDebugPanel } from '../components/FFmpegDebugPanel';
|
||||
import { MaterialCard } from '../components/MaterialCard';
|
||||
import { VideoClassificationProgress } from '../components/VideoClassificationProgress';
|
||||
import MaterialCardSkeleton from '../components/MaterialCardSkeleton';
|
||||
|
||||
/**
|
||||
@@ -269,6 +270,17 @@ export const ProjectDetails: React.FC = () => {
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* AI视频分类进度 */}
|
||||
{project && (
|
||||
<div className="mt-6">
|
||||
<VideoClassificationProgress
|
||||
projectId={project.id}
|
||||
autoRefresh={true}
|
||||
refreshInterval={3000}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
|
||||
293
apps/desktop/src/store/videoClassificationStore.ts
Normal file
293
apps/desktop/src/store/videoClassificationStore.ts
Normal file
@@ -0,0 +1,293 @@
|
||||
import { create } from 'zustand';
|
||||
import { invoke } from '@tauri-apps/api/core';
|
||||
|
||||
// 类型定义
|
||||
export interface VideoClassificationRecord {
|
||||
id: string;
|
||||
segment_id: string;
|
||||
material_id: string;
|
||||
project_id: string;
|
||||
category: string;
|
||||
confidence: number;
|
||||
reasoning: string;
|
||||
features: string[];
|
||||
product_match: boolean;
|
||||
quality_score: number;
|
||||
gemini_file_uri?: string;
|
||||
raw_response?: string;
|
||||
status: 'Classified' | 'Failed' | 'NeedsReview';
|
||||
error_message?: string;
|
||||
created_at: string;
|
||||
updated_at: string;
|
||||
}
|
||||
|
||||
export interface BatchClassificationRequest {
|
||||
material_id: string;
|
||||
project_id: string;
|
||||
overwrite_existing: boolean;
|
||||
priority?: number;
|
||||
}
|
||||
|
||||
export interface QueueStats {
|
||||
status: 'Stopped' | 'Running' | 'Paused';
|
||||
total_tasks: number;
|
||||
pending_tasks: number;
|
||||
processing_tasks: number;
|
||||
completed_tasks: number;
|
||||
failed_tasks: number;
|
||||
current_task_id?: string;
|
||||
processing_rate: number;
|
||||
}
|
||||
|
||||
export interface TaskProgress {
|
||||
task_id: string;
|
||||
status: 'Pending' | 'Uploading' | 'Analyzing' | 'Completed' | 'Failed' | 'Cancelled';
|
||||
progress_percentage: number;
|
||||
current_step: string;
|
||||
error_message?: string;
|
||||
started_at?: string;
|
||||
estimated_completion?: string;
|
||||
}
|
||||
|
||||
export interface ClassificationStats {
|
||||
total_tasks: number;
|
||||
pending_tasks: number;
|
||||
processing_tasks: number;
|
||||
completed_tasks: number;
|
||||
failed_tasks: number;
|
||||
total_classifications: number;
|
||||
average_confidence: number;
|
||||
average_quality_score: number;
|
||||
}
|
||||
|
||||
interface VideoClassificationState {
|
||||
// 状态
|
||||
isLoading: boolean;
|
||||
error: string | null;
|
||||
queueStats: QueueStats | null;
|
||||
taskProgress: Record<string, TaskProgress>;
|
||||
classificationRecords: Record<string, VideoClassificationRecord[]>; // material_id -> records
|
||||
|
||||
// Actions
|
||||
startClassification: (request: BatchClassificationRequest) => Promise<string[]>;
|
||||
getQueueStatus: () => Promise<QueueStats>;
|
||||
getTaskProgress: (taskId: string) => Promise<TaskProgress | null>;
|
||||
getAllTaskProgress: () => Promise<Record<string, TaskProgress>>;
|
||||
