diff --git a/.gitignore b/.gitignore
index 1678912d0019cdb52dffcdf9dd0c974d301f8d00..82c624f653fa030a76e91673e048952a94edce3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,8 @@ models/
*.pt
*.onnx
*.engine
+CLAUDE.md
+*.ts
+.claude
+
+design_previews/
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e66603e8ac1a2a6c3df22c8835ed1b09c6b22e3
--- /dev/null
+++ b/app.py
@@ -0,0 +1,1324 @@
+from __future__ import annotations
+
+import json
+import html
+import os
+import shutil
+from pathlib import Path
+from typing import Any
+
+import gradio as gr
+
+from tiny_trigger.actions import dispatch_events
+from tiny_trigger.automation import evaluate_video_detections, load_automation_text
+from tiny_trigger.detector import parse_class_prompt
+from tiny_trigger.llm import compile_automation_with_llamacpp
+from tiny_trigger.store import (
+ EVENTS_PATH,
+ RuntimeState,
+ append_events,
+ load_local_config,
+ load_runtime_state,
+ load_saved_automations,
+ save_automations,
+ save_runtime_state,
+)
+from tiny_trigger.video import process_video, render_automation_video
+
+
+LOCAL_CONFIG = load_local_config()
+DEFAULT_CLASSES = os.getenv(
+ "TINY_TRIGGER_CLASSES",
+ LOCAL_CONFIG.default_classes or "person, wheel, clothes, chair, monitor, steering wheel, bag, sticker",
+)
+DEFAULT_MODEL = os.getenv("TINY_TRIGGER_YOLOE_MODEL", LOCAL_CONFIG.default_detector_model or "yoloe-26s-seg.pt")
+DEFAULT_DEVICE = os.getenv("TINY_TRIGGER_DEVICE", LOCAL_CONFIG.default_device or "auto")
+DEFAULT_IMAGE_SIZE = int(os.getenv("TINY_TRIGGER_IMAGE_SIZE", LOCAL_CONFIG.default_image_size or 960))
+DEFAULT_MAX_DETECTIONS = int(os.getenv("TINY_TRIGGER_MAX_DETECTIONS", LOCAL_CONFIG.default_max_detections or 100))
+DEFAULT_WEBHOOK_URL = os.getenv("TINY_TRIGGER_WEBHOOK_URL", LOCAL_CONFIG.webhook_url or "")
+DEFAULT_LLAMACPP_BASE_URL = os.getenv(
+ "TINY_TRIGGER_LLAMACPP_BASE_URL",
+ LOCAL_CONFIG.llamacpp_base_url or "http://127.0.0.1:8080/v1",
+)
+DEFAULT_LLAMACPP_MODEL = os.getenv(
+ "TINY_TRIGGER_LLAMACPP_MODEL",
+ LOCAL_CONFIG.llamacpp_model or "ggml-org/Qwen3-1.7B-GGUF:Q4_K_M",
+)
+
+
+def _ensure_ffmpeg_available() -> None:
+ if shutil.which("ffmpeg") and shutil.which("ffprobe"):
+ return
+
+ try:
+ import static_ffmpeg
+
+ static_ffmpeg.add_paths()
+ except ImportError:
+ pass
+ if shutil.which("ffmpeg") and shutil.which("ffprobe"):
+ return
+
+ try:
+ import imageio_ffmpeg
+ except ImportError:
+ return
+
+ ffmpeg_exe = Path(imageio_ffmpeg.get_ffmpeg_exe())
+ local_bin = Path(".local") / "bin"
+ local_bin.mkdir(parents=True, exist_ok=True)
+ ffmpeg_link = local_bin / ("ffmpeg.exe" if os.name == "nt" else "ffmpeg")
+ if not ffmpeg_link.exists():
+ try:
+ ffmpeg_link.symlink_to(ffmpeg_exe)
+ except OSError:
+ shutil.copy2(ffmpeg_exe, ffmpeg_link)
+ ffmpeg_link.chmod(0o755)
+ os.environ["PATH"] = f"{local_bin.resolve()}{os.pathsep}{os.environ.get('PATH', '')}"
+
+
+_ensure_ffmpeg_available()
+
+APP_CSS = """
+:root {
+ --tt-bg: #f6f7f8;
+ --tt-surface: #ffffff;
+ --tt-surface-soft: #eef2f5;
+ --tt-text: #17202a;
+ --tt-muted: #687484;
+ --tt-border: #dce1e7;
+ --tt-border-strong: #cbd3dd;
+ --tt-red: #d93a2f;
+ --tt-red-dark: #b92f27;
+ --tt-teal: #109f8f;
+ --tt-blue: #3569d4;
+ --tt-lime: #d5ef4a;
+ --tt-shadow: 0 20px 45px rgba(31, 41, 55, 0.08);
+ --tt-radius: 8px;
+}
+
+body,
+.gradio-container {
+ background:
