feat: add timeline fps in api request

content/docs/NOTES.md

@@ -163,4 +163,4 @@ Abort trap: 6
   <summary>How can I interpret my data more intuitively?</summary>
 
   We recommend using [TablePlus](https://tableplus.com/) to open the SQLite database located alongside the data.
-</details>
+</details>

screenpipe-app-tauri/app/timeline/page.tsx

@@ -75,16 +75,18 @@ export default function Timeline() {
   }, []);
 
   const setupEventSource = () => {
+    console.log("timeline fps:", settings.timelineFps);
     if (eventSourceRef.current) {
       eventSourceRef.current.close();
     }
 
+    const target_fps = settings.timelineFps;
     const endTime = new Date();
     endTime.setMinutes(endTime.getMinutes() - 2);
     const startTime = new Date();
     startTime.setHours(0, 1, 0, 0);
 
-    const url = `http://localhost:3030/stream/frames?start_time=${startTime.toISOString()}&end_time=${endTime.toISOString()}&order=descending`;
+    const url = `http://localhost:3030/stream/frames?start_time=${startTime.toISOString()}&end_time=${endTime.toISOString()}&order=descending&target_fps=${target_fps}`;
 
     setLoadedTimeRange({
       start: startTime,
@@ -181,7 +183,7 @@ export default function Timeline() {
       setIsLoading(false);
       setError(null);
     };
-  }, []);
+  }, [settings.timelineFps]);
 
   const handleScroll = useMemo(
     () =>

screenpipe-app-tauri/components/recording-settings.tsx

@@ -104,6 +104,7 @@ export function RecordingSettings({
   > | null>(null);
   const [windowsForIgnore, setWindowsForIgnore] = useState("");
   const [windowsForInclude, setWindowsForInclude] = useState("");
+  const [isTimelineEnabled, setIsTimelineEnabled] = useState(localSettings.enableFrameCache);
 
   const [availableMonitors, setAvailableMonitors] = useState<MonitorDevice[]>(
     []
@@ -229,6 +230,7 @@ export function RecordingSettings({
         includedWindows: localSettings.includedWindows,
         deepgramApiKey: localSettings.deepgramApiKey,
         fps: localSettings.fps,
+        timelineFps: localSettings.timelineFps,
         vadSensitivity: localSettings.vadSensitivity,
         audioChunkDuration: localSettings.audioChunkDuration,
         analyticsEnabled: localSettings.analyticsEnabled,
@@ -384,6 +386,10 @@ export function RecordingSettings({
     setLocalSettings({ ...localSettings, fps: value[0] });
   };
 
+  const handleTimelineFpsChange = (value: number[]) => {
+    setLocalSettings({ ...localSettings, timelineFps: value[0] });
+  };
+
   const handleVadSensitivityChange = (value: number[]) => {
     const sensitivityMap: { [key: number]: VadSensitivity } = {
       2: "high",
@@ -583,6 +589,7 @@ export function RecordingSettings({
   };
 
   const handleFrameCacheToggle = (checked: boolean) => {
+    setIsTimelineEnabled(checked);
     setLocalSettings({
       ...localSettings,
       enableFrameCache: checked,
@@ -1469,7 +1476,7 @@ export function RecordingSettings({
                 <div className="flex items-center space-x-2">
                   <Switch
                     id="frame-cache-toggle"
-                    checked={localSettings.enableFrameCache}
+                    checked={isTimelineEnabled}
                     onCheckedChange={handleFrameCacheToggle}
                   />
                   <Label
@@ -1496,6 +1503,41 @@ export function RecordingSettings({
                 </div>
               </div>
             </div>
+            {isTimelineEnabled && (
+              <div className="flex !w-full items-center space-x-4">
+                <Slider
+                  id="timeline-fps"
+                  min={0.1}
+                  max={10}
+                  step={0.1}
+                  value={[localSettings.timelineFps]}
+                  onValueChange={handleTimelineFpsChange}
+                  className="flex-grow"
+                />
+                <span className="w-12 text-right">
+                  {localSettings.timelineFps.toFixed(1)}
+                </span>
+                  <Label
+                    htmlFor="timeline-fps"
+                    className="flex items-center space-x-2"
+                  >
+                    <TooltipProvider>
+                      <Tooltip>
+                        <TooltipTrigger>
+                          <HelpCircle className="h-4 w-4 cursor-default" />
+                        </TooltipTrigger>
+                        <TooltipContent side="right">
+                          <p>
+                            timeline fps tuner, which will let you set fps for timeline stream
+                            <br />
+                            may increase CPU usage and memory consumption.
+                          </p>
+                        </TooltipContent>
+                      </Tooltip>
+                    </TooltipProvider>
+                  </Label>
+              </div>
+            )}
             {isMacOS && (
               <div className="flex items-center space-x-2">
                 <Switch