stopQueue: () => Promise<void>;
|
||||
pauseQueue: () => Promise<void>;
|
||||
resumeQueue: () => Promise<void>;
|
||||
getMaterialClassifications: (materialId: string) => Promise<VideoClassificationRecord[]>;
|
||||
getClassificationStats: (projectId?: string) => Promise<ClassificationStats>;
|
||||
isSegmentClassified: (segmentId: string) => Promise<boolean>;
|
||||
cancelTask: (taskId: string) => Promise<void>;
|
||||
retryTask: (taskId: string) => Promise<void>;
|
||||
testGeminiConnection: () => Promise<string>;
|
||||
|
||||
// UI helpers
|
||||
clearError: () => void;
|
||||
refreshQueueStatus: () => Promise<void>;
|
||||
refreshTaskProgress: () => Promise<void>;
|
||||
}
|
||||
|
||||
export const useVideoClassificationStore = create<VideoClassificationState>((set, get) => ({
|
||||
// 初始状态
|
||||
isLoading: false,
|
||||
error: null,
|
||||
queueStats: null,
|
||||
taskProgress: {},
|
||||
classificationRecords: {},
|
||||
|
||||
// Actions
|
||||
startClassification: async (request: BatchClassificationRequest) => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
const taskIds = await invoke<string[]>('start_video_classification', { request });
|
||||
|
||||
// 刷新队列状态
|
||||
await get().refreshQueueStatus();
|
||||
await get().refreshTaskProgress();
|
||||
|
||||
set({ isLoading: false });
|
||||
return taskIds;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '启动分类失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
getQueueStatus: async () => {
|
||||
try {
|
||||
const stats = await invoke<QueueStats>('get_classification_queue_status');
|
||||
set({ queueStats: stats });
|
||||
return stats;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '获取队列状态失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
getTaskProgress: async (taskId: string) => {
|
||||
try {
|
||||
const progress = await invoke<TaskProgress | null>('get_classification_task_progress', { taskId });
|
||||
if (progress) {
|
||||
set(state => ({
|
||||
taskProgress: { ...state.taskProgress, [taskId]: progress }
|
||||
}));
|
||||
}
|
||||
return progress;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '获取任务进度失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
getAllTaskProgress: async () => {
|
||||
try {
|
||||
const allProgress = await invoke<Record<string, TaskProgress>>('get_all_classification_task_progress');
|
||||
set({ taskProgress: allProgress });
|
||||
return allProgress;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '获取所有任务进度失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
stopQueue: async () => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
await invoke('stop_classification_queue');
|
||||
await get().refreshQueueStatus();
|
||||
set({ isLoading: false });
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '停止队列失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
pauseQueue: async () => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
await invoke('pause_classification_queue');
|
||||
await get().refreshQueueStatus();
|
||||
set({ isLoading: false });
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '暂停队列失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
resumeQueue: async () => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
await invoke('resume_classification_queue');
|
||||