+ linear-gradient(180deg, rgba(16, 159, 143, 0.08), rgba(246, 247, 248, 0) 310px),
+ var(--tt-bg) !important;
+ color: var(--tt-text) !important;
+ font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif !important;
+ letter-spacing: 0 !important;
+}
+
+.gradio-container {
+ max-width: 1440px !important;
+ margin: 0 auto !important;
+ padding: 22px 24px 32px !important;
+}
+
+footer {
+ display: none !important;
+}
+
+.tt-hero {
+ display: flex;
+ align-items: flex-start;
+ justify-content: space-between;
+ gap: 18px;
+ margin-bottom: 18px;
+}
+
+.tt-kicker {
+ margin: 0 0 5px;
+ color: var(--tt-muted);
+ font-size: 12px;
+ font-weight: 800;
+ letter-spacing: 0.08em;
+ text-transform: uppercase;
+}
+
+.tt-title {
+ margin: 0;
+ color: var(--tt-text);
+ font-size: 31px;
+ line-height: 1.05;
+ font-weight: 820;
+}
+
+.tt-subtitle {
+ margin: 8px 0 0;
+ color: var(--tt-muted);
+ font-size: 14px;
+}
+
+.tt-chips {
+ display: flex;
+ flex-wrap: wrap;
+ justify-content: flex-end;
+ gap: 9px;
+}
+
+.tt-chip {
+ display: inline-flex;
+ align-items: center;
+ min-height: 31px;
+ padding: 0 12px;
+ border: 1px solid var(--tt-border);
+ border-radius: 999px;
+ background: rgba(255, 255, 255, 0.76);
+ color: var(--tt-text);
+ font-size: 12px;
+ font-weight: 800;
+ white-space: nowrap;
+ box-shadow: 0 8px 20px rgba(31, 41, 55, 0.04);
+}
+
+.tt-chip.live {
+ border-color: transparent;
+ background: var(--tt-teal);
+ color: #ffffff;
+}
+
+.tt-grid {
+ gap: 16px !important;
+ align-items: stretch !important;
+}
+
+.tt-panel {
+ border: 1px solid var(--tt-border) !important;
+ border-radius: var(--tt-radius) !important;
+ background: var(--tt-surface) !important;
+ color: var(--tt-text) !important;
+ box-shadow: var(--tt-shadow) !important;
+ overflow: hidden !important;
+}
+
+.tt-panel > div,
+.tt-panel .form,
+.tt-panel .block,
+.tt-panel .wrap,
+.tt-panel .contain,
+.tt-video-panel > div,
+.tt-data-panel > div {
+ background: var(--tt-surface) !important;
+ color: var(--tt-text) !important;
+}
+
+.tt-panel > .form,
+.tt-panel .form {
+ border: 0 !important;
+ background: transparent !important;
+}
+
+.tt-panel-title h2,
+.tt-panel-title h3,
+.tt-section h2,
+.tt-section h3 {
+ margin: 0 !important;
+ color: var(--tt-muted) !important;
+ font-size: 12px !important;
+ font-weight: 850 !important;
+ letter-spacing: 0.08em !important;
+ line-height: 1.2 !important;
+ text-transform: uppercase !important;
+}
+
+.tt-panel-title {
+ margin-bottom: 10px !important;
+}
+
+.tt-panel .block,
+.tt-panel .block.padded {
+ border-color: var(--tt-border) !important;
+ border-radius: 7px !important;
+}
+
+.tt-panel label,
+.tt-panel .label-wrap,
+.tt-panel .wrap label,
+.tt-panel .block-label {
+ color: var(--tt-muted) !important;
+ font-size: 12px !important;
+ font-weight: 780 !important;
+}
+
+.tt-panel textarea,
+.tt-panel input,
+.tt-panel select {
+ color: var(--tt-text) !important;
+ background: #ffffff !important;
+}
+
+.tt-panel textarea,
+.tt-panel input {
+ border-color: var(--tt-border-strong) !important;
+}
+
+.tt-panel .secondary,
+.tt-panel button.secondary {
+ border: 1px solid var(--tt-border) !important;
+ background: var(--tt-surface-soft) !important;
+ color: var(--tt-text) !important;
+}
+
+.tt-primary button,
+.tt-panel button.primary,
+button.primary {
+ border: 0 !important;
+ background: var(--tt-red) !important;
