phase 4

detect/emit.py

@@ -0,0 +1,68 @@
+"""
+Event emission helpers for detection pipeline stages.
+
+Single place that knows how to build event payloads.
+Stages call these instead of constructing dicts or dataclasses directly.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from datetime import datetime, timezone
+
+from detect.events import push_detect_event
+from detect.models import PipelineStats
+
+
+def log(job_id: str | None, stage: str, level: str, msg: str) -> None:
+    """Emit a log event."""
+    if not job_id:
+        return
+    payload = {
+        "level": level,
+        "stage": stage,
+        "msg": msg,
+        "ts": datetime.now(timezone.utc).isoformat(),
+    }
+    push_detect_event(job_id, "log", payload)
+
+
+def stats(job_id: str | None, **kwargs) -> None:
+    """Emit a stats_update event. Pass only the fields that changed."""
+    if not job_id:
+        return
+    s = PipelineStats(**kwargs)
+    push_detect_event(job_id, "stats_update", dataclasses.asdict(s))
+
+
+def detection(
+    job_id: str | None,
+    brand: str,
+    confidence: float,
+    source: str,
+    timestamp: float,
+    duration: float = 0.0,
+    content_type: str = "",
+    frame_ref: int | None = None,
+) -> None:
+    """Emit a brand detection event."""
+    if not job_id:
+        return
+    payload = {
+        "brand": brand,
+        "confidence": confidence,
+        "source": source,
+        "timestamp": timestamp,
+        "duration": duration,
+        "content_type": content_type,
+        "frame_ref": frame_ref,
+    }
+    push_detect_event(job_id, "detection", payload)
+
+
+def job_complete(job_id: str | None, report: dict) -> None:
+    """Emit a job_complete event with the final report."""
+    if not job_id:
+        return
+    payload = {"job_id": job_id, "report": report}
+    push_detect_event(job_id, "job_complete", payload)

detect/stages/frame_extractor.py

@@ -1,25 +1,41 @@
 """
 Stage 1 — Frame Extraction
 
-Extracts frames from a video at a configurable FPS using FFmpeg.
+Extracts frames from a video at a configurable FPS using the core ffmpeg module.
 Emits log + stats_update SSE events as it works.
 """
 
 from __future__ import annotations
 
-import subprocess
 import tempfile
 from pathlib import Path
 
+import ffmpeg
 import numpy as np
 from PIL import Image
 
 from core.ffmpeg.probe import probe_file
-from detect.events import push_detect_event
+from detect import emit
 from detect.models import Frame
 from detect.profiles.base import FrameExtractionConfig
 
 
+def _load_frames(tmpdir: Path, fps: float) -> list[Frame]:
+    """Load extracted JPEG files into Frame objects."""
+    frame_files = sorted(tmpdir.glob("frame_*.jpg"))
+    frames = []
+    for i, fpath in enumerate(frame_files):
+        img = Image.open(fpath)
+        frame = Frame(
+            sequence=i,
+            chunk_id=0,
+            timestamp=i / fps,
+            image=np.array(img),
+        )
+        frames.append(frame)
+    return frames
+
+
 def extract_frames(
     video_path: str,
     config: FrameExtractionConfig,
@@ -28,75 +44,37 @@ def extract_frames(
     """
     Extract frames from video at the configured FPS.
 
-    Uses FFmpeg to decode frames as raw images, then loads them
+    Uses ffmpeg-python to build the extraction pipeline,
-    as numpy arrays. Caps at config.max_frames.
+    outputs JPEG files to a temp dir, then loads as numpy arrays.
     """
     probe = probe_file(video_path)
     duration = probe.duration or 0.0
 
-    if job_id:
+    emit.log(job_id, "FrameExtractor", "INFO",
-        push_detect_event(job_id, "log", {
+             f"Starting extraction: {Path(video_path).name} "
-            "level": "INFO",
+             f"({duration:.1f}s, {probe.width}x{probe.height}, fps={config.fps})")
-            "stage": "FrameExtractor",
-            "msg": f"Starting extraction: {Path(video_path).name} "
-                   f"({duration:.1f}s, {probe.width}x{probe.height}, fps={config.fps})",
-        })
-
-    frames: list[Frame] = []
 
     with tempfile.TemporaryDirectory() as tmpdir:
         pattern = str(Path(tmpdir) / "frame_%06d.jpg")
 