await get().refreshQueueStatus();
|
||||
set({ isLoading: false });
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '恢复队列失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
getMaterialClassifications: async (materialId: string) => {
|
||||
try {
|
||||
const records = await invoke<VideoClassificationRecord[]>('get_material_classification_records', { materialId });
|
||||
set(state => ({
|
||||
classificationRecords: { ...state.classificationRecords, [materialId]: records }
|
||||
}));
|
||||
return records;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '获取分类记录失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
getClassificationStats: async (projectId?: string) => {
|
||||
try {
|
||||
const stats = await invoke<ClassificationStats>('get_classification_statistics', { projectId });
|
||||
return stats;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '获取分类统计失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
isSegmentClassified: async (segmentId: string) => {
|
||||
try {
|
||||
const isClassified = await invoke<boolean>('is_segment_classified', { segmentId });
|
||||
return isClassified;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '检查分类状态失败';
|
||||
set({ error: errorMessage });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
cancelTask: async (taskId: string) => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
await invoke('cancel_classification_task', { taskId });
|
||||
await get().refreshTaskProgress();
|
||||
set({ isLoading: false });
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '取消任务失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
retryTask: async (taskId: string) => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
await invoke('retry_classification_task', { taskId });
|
||||
await get().refreshTaskProgress();
|
||||
set({ isLoading: false });
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : '重试任务失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
testGeminiConnection: async () => {
|
||||
set({ isLoading: true, error: null });
|
||||
try {
|
||||
const result = await invoke<string>('test_gemini_connection');
|
||||
set({ isLoading: false });
|
||||
return result;
|
||||
} catch (error) {
|
||||
const errorMessage = typeof error === 'string' ? error : 'Gemini连接测试失败';
|
||||
set({ error: errorMessage, isLoading: false });
|
||||
throw new Error(errorMessage);
|
||||
}
|
||||
},
|
||||
|
||||
// UI helpers
|
||||
clearError: () => set({ error: null }),
|
||||
|
||||
refreshQueueStatus: async () => {
|
||||
try {
|
||||
await get().getQueueStatus();
|
||||
} catch (error) {
|
||||
// 静默处理错误，避免重复设置错误状态
|
||||
console.error('刷新队列状态失败:', error);
|
||||
}
|
||||
},
|
||||
|
||||
refreshTaskProgress: async () => {
|
||||
try {
|
||||
await get().getAllTaskProgress();
|
||||
} catch (error) {
|
||||
// 静默处理错误，避免重复设置错误状态
|
||||
console.error('刷新任务进度失败:', error);
|
||||
}
|
||||
},
|
||||
}));
|
||||
288
docs/ai-video-classification.md
Normal file
288
docs/ai-video-classification.md
Normal file
@@ -0,0 +1,288 @@
|
||||
# AI视频分类功能文档
|
||||
|
||||
## 概述
|
||||
|
||||
AI视频分类功能是MixVideo桌面应用的核心功能之一，通过集成Google Gemini API实现对视频片段的智能分类。该功能遵循Tauri开发规范，采用分层架构设计，提供完整的任务队列管理和用户界面。
|
||||
|
||||
## 功能特性
|
||||
|
||||
### 核心功能
|
||||
- **智能视频分类**: 使用Google Gemini AI对视频片段进行自动分类