screenpipe-app-tauri/lib/hooks/use-settings.tsx

@@ -49,6 +49,7 @@ export type Settings= {
   aiUrl: string;
   aiMaxContextChars: number;
   fps: number;
+  timelineFps: number;
   vadSensitivity: VadSensitivity;
   analyticsEnabled: boolean;
   audioChunkDuration: number; // new field
@@ -94,6 +95,7 @@ const DEFAULT_SETTINGS: Settings = {
   aiUrl: "https://api.openai.com/v1",
   aiMaxContextChars: 30000,
   fps: 0.5,
+  timelineFps: 0.1,
   vadSensitivity: "high",
   analyticsEnabled: true,
   audioChunkDuration: 30, // default to 10 seconds
@@ -299,4 +301,4 @@ async function createUserSettings(
   }
 
   return userSettingsObject as Settings
-}
+}

screenpipe-server/src/bin/screenpipe-server.rs

@@ -984,9 +984,8 @@ fn persist_path_windows(new_path: PathBuf) -> anyhow::Result<()> {
 
     // Ensure 'setx' command can handle the new PATH length
     if current_path.len() + new_path.to_str().unwrap_or("").len() + 1 > 1024 {
-        return Err(anyhow::anyhow!(
-            "the PATH is too long to persist using 'setx'. please shorten the PATH."
-        ));
+        debug!("the PATH is too long to persist using 'setx'. please shorten the PATH.");
+        return Ok(());
     }
 
     // Construct the new PATH string

screenpipe-server/src/server.rs

@@ -1219,6 +1219,7 @@ enum Order {
 pub struct StreamFramesRequest {
     start_time: DateTime<Utc>,
     end_time: DateTime<Utc>,
+    target_fps: Option<f64>,
     // #[serde(rename = "order")]
     // #[serde(default = "descending")]
     // order: Order,
@@ -1370,9 +1371,10 @@ async fn stream_frames_handler(
         // Spawn frame extraction task using get_frames
         tokio::spawn({
             let frame_tx = frame_tx.clone();
+            let target_fps = request.target_fps.unwrap_or(0.1);
             async move {
                 tokio::select! {
-                    result = cache.get_frames(center_timestamp, duration_minutes, frame_tx.clone(), true) => {
+                    result = cache.get_frames(center_timestamp, duration_minutes, frame_tx.clone(), true, target_fps) => {
                         if let Err(e) = result {
                             error!("frame extraction failed: {}", e);
                             // Send error to client

screenpipe-server/src/video_cache.rs

@@ -458,6 +458,7 @@ impl FrameCache {
         start_time: DateTime<Utc>,
         end_time: DateTime<Utc>,
         frame_tx: FrameChannel,
+        target_fps: f64,
     ) -> Result<()> {
         let mut extraction_queue = HashMap::new();
         let mut total_frames = 0;
@@ -545,6 +546,7 @@ impl FrameCache {
                     tasks,
                     frame_tx.clone(),
                     self.cache_tx.clone(),
+                    target_fps,
                 )
                 .await?;
                 total_frames += extracted;
@@ -561,6 +563,7 @@ impl FrameCache {
         duration_minutes: i64,
         frame_tx: Sender<TimeSeriesFrame>,
         descending: bool,
+        target_fps: f64,
     ) -> Result<()> {
         let start = timestamp - Duration::minutes(duration_minutes / 2);
         let end = timestamp + Duration::minutes(duration_minutes / 2);
@@ -575,7 +578,7 @@ impl FrameCache {
             let cache_clone = self.clone();
             tokio::spawn(async move {
                 let result = cache_clone
-                    .extract_frames_batch(start, end, extract_tx)
+                    .extract_frames_batch(start, end, extract_tx, target_fps)
                     .await;
                 debug!("extraction task completed: {:?}", result.is_ok());
                 result
@@ -636,6 +639,7 @@ async fn extract_frame(
     tasks: Vec<(FrameData, OCREntry)>,
     frame_tx: FrameChannel,
     cache_tx: mpsc::Sender<CacheMessage>,
+    target_fps: f64,
 ) -> Result<usize> {
     if !is_video_file_complete(&ffmpeg, &video_file_path).await? {
         debug!("skipping incomplete video file: {}", video_file_path);
@@ -655,11 +659,11 @@ async fn extract_frame(
     let output_pattern = temp_dir.path().join("frame%d.jpg");
 
     // Calculate frame interval based on target FPS
-    let frame_interval = (source_fps / 0.1).round() as i64; // Using 0.1 as target FPS
+    let frame_interval = (source_fps / target_fps).round() as i64; // Using 0.1 as target FPS
 
     debug!(
         "extracting frames with interval {} (source: {}fps, target: {}fps)",
-        frame_interval, source_fps, 0.1
+        frame_interval, source_fps, target_fps
     );
 
     // Calculate which frames to extract