+ color: #ffffff !important;
+ font-weight: 820 !important;
+}
+
+.tt-panel button.primary:hover,
+button.primary:hover {
+ background: var(--tt-red-dark) !important;
+}
+
+.tt-run-row {
+ gap: 10px !important;
+ align-items: stretch !important;
+ flex-wrap: nowrap !important;
+}
+
+.tt-run-row button,
+.tt-panel button {
+ min-height: 40px !important;
+ border-radius: 7px !important;
+ padding: 0 14px !important;
+ font-size: 13px !important;
+ font-weight: 820 !important;
+ line-height: 1.1 !important;
+ white-space: nowrap !important;
+}
+
+.tt-run-row button {
+ width: 100% !important;
+}
+
+.tt-run-row .form,
+.tt-run-row > div {
+ flex: 1 1 0 !important;
+ min-width: 0 !important;
+}
+
+.gradio-container button[role="tab"] {
+ min-height: 36px !important;
+ border: 1px solid var(--tt-border) !important;
+ border-radius: 7px !important;
+ background: rgba(255, 255, 255, 0.78) !important;
+ color: var(--tt-text) !important;
+ font-size: 13px !important;
+ font-weight: 820 !important;
+ box-shadow: none !important;
+}
+
+.gradio-container button[role="tab"][aria-selected="true"] {
+ border-color: transparent !important;
+ background: var(--tt-blue) !important;
+ color: #ffffff !important;
+}
+
+.tt-video-panel {
+ border: 1px solid var(--tt-border) !important;
+ border-radius: var(--tt-radius) !important;
+ background: var(--tt-surface) !important;
+ box-shadow: var(--tt-shadow) !important;
+ overflow: hidden !important;
+}
+
+.tt-video-panel .tabs,
+.tt-data-panel .tabs,
+.tt-automation-panel .tabs {
+ border: 0 !important;
+}
+
+.tt-video-panel .tab-nav,
+.tt-data-panel .tab-nav,
+.tt-automation-panel .tab-nav {
+ gap: 7px !important;
+ padding: 0 !important;
+ border: 0 !important;
+}
+
+.tt-video-panel button[role="tab"],
+.tt-data-panel button[role="tab"],
+.tt-automation-panel button[role="tab"] {
+ min-height: 34px !important;
+ border: 1px solid var(--tt-border) !important;
+ border-radius: 7px !important;
+ background: var(--tt-surface-soft) !important;
+ color: var(--tt-text) !important;
+ font-size: 12px !important;
+ font-weight: 820 !important;
+}
+
+.tt-video-panel button[role="tab"][aria-selected="true"],
+.tt-data-panel button[role="tab"][aria-selected="true"],
+.tt-automation-panel button[role="tab"][aria-selected="true"] {
+ background: var(--tt-blue) !important;
+ color: #ffffff !important;
+}
+
+.tt-result-video {
+ min-height: 430px !important;
+}
+
+.tt-result-video video {
+ border-radius: 8px !important;
+ background:
+ linear-gradient(90deg, rgba(23, 32, 42, 0.07) 1px, transparent 1px),
+ linear-gradient(0deg, rgba(23, 32, 42, 0.07) 1px, transparent 1px),
+ #edf1f5 !important;
+ background-size: 48px 48px, 48px 48px, 100% 100% !important;
+}
+
+.tt-status-grid {
+ gap: 10px !important;
+}
+
+.tt-status-card {
+ min-height: 82px;
+ padding: 13px 14px;
+ border: 1px solid var(--tt-border);
+ border-radius: var(--tt-radius);
+ background: #ffffff;
+ box-shadow: inset 0 0 0 1px rgba(255,255,255,0.4);
+}
+
+.tt-status-card strong {
+ display: block;
+ margin-bottom: 4px;
+ color: var(--tt-text);
+ font-size: 14px;
+ line-height: 1.2;
+}
+
+.tt-status-card span {
+ display: block;
+ color: var(--tt-muted);
+ font-size: 12px;
+ line-height: 1.35;
+}
+
+.tt-status-card.success {
+ border-color: rgba(16, 159, 143, 0.35);
+ background: rgba(16, 159, 143, 0.08);
+}
+