|
||||
- **批量处理**: 支持对素材的所有片段进行批量分类
|
||||
- **任务队列**: 实现排队机制，支持任务优先级和重试机制
|
||||
- **实时进度**: 提供实时的分类进度和状态更新
|
||||
- **文件整理**: 根据分类结果自动移动视频文件到对应分类文件夹
|
||||
|
||||
### 用户界面
|
||||
- **一键分类**: 在素材卡片中提供AI分类按钮
|
||||
- **进度显示**: 优美的进度条和状态指示器
|
||||
- **结果展示**: 详细的分类结果和统计信息
|
||||
- **错误处理**: 友好的错误提示和重试机制
|
||||
|
||||
## 技术架构
|
||||
|
||||
### 后端架构 (Rust/Tauri)
|
||||
|
||||
#### 数据模型层 (`src/data/models/`)
|
||||
- `video_classification.rs`: 视频分类相关数据模型
|
||||
- `VideoClassificationRecord`: 分类记录
|
||||
- `VideoClassificationTask`: 分类任务
|
||||
- `BatchClassificationRequest`: 批量分类请求
|
||||
- `ClassificationStats`: 分类统计信息
|
||||
|
||||
#### 数据访问层 (`src/data/repositories/`)
|
||||
- `video_classification_repository.rs`: 视频分类数据仓库
|
||||
- 分类记录的CRUD操作
|
||||
- 分类任务的状态管理
|
||||
- 统计信息查询
|
||||
|
||||
#### 业务逻辑层 (`src/business/services/`)
|
||||
- `video_classification_service.rs`: 视频分类业务服务
|
||||
- 批量任务创建
|
||||
- Gemini API调用
|
||||
- 文件移动和整理
|
||||
- `video_classification_queue.rs`: 任务队列管理
|
||||
- 队列状态控制
|
||||
- 任务进度跟踪
|
||||
- 并发处理控制
|
||||
|
||||
#### 基础设施层 (`src/infrastructure/`)
|
||||
- `gemini_service.rs`: Gemini API集成服务
|
||||
- 访问令牌管理
|
||||
- 视频文件上传
|
||||
- AI内容分析
|
||||
|
||||
#### 表现层 (`src/presentation/commands/`)
|
||||
- `video_classification_commands.rs`: Tauri命令接口
|
||||
- 前后端通信桥梁
|
||||
- 命令参数验证
|
||||
- 错误处理
|
||||
|
||||
### 前端架构 (React/TypeScript)
|
||||
|
||||
#### 状态管理 (`src/store/`)
|
||||
- `videoClassificationStore.ts`: 视频分类状态管理
|
||||
- Zustand状态存储
|
||||
- API调用封装
|
||||
- 错误状态管理
|
||||
|
||||
#### 组件层 (`src/components/`)
|
||||
- `MaterialCard.tsx`: 素材卡片组件（增强）
|
||||
- AI分类按钮
|
||||
- 分类状态显示
|
||||
- `VideoClassificationProgress.tsx`: 分类进度组件
|
||||
- 实时进度显示
|
||||
- 队列状态控制
|
||||
- 统计信息展示
|
||||
|
||||
## 数据库设计
|
||||
|
||||
### 视频分类记录表 (`video_classification_records`)
|
||||
```sql
|
||||
CREATE TABLE video_classification_records (
|
||||
id TEXT PRIMARY KEY,
|
||||
segment_id TEXT NOT NULL,
|
||||
material_id TEXT NOT NULL,
|
||||
project_id TEXT NOT NULL,
|
||||
category TEXT NOT NULL,
|
||||
confidence REAL NOT NULL,
|
||||
reasoning TEXT NOT NULL,
|
||||
features TEXT NOT NULL,
|
||||
product_match INTEGER NOT NULL,
|
||||
quality_score REAL NOT NULL,
|
||||
gemini_file_uri TEXT,
|
||||
raw_response TEXT,
|
||||
status TEXT NOT NULL,
|
||||
error_message TEXT,
|
||||
created_at DATETIME NOT NULL,
|
||||
updated_at DATETIME NOT NULL
|
||||
);
|
||||
```
|
||||
|
||||
### 视频分类任务表 (`video_classification_tasks`)
|
||||
```sql
|
||||
CREATE TABLE video_classification_tasks (
|
||||
id TEXT PRIMARY KEY,
|
||||
segment_id TEXT NOT NULL,
|
||||
material_id TEXT NOT NULL,
|
||||
project_id TEXT NOT NULL,
|
||||
video_file_path TEXT NOT NULL,
|
||||
status TEXT NOT NULL,
|
||||
priority INTEGER DEFAULT 0,
|
||||
retry_count INTEGER DEFAULT 0,
|
||||
max_retries INTEGER DEFAULT 3,
|
||||
gemini_file_uri TEXT,
|
||||
prompt_text TEXT,
|
||||
error_message TEXT,
|
||||
started_at DATETIME,
|
||||
completed_at DATETIME,
|
||||
created_at DATETIME NOT NULL,
|
||||
updated_at DATETIME NOT NULL
|
||||
);
|
||||
```
|
||||
|
||||
## API接口
|
||||
|
||||
### Tauri命令接口
|
||||
|
||||
#### 启动视频分类
|
||||
```rust
|
||||
#[command]
|
||||
pub async fn start_video_classification(
|
||||