-        cmd = [
+        stream = (
-            "ffmpeg", "-i", video_path,
+            ffmpeg
-            "-vf", f"fps={config.fps}",
+            .input(video_path)
-            "-q:v", "2",
+            .filter("fps", fps=config.fps)
-            "-frames:v", str(config.max_frames),
+            .output(pattern, qscale=2, frames=config.max_frames)
-            pattern,
+            .overwrite_output()
-            "-y", "-loglevel", "warning",
+        )
-        ]
 
-        result = subprocess.run(cmd, capture_output=True, text=True)
+        try:
+            stream.run(capture_stdout=True, capture_stderr=True, quiet=True)
+        except ffmpeg.Error as e:
+            stderr = e.stderr.decode() if e.stderr else "unknown error"
+            emit.log(job_id, "FrameExtractor", "ERROR", f"FFmpeg failed: {stderr[:200]}")
+            raise RuntimeError(f"FFmpeg failed: {stderr}") from e
 
-        if result.returncode != 0:
+        frames = _load_frames(Path(tmpdir), config.fps)
-            if job_id:
-                push_detect_event(job_id, "log", {
-                    "level": "ERROR",
-                    "stage": "FrameExtractor",
-                    "msg": f"FFmpeg failed: {result.stderr[:200]}",
-                })
-            raise RuntimeError(f"FFmpeg failed: {result.stderr}")
 
-        frame_files = sorted(Path(tmpdir).glob("frame_*.jpg"))
+    emit.log(job_id, "FrameExtractor", "INFO", f"Extracted {len(frames)} frames")
-
+    emit.stats(job_id, frames_extracted=len(frames))
-        for i, fpath in enumerate(frame_files):
-            img = Image.open(fpath)
-            arr = np.array(img)
-            timestamp = i / config.fps
-
-            frames.append(Frame(
-                sequence=i,
-                chunk_id=0,
-                timestamp=timestamp,
-                image=arr,
-            ))
-
-        if job_id:
-            push_detect_event(job_id, "log", {
-                "level": "INFO",
-                "stage": "FrameExtractor",
-                "msg": f"Extracted {len(frames)} frames",
-            })
-            push_detect_event(job_id, "stats_update", {
-                "frames_extracted": len(frames),
-                "frames_after_scene_filter": 0,
-                "regions_detected": 0,
-                "regions_resolved_by_ocr": 0,
-                "regions_escalated_to_local_vlm": 0,
-                "regions_escalated_to_cloud_llm": 0,
-                "cloud_llm_calls": 0,
-                "processing_time_seconds": 0.0,
-                "estimated_cloud_cost_usd": 0.0,
-            })
 
     return frames

detect/stages/scene_filter.py

@@ -0,0 +1,76 @@
+"""
+Stage 2 — Scene Filter
+
+Removes near-duplicate frames using perceptual hashing (pHash).
+Frames with a hamming distance below the threshold are considered
+duplicates and dropped. This dramatically reduces work for downstream
+CV stages without losing unique visual content.
+"""
+
+from __future__ import annotations
+
+import imagehash
+from PIL import Image
+
+from detect import emit
+from detect.models import Frame
+from detect.profiles.base import SceneFilterConfig
+
+
+def _compute_hashes(frames: list[Frame]) -> list[imagehash.ImageHash]:
+    """Compute perceptual hashes for all frames."""
+    hashes = []
+    for f in frames:
+        img = Image.fromarray(f.image)
+        h = imagehash.phash(img)
+        f.perceptual_hash = str(h)
+        hashes.append(h)
+    return hashes
+
+
+def _dedup(frames: list[Frame], hashes: list[imagehash.ImageHash], threshold: int) -> list[Frame]:
+    """Greedy dedup: keep a frame if it's sufficiently different from all kept frames."""
+    kept = [frames[0]]
+    kept_hashes = [hashes[0]]
+
+    for i in range(1, len(frames)):
+        is_duplicate = any(hashes[i] - kh < threshold for kh in kept_hashes)
+        if not is_duplicate:
+            kept.append(frames[i])
+            kept_hashes.append(hashes[i])
+
+    return kept
+
+
+def scene_filter(
+    frames: list[Frame],
+    config: SceneFilterConfig,
+    job_id: str | None = None,
+) -> list[Frame]:
+    """
+    Filter near-duplicate frames based on perceptual hash distance.
+
+    Keeps the first frame in each group of similar frames.
+    Returns a new list — does not mutate the input.
+    """
+    if not config.enabled:
+        emit.log(job_id, "SceneFilter", "INFO", "Scene filter disabled, passing all frames through")
+        return frames
+
+    if not frames:
+        return []
+
+    emit.log(job_id, "SceneFilter", "INFO",
+             f"Filtering {len(frames)} frames (hamming_threshold={config.hamming_threshold})")
+
+    hashes = _compute_hashes(frames)
+    kept = _dedup(frames, hashes, config.hamming_threshold)
+
+    dropped = len(frames) - len(kept)
+    pct = (dropped / len(frames) * 100) if frames else 0
+
+    emit.log(job_id, "SceneFilter", "INFO",
+             f"Kept {len(kept)} frames, dropped {dropped} ({pct:.0f}% reduction)")
+    emit.stats(job_id, frames_extracted=len(frames), frames_after_scene_filter=len(kept))
+
+    return kept