+.tt-status-card.action {
+ border-color: rgba(217, 58, 47, 0.32);
+ background: rgba(217, 58, 47, 0.09);
+}
+
+.tt-status-card.warning {
+ border-color: rgba(213, 160, 20, 0.38);
+ background: rgba(213, 160, 20, 0.11);
+}
+
+.tt-status-card.idle {
+ background: #f8fafc;
+}
+
+.tt-label-audit {
+ display: grid;
+ gap: 10px;
+ padding: 13px 14px;
+ border: 1px solid rgba(16, 159, 143, 0.28);
+ border-radius: var(--tt-radius);
+ background: rgba(16, 159, 143, 0.07);
+}
+
+.tt-label-audit strong {
+ display: block;
+ color: var(--tt-text);
+ font-size: 14px;
+ line-height: 1.2;
+}
+
+.tt-label-audit span {
+ color: var(--tt-muted);
+ font-size: 12px;
+}
+
+.tt-chip-row {
+ display: flex;
+ flex-wrap: wrap;
+ gap: 8px;
+}
+
+.tt-flow-copy {
+ margin: 0 0 12px;
+ color: var(--tt-muted);
+ font-size: 13px;
+ line-height: 1.45;
+}
+
+.tt-code textarea,
+.tt-code code,
+.tt-code pre {
+ font-family: "SFMono-Regular", Consolas, "Liberation Mono", monospace !important;
+ font-size: 12px !important;
+ line-height: 1.45 !important;
+}
+
+.tt-data-panel {
+ border: 1px solid var(--tt-border) !important;
+ border-radius: var(--tt-radius) !important;
+ background: var(--tt-surface) !important;
+ box-shadow: var(--tt-shadow) !important;
+ overflow: hidden !important;
+}
+
+.tt-data-panel table {
+ font-size: 13px !important;
+}
+
+.tt-data-panel th {
+ color: var(--tt-muted) !important;
+ font-size: 11px !important;
+ font-weight: 820 !important;
+ text-transform: uppercase !important;
+}
+
+.tt-compact textarea {
+ min-height: 84px !important;
+}
+
+.tt-demo-state {
+ margin-top: 10px;
+}
+
+@media (max-width: 980px) {
+ .gradio-container {
+ padding: 16px !important;
+ }
+
+ .tt-hero {
+ flex-direction: column;
+ }
+
+ .tt-chips {
+ justify-content: flex-start;
+ }
+
+ .tt-result-video {
+ min-height: 300px !important;
+ }
+}
+"""
+
+FALLBACK_RULES = """rules:
+ - name: person-near-steering-wheel
+ when:
+ all:
+ - present: {label: person, min_count: 1}
+ - near: {a: person, b: steering wheel, max_distance: 0.45}
+ gate:
+ enabled: true
+ cooldown: {key: turn-on-pc, minutes: 5}
+ then:
+ - type: webhook
+ name: turn on pc
+"""
+SAVED_AUTOMATIONS = load_saved_automations()
+DEFAULT_RULES = (
+ SAVED_AUTOMATIONS.model_dump_json(by_alias=True, indent=2)
+ if SAVED_AUTOMATIONS
+ else FALLBACK_RULES
+)
+
+
+def _detection_rows(detections: list[Any]) -> list[list[Any]]:
+ return [
+ [
+ det.frame_index,
+ round(det.timestamp_sec, 2),
+ det.label,
+ round(det.confidence, 3),
+ [round(value, 1) for value in det.bbox_xyxy],
+ [round(value, 3) for value in det.bbox_xyxy_norm],
+ ]
+ for det in detections
+ ]
+
+
+def _event_rows(events: list[Any]) -> list[list[Any]]:
+ return [
+ [
+ event.rule,
+ event.action,
+ event.type,
+ event.status,
+ event.frame_index,
+ round(event.timestamp_sec, 2),
+ event.response_status or "",
+ event.error or "",
+ ]
+ for event in events
+ ]
+
+
+def _result_json(result: Any) -> dict[str, Any]:
+ return json.loads(result.model_dump_json())
+
+
+def _status_card(tone: str, title: str, body: str) -> str:
+ return f"""
+
+ {title}
+ {body}
+
+"""
+
+
+def _rules_json(document: Any) -> dict[str, Any]:
+ return json.loads(document.model_dump_json(by_alias=True))
+
+
+def _automation_labels_from_document(document: Any) -> list[str]:
+ labels: list[str] = []
+ for rule in document.rules:
+ for condition in [*rule.when.all_conditions, *rule.when.any_conditions]:
+ if condition.present:
+ labels.append(condition.present.label)
+ if condition.count:
+ labels.append(condition.count.label)
+ if condition.near:
+ labels.extend([condition.near.a, condition.near.b])
+ return parse_class_prompt(labels)
+
+
+def _automation_labels_from_text(rules_text: str) -> list[str]:
+ return _automation_labels_from_document(load_automation_text(rules_text))
+
+
+def _automation_labels_from_rule(rule: Any) -> list[str]:
+ labels: list[str] = []
+ for condition in [*rule.when.all_conditions, *rule.when.any_conditions]:
+ if condition.present:
+ labels.append(condition.present.label)
+ if condition.count:
+ labels.append(condition.count.label)
+ if condition.near:
+ labels.extend([condition.near.a, condition.near.b])
+ return parse_class_prompt(labels)
+
+
+def _labels_summary(labels: list[str]) -> str:
+ if not labels:
+ return "No detector labels found in the active automations."
+ return ", ".join(labels)
+
+
+def _labels_card(labels: list[str]) -> str:
+ chips = "".join(f"{html.escape(label)}" for label in labels)
+ chips = chips or "No labels"
+ return f"""
+
+
+ Detector audit
+ {len(labels)} label{'s' if len(labels) != 1 else ''} derived from active automations
+
+
{chips}
+
+"""
+
+
+def _condition_summary(rule: Any) -> str:
+ parts: list[str] = []
+ for condition in [*rule.when.all_conditions, *rule.when.any_conditions]:
+ if condition.present:
+ parts.append(f"present {condition.present.label}")
+ if condition.count:
+ parts.append(f"{condition.count.label} >= {condition.count.minimum}")
+ if condition.near:
+ parts.append(f"{condition.near.a} near {condition.near.b}")
+ if condition.cooldown:
+ duration = condition.cooldown.minutes or condition.cooldown.seconds or 0
+ unit = "min" if condition.cooldown.minutes is not None else "sec"
+ parts.append(f"cooldown {duration:g}{unit}")
+ return " + ".join(parts)
+
+
+def _action_summary(rule: Any) -> str:
+ return ", ".join(f"{action.type}: {action.name}" for action in rule.then)
+
+
+def _rule_rows(document: Any) -> list[list[Any]]:
+ rows: list[list[Any]] = []
+ for rule in document.rules:
+ rows.append(
+ [
+ "on" if rule.gate.enabled else "off",
+ rule.name,
+ _labels_summary(_automation_labels_from_rule(rule)),
+ _condition_summary(rule),
+ _action_summary(rule),
+ ]
+ )
+ return rows
+
+
+def _rule_names(document: Any) -> list[str]:
+ return [rule.name for rule in document.rules]
+
+
+def _rule_enabled(document: Any, rule_name: str | None) -> bool:
+ for rule in document.rules:
+ if rule.name == rule_name:
+ return rule.gate.enabled
+ return False
+
+
+def _rules_text_from_data(data: dict[str, Any]) -> str:
+ document = load_automation_text(json.dumps(data))
+ return document.model_dump_json(by_alias=True, indent=2)
+
+
+def _merge_generated_rules(existing_rules_text: str, generated_document: Any) -> Any:
+ try:
+ existing_data = _rules_json(load_automation_text(existing_rules_text))
+ existing_rules = existing_data["rules"]
+ except Exception:
+ existing_rules = []
+
+ generated_rules = _rules_json(generated_document)["rules"]
+ merged_by_name: dict[str, dict[str, Any]] = {rule["name"]: rule for rule in existing_rules}
+ ordered_names = [rule["name"] for rule in existing_rules]
+ for rule in generated_rules:
+ if rule["name"] not in merged_by_name:
+ ordered_names.append(rule["name"])
+ merged_by_name[rule["name"]] = rule
+
+ merged_rules = [merged_by_name[name] for name in ordered_names]
+ return load_automation_text(json.dumps({"rules": merged_rules}))
+
+
+def refresh_automation_manager(rules_text: str) -> tuple[list[list[Any]], Any, bool, dict[str, Any], str, str, str]:
+ try:
+ document = load_automation_text(rules_text)
+ except Exception as exc:
+ raise gr.Error(f"Rules did not validate: {exc}") from exc
+ names = _rule_names(document)
+ selected = names[0] if names else None
+ labels = _automation_labels_from_document(document)
+ return (
+ _rule_rows(document),
+ gr.update(choices=names, value=selected),
+ _rule_enabled(document, selected),
+ {"status": "valid", "rules": _rules_json(document)["rules"], "labels": labels},
+ _status_card("success", "Manager refreshed", f"{len(names)} automation(s) loaded."),
+ _labels_card(labels),
+ _labels_card(labels),
+ )
+
+
+def select_managed_rule(rules_text: str, rule_name: str | None) -> tuple[bool, str]:
+ document = load_automation_text(rules_text)
+ return (
+ _rule_enabled(document, rule_name),
+ _status_card("idle", "Automation selected", rule_name or "No automation selected."),
+ )
+
+
+def set_rule_enabled(rules_text: str, rule_name: str | None, enabled: bool) -> tuple[
+ str,
+ list[list[Any]],
+ Any,
+ bool,
+ dict[str, Any],
+ str,
+ str,
+ str,
+]:
+ if not rule_name:
+ raise gr.Error("Choose an automation first.")
+
+ document = load_automation_text(rules_text)
+ data = _rules_json(document)
+ changed = False
+ for rule in data["rules"]:
+ if rule["name"] == rule_name:
+ rule.setdefault("gate", {})
+ rule["gate"]["enabled"] = enabled
+ changed = True
+ break
+ if not changed:
+ raise gr.Error(f"Automation not found: {rule_name}")
+
+ updated_text = _rules_text_from_data(data)
+ updated_document = load_automation_text(updated_text)
+ labels = _automation_labels_from_document(updated_document)
+ names = _rule_names(updated_document)
+ return (
+ updated_text,
+ _rule_rows(updated_document),
+ gr.update(choices=names, value=rule_name),
+ enabled,
+ {"status": "updated", "rules": _rules_json(updated_document)["rules"], "labels": labels},
+ _status_card("success", "Automation updated", f"{rule_name} is {'on' if enabled else 'off'}."),
+ _labels_card(labels),
+ _labels_card(labels),
+ )
+
+
+def _video_file_path(value: Any) -> str | None:
+ if value is None:
+ return None
+ if isinstance(value, str):
+ return value
+ if isinstance(value, Path):
+ return str(value)
+ if isinstance(value, dict):
+ return value.get("path") or value.get("name")
+ path = getattr(value, "path", None) or getattr(value, "name", None)
+ return str(path) if path else None
+
+
+def reset_demo_state() -> tuple[str, dict[str, Any]]:
+ save_runtime_state(RuntimeState())
+ if EVENTS_PATH.exists():
+ EVENTS_PATH.unlink()
+ return (
+ _status_card("success", "Demo state reset", "Cooldown memory and action history are clear."),
+ {"status": "reset", "last_fired": {}, "events": "cleared"},
+ )
+
+
+def run_detector(
+ video_path: str | None,
+ class_prompt: str,
+ confidence: float,
+ frame_stride: int,
+ max_frames: int,
+ model_name: str,
+ image_size: int,
+ device: str,
+ max_detections: int,
+) -> tuple[str | None, list[list[Any]], dict[str, Any], str]:
+ if not video_path:
+ raise gr.Error("Upload a video first.")