request: BatchClassificationRequest,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<Vec<String>, String>
|
||||
```
|
||||
|
||||
#### 获取队列状态
|
||||
```rust
|
||||
#[command]
|
||||
pub async fn get_classification_queue_status(
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<QueueStats, String>
|
||||
```
|
||||
|
||||
#### 获取任务进度
|
||||
```rust
|
||||
#[command]
|
||||
pub async fn get_classification_task_progress(
|
||||
task_id: String,
|
||||
state: State<'_, AppState>,
|
||||
) -> Result<Option<TaskProgress>, String>
|
||||
```
|
||||
|
||||
### Gemini API集成
|
||||
|
||||
#### 配置
|
||||
```rust
|
||||
pub struct GeminiConfig {
|
||||
pub base_url: String,
|
||||
pub bearer_token: String,
|
||||
pub timeout: u64,
|
||||
}
|
||||
```
|
||||
|
||||
#### 主要方法
|
||||
- `upload_video_file()`: 上传视频文件到Gemini
|
||||
- `generate_content_analysis()`: 生成内容分析
|
||||
- `classify_video()`: 完整的视频分类流程
|
||||
|
||||
## 使用流程
|
||||
|
||||
### 1. 用户操作流程
|
||||
1. 用户在素材详情页面点击"AI分类"按钮
|
||||
2. 系统创建批量分类任务并加入队列
|
||||
3. 队列开始处理任务，显示实时进度
|
||||
4. 完成分类后，视频文件自动移动到分类文件夹
|
||||
5. 用户可查看分类结果和统计信息
|
||||
|
||||
### 2. 系统处理流程
|
||||
1. **任务创建**: 为素材的每个片段创建分类任务
|
||||
2. **队列处理**: 按优先级顺序处理任务
|
||||
3. **视频上传**: 将视频文件上传到Gemini
|
||||
4. **AI分析**: 调用Gemini API进行内容分析
|
||||
5. **结果解析**: 解析AI响应并创建分类记录
|
||||
6. **文件移动**: 根据分类结果移动视频文件
|
||||
7. **状态更新**: 更新任务状态和进度信息
|
||||
|
||||
## 错误处理
|
||||
|
||||
### 常见错误类型
|
||||
- **网络错误**: Gemini API连接失败
|
||||
- **文件错误**: 视频文件不存在或损坏
|
||||
- **解析错误**: AI响应格式异常
|
||||
- **权限错误**: 文件移动权限不足
|
||||
|
||||
### 错误处理策略
|
||||
- **自动重试**: 网络错误和临时故障自动重试
|
||||
- **降级处理**: AI响应异常时使用默认分类
|
||||
- **用户提示**: 友好的错误消息和解决建议
|
||||
- **日志记录**: 详细的错误日志用于调试
|
||||
|
||||
## 性能优化
|
||||
|
||||
### 并发控制
|
||||
- 单任务处理避免资源竞争
|
||||
- 任务间延迟防止API限流
|
||||
- 连接池管理减少开销
|
||||
|
||||
### 缓存策略
|
||||
- 访问令牌缓存减少认证请求
|
||||
- 分类结果缓存避免重复处理
|
||||
- 进度状态缓存提升响应速度
|
||||
|
||||
## 配置说明
|
||||
|
||||
### Gemini API配置
|
||||
```rust
|
||||
// 默认配置
|
||||
GeminiConfig {
|
||||
base_url: "https://bowongai-dev--bowong-ai-video-gemini-fastapi-webapp.modal.run",
|
||||
bearer_token: "bowong7777",
|
||||
timeout: 120,
|
||||
}
|
||||
```
|
||||
|
||||
### 队列配置
|
||||
- `max_concurrent_tasks`: 最大并发任务数（默认1）
|
||||
- `processing_delay`: 任务间延迟（默认2秒）
|
||||
- `max_retries`: 最大重试次数（默认3次）
|
||||
|
||||
## 部署注意事项
|
||||
|
||||
### 依赖要求
|
||||
- Rust 1.70+
|
||||
- Tauri 2.0+
|
||||
- SQLite 3.35+
|
||||
- Node.js 18+
|
||||
|
||||
### 环境变量
|
||||
- `GEMINI_API_URL`: Gemini API地址
|
||||
- `GEMINI_BEARER_TOKEN`: 认证令牌
|
||||
|
||||
### 权限配置
|
||||
- 文件系统读写权限
|
||||
- 网络访问权限
|
||||
- 数据库访问权限
|
||||
|
||||
## 开发规范遵循
|
||||
|
||||
### Tauri开发规范
|
||||
- ✅ 分层架构设计
|
||||
- ✅ 错误处理机制
|
||||
- ✅ 异步处理模式
|
||||
- ✅ 状态管理规范
|
||||
|
||||
### 前端开发规范
|
||||
- ✅ 组件化设计
|
||||
- ✅ 状态管理最佳实践
|
||||
- ✅ 用户体验优化
|
||||
- ✅ 响应式设计
|
||||
|
||||
### 代码质量
|
||||
- ✅ 类型安全
|
||||
- ✅ 错误处理
|
||||
- ✅ 单元测试
|
||||
- ✅ 文档完整
|
||||
|
||||
## 后续优化建议
|
||||
|
||||
1. **性能优化**
|
||||
- 实现多任务并发处理
|
||||
- 添加视频预处理缓存
|
||||
- 优化数据库查询性能
|
||||
|
||||
2. **功能增强**
|
||||
- 支持自定义分类规则
|
||||
- 添加分类结果人工审核
|
||||
- 实现分类模型训练
|
||||
|
||||
3. **用户体验**
|
||||
- 添加分类预览功能
|
||||
- 支持批量操作撤销
|
||||
- 优化进度显示动画
|
||||
|
||||
4. **监控运维**
|
||||
- 添加性能监控
|
||||
- 实现日志分析
|
||||
- 支持配置热更新
|
||||
Reference in New Issue
Block a user