tests/detect/manual/push_logs.py

@@ -10,12 +10,16 @@ Opens: http://mpr.local.ar/detection/?job=<JOB_ID>
 
 import argparse
 import json
+import logging
 import random
 import time
 from datetime import datetime, timezone
 
 import redis
 
+logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
+logger = logging.getLogger(__name__)
+
 STAGES = ["FrameExtractor", "SceneFilter", "YOLODetector", "OCRStage", "BrandResolver", "VLMLocal", "Aggregator"]
 LEVELS = ["INFO", "INFO", "INFO", "INFO", "WARNING", "DEBUG", "ERROR"]  # weighted toward INFO
 MESSAGES = {
@@ -70,9 +74,9 @@ def main():
     r = redis.Redis(port=args.port, decode_responses=True)
     key = f"detect_events:{args.job}"
 
-    print(f"Pushing {args.count} log events to {key} (redis port {args.port})")
+    logger.info("Pushing %d log events to %s (redis port %d)", args.count, key, args.port)
-    print(f"Open: http://mpr.local.ar/detection/?job={args.job}")
+    logger.info("Open: http://mpr.local.ar/detection/?job=%s", args.job)
-    print()
+    input("\nPress Enter to start...")
 
     for i in range(args.count):
         stage = random.choice(STAGES)
@@ -88,10 +92,10 @@ def main():
         }
 
         r.rpush(key, json.dumps(event))
-        print(f"  {level:7s} {stage:16s} {msg[:60]}")
+        logger.log(getattr(logging, level, logging.INFO), "[%s] %s", stage, msg)
         time.sleep(args.delay)
 
-    print(f"\nDone. {args.count} events pushed.")
+    logger.info("Done. %d events pushed.", args.count)
 
 
 if __name__ == "__main__":

tests/detect/manual/push_pipeline.py

@@ -10,11 +10,15 @@ Opens: http://mpr.local.ar/detection/?job=<JOB_ID>
 
 import argparse
 import json
+import logging
 import time
 from datetime import datetime, timezone
 
 import redis
 
+logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
+logger = logging.getLogger(__name__)
+
 
 def ts():
     return datetime.now(timezone.utc).isoformat()
@@ -25,7 +29,7 @@ def push(r, key, event):
     r.rpush(key, json.dumps(event))
     etype = event["event"]
     detail = event.get("msg", event.get("stage", ""))
-    print(f"  [{etype:14s}] {detail}")
+    logger.info("[%s] %s", etype, detail)
     return event
 
 
@@ -39,12 +43,11 @@ def main():
     r = redis.Redis(port=args.port, decode_responses=True)
     key = f"detect_events:{args.job}"
 
-    # Clear previous events for this job
     r.delete(key)
 
-    print(f"Simulating pipeline run → {key}")
+    logger.info("Simulating pipeline run → %s", key)
-    print(f"Open: http://mpr.local.ar/detection/?job={args.job}")
+    logger.info("Open: http://mpr.local.ar/detection/?job=%s", args.job)
-    print()
+    input("\nPress Enter to start...")
 