+ if not parse_class_prompt(class_prompt):
+ raise gr.Error("Enter at least one class prompt.")
+
+ result = process_video(
+ video_path=video_path,
+ class_prompt=class_prompt,
+ confidence=confidence,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ model_name=model_name or DEFAULT_MODEL,
+ image_size=image_size,
+ device=None if device == "auto" else device,
+ max_detections=max_detections,
+ )
+ return (
+ result.output_video_path,
+ _detection_rows(result.detections),
+ _result_json(result),
+ _status_card(
+ "success",
+ f"{len(result.detections)} detections",
+ f"Processed {result.processed_frames} frames at {result.output_fps:.1f} FPS output.",
+ ),
+ )
+
+
+def compile_rules(
+ instruction: str,
+ current_rules_text: str,
+ label_hints: str,
+ base_url: str,
+ model: str,
+) -> tuple[str, dict[str, Any], str, str, list[list[Any]], Any, bool, str]:
+ if not instruction.strip():
+ raise gr.Error("Describe the automation first.")
+
+ try:
+ compiled = compile_automation_with_llamacpp(
+ instruction=instruction,
+ class_names=parse_class_prompt(label_hints),
+ base_url=base_url or DEFAULT_LLAMACPP_BASE_URL,
+ model=model or DEFAULT_LLAMACPP_MODEL,
+ )
+ except Exception as exc:
+ raise gr.Error(f"Could not compile automation: {exc}") from exc
+ merged_document = _merge_generated_rules(current_rules_text, compiled.document)
+ rules_json = merged_document.model_dump_json(by_alias=True, indent=2)
+ labels = _automation_labels_from_document(merged_document)
+ names = _rule_names(merged_document)
+ generated_names = _rule_names(compiled.document)
+ selected = generated_names[0] if generated_names else (names[0] if names else None)
+ labels_card = _labels_card(labels)
+ return (
+ rules_json,
+ {
+ "raw_text": compiled.raw_text,
+ "generated": _rules_json(compiled.document)["rules"],
+ "active_rules": json.loads(rules_json)["rules"],
+ "labels": labels,
+ },
+ _status_card(
+ "success",
+ "Generated and added",
+ f"{len(compiled.document.rules)} new rule(s). {len(merged_document.rules)} active automation(s).",
+ ),
+ labels_card,
+ _rule_rows(merged_document),
+ gr.update(choices=names, value=selected),
+ _rule_enabled(merged_document, selected),
+ labels_card,
+ )
+
+
+def validate_rules(rules_text: str) -> tuple[dict[str, Any], str, str, list[list[Any]], Any, bool, str]:
+ try:
+ document = load_automation_text(rules_text)
+ except Exception as exc:
+ raise gr.Error(f"Rules did not validate: {exc}") from exc
+ labels = _automation_labels_from_document(document)
+ names = _rule_names(document)
+ selected = names[0] if names else None
+ labels_card = _labels_card(labels)
+ return (
+ {"status": "valid", "rules": _rules_json(document)["rules"], "labels": labels},
+ _status_card("success", "Validation passed", f"{len(document.rules)} rule(s) checked."),
+ labels_card,
+ _rule_rows(document),
+ gr.update(choices=names, value=selected),
+ _rule_enabled(document, selected),
+ labels_card,
+ )
+
+
+def save_rules(rules_text: str) -> tuple[str, dict[str, Any], str, str, list[list[Any]], Any, bool, str]:
+ document = load_automation_text(rules_text)
+ save_automations(document)
+ rules_json = document.model_dump_json(by_alias=True, indent=2)
+ labels = _automation_labels_from_document(document)
+ names = _rule_names(document)
+ selected = names[0] if names else None
+ labels_card = _labels_card(labels)
+ return (
+ rules_json,
+ {"status": "saved", "rules": json.loads(rules_json), "labels": labels},
+ _status_card(
+ "success",
+ "Saved locally",
+ f"{len(document.rules)} rule(s) stored. Monitor will watch: {_labels_summary(labels)}.",
+ ),
+ labels_card,
+ _rule_rows(document),
+ gr.update(choices=names, value=selected),
+ _rule_enabled(document, selected),
+ labels_card,
+ )
+
+
+def run_monitor_clip(
+ clip_file: Any,
+ rules_text: str,
+ confidence: float,
+ frame_stride: int,
+ max_frames: int,
+ model_name: str,
+ image_size: int,
+ device: str,
+ max_detections: int,
+ enable_webhooks: bool,
+ webhook_url: str,
+) -> tuple[str | None, list[list[Any]], list[list[Any]], dict[str, Any], str, str]:
+ video_path = _video_file_path(clip_file)
+ if not video_path:
+ raise gr.Error("Upload a clip first.")