     delay = args.delay
 
@@ -171,7 +174,7 @@ def main():
                        },
                    }})
 
-    print(f"\nPipeline simulation complete.")
+    logger.info("Pipeline simulation complete.")
 
 
 if __name__ == "__main__":

tests/detect/manual/run_extract_filter.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+"""
+Run FrameExtractor → SceneFilter on a real video and push events to Redis.
+
+Usage:
+    python tests/detect/manual/run_extract_filter.py [--job JOB_ID] [--port PORT]
+
+Opens: http://mpr.local.ar/detection/?job=<JOB_ID>
+"""
+
+import argparse
+import logging
+import os
+import sys
+
+# Parse args early so we can set REDIS_URL before imports
+parser = argparse.ArgumentParser()
+parser.add_argument("--job", default="extract-filter-test")
+parser.add_argument("--port", type=int, default=6382)
+args = parser.parse_args()
+
+os.environ["REDIS_URL"] = f"redis://localhost:{args.port}/0"
+logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
+
+sys.path.insert(0, ".")
+
+from detect.profiles.soccer import SoccerBroadcastProfile
+from detect.stages.frame_extractor import extract_frames
+from detect.stages.scene_filter import scene_filter
+
+logger = logging.getLogger(__name__)
+
+VIDEO = "media/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4"
+
+
+def main():
+    profile = SoccerBroadcastProfile()
+
+    logger.info("Job: %s", args.job)
+    logger.info("Open: http://mpr.local.ar/detection/?job=%s", args.job)
+    input("\nPress Enter to start...")
+
+    # Stage 1: Extract frames
+    extract_config = profile.frame_extraction_config()
+    extract_config.max_frames = 30
+    logger.info("Extracting frames (fps=%s, max=%d)...", extract_config.fps, extract_config.max_frames)
+    frames = extract_frames(VIDEO, extract_config, job_id=args.job)
+    logger.info("  → %d frames extracted", len(frames))
+
+    # Stage 2: Scene filter
+    filter_config = profile.scene_filter_config()
+    logger.info("Filtering scenes (hamming_threshold=%d)...", filter_config.hamming_threshold)
+    kept = scene_filter(frames, filter_config, job_id=args.job)
+    logger.info("  → %d frames kept (%d dropped)", len(kept), len(frames) - len(kept))
+
+    logger.info("Done.")
+
+
+if __name__ == "__main__":
+    main()

tests/detect/manual/test_frame_extractor_e2e.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+"""
+End-to-end test: run FrameExtractor and verify SSE events are emitted.
+
+Usage:
+    python tests/detect/manual/test_frame_extractor_e2e.py
+
+Requires Redis running. Events appear at: http://mpr.local.ar/detection/?job=e2e-test
+"""
+
+import logging
+import sys
+
+sys.path.insert(0, ".")
+
+from detect.profiles.soccer import SoccerBroadcastProfile
+from detect.stages.frame_extractor import extract_frames
+
+logger = logging.getLogger(__name__)
+
+VIDEO = "media/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4"
+JOB_ID = "e2e-test"
+
+profile = SoccerBroadcastProfile()
+config = profile.frame_extraction_config()
+config.max_frames = 20
+
+logger.info("Extracting frames from %s (fps=%s, max=%d)", VIDEO, config.fps, config.max_frames)
+logger.info("Open: http://mpr.local.ar/detection/?job=%s", JOB_ID)
+input("\nPress Enter to start...")
+
+frames = extract_frames(VIDEO, config, job_id=JOB_ID)
+logger.info("Done: %d frames extracted", len(frames))
+logger.info("Open http://mpr.local.ar/detection/?job=%s to see the events", JOB_ID)

tests/detect/test_frame_extractor.py

@@ -61,7 +61,7 @@ def test_extract_frames_with_events(monkeypatch):
     def mock_push(job_id, event_type, data):
         events.append((job_id, event_type, data))
 
-    monkeypatch.setattr("detect.stages.frame_extractor.push_detect_event", mock_push)
+    monkeypatch.setattr("detect.emit.push_detect_event", mock_push)
 
     video = _get_sample_video()
     config = FrameExtractionConfig(fps=1, max_frames=5)

tests/detect/test_frame_extractor_e2e.py

@@ -1,27 +0,0 @@
-"""
-End-to-end test: run FrameExtractor and verify SSE events are emitted.
-
-Usage (manual):
-    python tests/detect/test_frame_extractor_e2e.py
-
-Requires Redis running on localhost:6381.
-Push events will appear at: http://mpr.local.ar/detection/?job=e2e-test
-"""
-
-import sys
-sys.path.insert(0, ".")
-
-from detect.profiles.soccer import SoccerBroadcastProfile
-from detect.stages.frame_extractor import extract_frames
-
-VIDEO = "media/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4"
-JOB_ID = "e2e-test"
-
-profile = SoccerBroadcastProfile()
-config = profile.frame_extraction_config()
-config.max_frames = 20  # keep it quick
-
-print(f"Extracting frames from {VIDEO} (fps={config.fps}, max={config.max_frames})")
-frames = extract_frames(VIDEO, config, job_id=JOB_ID)
-print(f"Done: {len(frames)} frames extracted")
-print(f"Open http://mpr.local.ar/detection/?job={JOB_ID} to see the events")

tests/detect/test_scene_filter.py

@@ -0,0 +1,84 @@
+"""Tests for SceneFilter stage."""
+
+import numpy as np
+import pytest
+
+from detect.models import Frame
+from detect.profiles.base import SceneFilterConfig
+from detect.stages.scene_filter import scene_filter
+
+
+def _make_frame(seq: int, color: tuple[int, int, int] = (128, 128, 128)) -> Frame:
+    """Create a solid-color test frame."""
+    img = np.full((64, 64, 3), color, dtype=np.uint8)
+    return Frame(sequence=seq, chunk_id=0, timestamp=seq * 0.5, image=img)
+
+
+def test_identical_frames_deduped():
+    frames = [_make_frame(i) for i in range(10)]
+    config = SceneFilterConfig(hamming_threshold=8)
+    kept = scene_filter(frames, config)
+
+    # All identical → only first survives
+    assert len(kept) == 1
+    assert kept[0].sequence == 0
+
+
+def _make_noisy_frame(seq: int, seed: int) -> Frame:
+    """Create a frame with random noise — each is visually unique."""
+    rng = np.random.RandomState(seed)
+    img = rng.randint(0, 256, (64, 64, 3), dtype=np.uint8)
+    return Frame(sequence=seq, chunk_id=0, timestamp=seq * 0.5, image=img)
+
+
+def test_different_frames_kept():
+    frames = [_make_noisy_frame(i, seed=i * 1000) for i in range(5)]
+    config = SceneFilterConfig(hamming_threshold=8)
+    kept = scene_filter(frames, config)
+
+    # Random noise frames are visually distinct → most should survive
+    assert len(kept) >= 3
+
+
+def test_disabled_passes_all():
+    frames = [_make_frame(i) for i in range(5)]
+    config = SceneFilterConfig(enabled=False)
+    kept = scene_filter(frames, config)
+
+    assert len(kept) == 5
+
+
+def test_empty_input():
+    config = SceneFilterConfig(hamming_threshold=8)
+    kept = scene_filter([], config)
+    assert kept == []
+
+
+def test_single_frame():
+    frames = [_make_frame(0)]
+    config = SceneFilterConfig(hamming_threshold=8)
+    kept = scene_filter(frames, config)
+    assert len(kept) == 1
+
+
+def test_hashes_populated():
+    frames = [_make_frame(i, color=(i * 50, 100, 200)) for i in range(3)]
+    config = SceneFilterConfig(hamming_threshold=8)
+    scene_filter(frames, config)
+
+    for f in frames:
+        assert f.perceptual_hash != ""
+
+
+def test_events_emitted(monkeypatch):
+    events = []
+    monkeypatch.setattr("detect.emit.push_detect_event",
+                        lambda job_id, etype, data: events.append((etype, data)))
+
+    frames = [_make_frame(i) for i in range(5)]
+    config = SceneFilterConfig(hamming_threshold=8)
+    scene_filter(frames, config, job_id="test-job")
+
+    event_types = [e[0] for e in events]
+    assert "log" in event_types
+    assert "stats_update" in event_types

ui/detection-app/Dockerfile

@@ -9,6 +9,7 @@ COPY framework/ ./framework/
 COPY detection-app/ ./detection-app/
 
 WORKDIR /ui/detection-app
+ENV CI=true
 RUN pnpm install
 
 EXPOSE 5175

ui/detection-app/src/App.vue

@@ -3,6 +3,7 @@ import { ref } from 'vue'
 import { SSEDataSource, Panel, LayoutGrid } from 'mpr-ui-framework'
 import 'mpr-ui-framework/src/tokens.css'
 import LogPanel from './panels/LogPanel.vue'
+import FunnelPanel from './panels/FunnelPanel.vue'
 import type { StatsUpdate } from './types/sse-contract'
 
 const jobId = ref(new URLSearchParams(window.location.search).get('job') || 'test-job')
@@ -39,7 +40,7 @@ source.connect()
       <span class="job-id">job: {{ jobId }}</span>
     </header>
 
-    <LayoutGrid :columns="2" :rows="1" gap="var(--space-2)">
+    <LayoutGrid :columns="2" :rows="2" gap="var(--space-2)">
       <Panel title="Stats" :status="status">
         <div class="stats" v-if="stats">
           <div class="stat" v-for="s in [
@@ -57,6 +58,8 @@ source.connect()
         <div v-else class="empty">Waiting for stats...</div>
       </Panel>
 
+      <FunnelPanel :source="source" :status="status" />
+
       <LogPanel :source="source" :status="status" />
     </LayoutGrid>
   </div>

ui/detection-app/src/panels/FunnelPanel.vue

@@ -0,0 +1,56 @@
+<script setup lang="ts">
+import { ref, computed } from 'vue'
+import { Panel } from 'mpr-ui-framework'
+import TimeSeriesRenderer from 'mpr-ui-framework/src/renderers/TimeSeriesRenderer.vue'
+import type { DataSource } from 'mpr-ui-framework'
+import type { StatsUpdate } from '../types/sse-contract'
+
+const props = defineProps<{
+  source: DataSource
+  status?: 'idle' | 'live' | 'processing' | 'error'
+}>()
+
+// Accumulate stats snapshots over time
+const snapshots = ref<{ ts: number; stats: StatsUpdate }[]>([])
+const startTime = Date.now() / 1000
+
+props.source.on<StatsUpdate>('stats_update', (e) => {
+  snapshots.value.push({ ts: Date.now() / 1000 - startTime, stats: e })
+})
+
+const series = [
+  { label: 'Frames', color: '#4f9cf9' },
+  { label: 'After filter', color: '#3ecf8e' },
+  { label: 'Regions', color: '#f5a623' },
+  { label: 'OCR resolved', color: '#a78bfa' },
+]
+
+const chartData = computed(() => {
+  const timestamps = snapshots.value.map((s) => s.ts)
+  const frames = snapshots.value.map((s) => s.stats.frames_extracted)
+  const filtered = snapshots.value.map((s) => s.stats.frames_after_scene_filter)
+  const regions = snapshots.value.map((s) => s.stats.regions_detected)
+  const ocr = snapshots.value.map((s) => s.stats.regions_resolved_by_ocr)
+  return [timestamps, frames, filtered, regions, ocr] as const
+})
+</script>
+
+<template>
+  <Panel title="Processing Funnel" :status="status">
+    <TimeSeriesRenderer
+      v-if="snapshots.length > 0"
+      :series="series"
+      :data="chartData"
+      :stacked="true"
+    />
+    <div v-else class="empty">Waiting for stats...</div>
+  </Panel>
+</template>
+
+<style scoped>
+.empty {
+  color: var(--text-dim);
+  padding: var(--space-6);
+  text-align: center;
+}
+</style>

ui/framework/package.json

@@ -11,7 +11,8 @@
   },
   "dependencies": {
     "vue": "^3.5",
-    "pinia": "^2.2"
+    "pinia": "^2.2",
+    "uplot": "^1.6"
   },
   "devDependencies": {
     "typescript": "^5.6",

ui/framework/pnpm-lock.yaml

@@ -11,6 +11,9 @@ importers:
       pinia:
         specifier: ^2.2
         version: 2.3.1(typescript@5.9.3)(vue@3.5.30(typescript@5.9.3))
+      uplot:
+        specifier: ^1.6
+        version: 1.6.32
       vue:
         specifier: ^3.5
         version: 3.5.30(typescript@5.9.3)
@@ -748,6 +751,9 @@ packages:
     engines: {node: '>=14.17'}
     hasBin: true
 
+  uplot@1.6.32:
+    resolution: {integrity: sha512-KIMVnG68zvu5XXUbC4LQEPnhwOxBuLyW1AHtpm6IKTXImkbLgkMy+jabjLgSLMasNuGGzQm/ep3tOkyTxpiQIw==}
+
   vite-node@2.1.9:
     resolution: {integrity: sha512-AM9aQ/IPrW/6ENLQg3AGY4K1N2TGZdR5e4gu/MmmR2xR3Ll1+dib+nook92g4TV3PXVyeyxdWwtaCAiUL0hMxA==}
     engines: {node: ^18.0.0 || >=20.0.0}
@@ -1460,6 +1466,8 @@ snapshots:
 
   typescript@5.9.3: {}
 
+  uplot@1.6.32: {}
+
   vite-node@2.1.9:
     dependencies:
       cac: 6.7.14

ui/framework/src/index.ts

@@ -10,3 +10,4 @@ export { default as LayoutGrid } from './components/LayoutGrid.vue'
 
 // Renderers
 export { default as LogRenderer } from './renderers/LogRenderer.vue'
+export { default as TimeSeriesRenderer } from './renderers/TimeSeriesRenderer.vue'

ui/framework/src/renderers/TimeSeriesRenderer.vue

@@ -0,0 +1,101 @@
+<script setup lang="ts">
+import { ref, onMounted, onUnmounted, watch, nextTick } from 'vue'
+import uPlot from 'uplot'
+import 'uplot/dist/uPlot.min.css'
+
+export interface TimeSeriesSeries {
+  label: string
+  color: string
+}
+
+const props = withDefaults(defineProps<{
+  /** Array of series configs (label + color) */
+  series: TimeSeriesSeries[]
+  /** Data: [timestamps[], series1[], series2[], ...] */
+  data: uPlot.AlignedData
+  /** Chart title (optional) */
+  title?: string
+  /** Stacked area mode */
+  stacked?: boolean
+}>(), {
+  stacked: false,
+})
+
+const container = ref<HTMLElement | null>(null)
+let chart: uPlot | null = null
+
+function buildOpts(): uPlot.Options {
+  const seriesOpts: uPlot.Series[] = [
+    { label: 'Time' },
+    ...props.series.map((s) => ({
+      label: s.label,
+      stroke: s.color,
+      fill: props.stacked ? s.color + '40' : undefined,
+      width: 2,
+    })),
+  ]
+
+  return {
+    width: container.value?.clientWidth ?? 400,
+    height: container.value?.clientHeight ?? 200,
+    series: seriesOpts,
+    axes: [
+      { stroke: '#555568', grid: { stroke: '#2e2e3822' } },
+      { stroke: '#555568', grid: { stroke: '#2e2e3822' } },
+    ],
+    cursor: { show: true },
+    legend: { show: true },
+  }
+}
+
+function createChart() {
+  if (!container.value) return
+  if (chart) chart.destroy()
+  chart = new uPlot(buildOpts(), props.data, container.value)
+}
+
+function resize() {
+  if (!chart || !container.value) return
+  chart.setSize({
+    width: container.value.clientWidth,
+    height: container.value.clientHeight,
+  })
+}
+
+watch(() => props.data, (newData) => {
+  if (chart) {
+    chart.setData(newData)
+  } else {
+    nextTick(createChart)
+  }
+}, { deep: true })
+
+onMounted(() => {
+  nextTick(createChart)
+  const observer = new ResizeObserver(resize)
+  if (container.value) observer.observe(container.value)
+  onUnmounted(() => {
+    observer.disconnect()
+    chart?.destroy()
+    chart = null
+  })
+})
+</script>
+
+<template>
+  <div ref="container" class="timeseries-renderer" />
+</template>
+
+<style scoped>
+.timeseries-renderer {
+  width: 100%;
+  height: 100%;
+  min-height: 150px;
+}
+
+.timeseries-renderer :deep(.u-legend) {
+  font-family: var(--font-mono);
+  font-size: var(--font-size-sm);
+  color: var(--text-secondary);
+}
+</style>