+
+ rules = load_automation_text(rules_text)
+ labels = _automation_labels_from_document(rules)
+ if not labels:
+ raise gr.Error("The active automations do not mention any detector labels.")
+
+ state = load_runtime_state()
+ result = process_video(
+ video_path=video_path,
+ class_prompt=labels,
+ confidence=confidence,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ model_name=model_name or DEFAULT_MODEL,
+ image_size=image_size,
+ device=None if device == "auto" else device,
+ max_detections=max_detections,
+ )
+ events, last_fired = evaluate_video_detections(rules.rules, result.detections, last_fired=state.last_fired)
+ dispatched = dispatch_events(events, enable_webhooks=enable_webhooks, webhook_url=webhook_url or None)
+ state.last_fired = last_fired
+ save_runtime_state(state)
+ append_events(dispatched)
+ automation_video_path = render_automation_video(
+ source_video_path=video_path,
+ detections=result.detections,
+ events=dispatched,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ )
+
+ action_count = len(dispatched)
+ status_tone = "action" if action_count else "warning"
+ status_title = f"{action_count} event{'s' if action_count != 1 else ''}"
+ status_body = (
+ "Replay rendered with fired automation overlays."
+ if action_count
+ else f"No automations fired. Watched: {_labels_summary(labels)}."
+ )
+ return (
+ automation_video_path,
+ _event_rows(dispatched),
+ _detection_rows(result.detections),
+ {
+ "watched_labels": labels,
+ "video": _result_json(result),
+ "actions": [event.model_dump(mode="json") for event in dispatched],
+ },
+ _status_card(status_tone, status_title, status_body),
+ _labels_card(labels),
+ )
+
+
+def run_detector_and_automations(
+ video_path: str | None,
+ class_prompt: str,
+ confidence: float,
+ frame_stride: int,
+ max_frames: int,
+ model_name: str,
+ image_size: int,
+ device: str,
+ max_detections: int,
+ rules_text: str,
+ enable_webhooks: bool,
+ webhook_url: str,
+) -> tuple[str | None, list[list[Any]], list[list[Any]], dict[str, Any], str]:
+ if not video_path:
+ raise gr.Error("Upload a video first.")
+
+ rules = load_automation_text(rules_text)
+ state = load_runtime_state()
+ result = process_video(
+ video_path=video_path,
+ class_prompt=class_prompt,
+ confidence=confidence,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ model_name=model_name or DEFAULT_MODEL,
+ image_size=image_size,
+ device=None if device == "auto" else device,
+ max_detections=max_detections,
+ )
+ events, last_fired = evaluate_video_detections(rules.rules, result.detections, last_fired=state.last_fired)
+ dispatched = dispatch_events(events, enable_webhooks=enable_webhooks, webhook_url=webhook_url or None)
+ state.last_fired = last_fired
+ save_runtime_state(state)
+ append_events(dispatched)
+ automation_video_path = render_automation_video(
+ source_video_path=video_path,
+ detections=result.detections,
+ events=dispatched,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ )
+ action_count = len(dispatched)
+ status_tone = "action" if action_count else "warning"
+ status_title = f"{action_count} action{'s' if action_count != 1 else ''} fired"
+ status_body = (
+ "Automation replay rendered with fired-action overlay."
+ if action_count
+ else "Rules validated, but no frame matched the automation."
+ )
+ return (
+ automation_video_path,
+ _detection_rows(result.detections),
+ _event_rows(dispatched),
+ {
+ "video": _result_json(result),
+ "actions": [event.model_dump(mode="json") for event in dispatched],
+ },
+ _status_card(status_tone, status_title, status_body),
+ )
+
+
+try:
+ INITIAL_RULE_DOCUMENT = load_automation_text(DEFAULT_RULES)
+ INITIAL_RULE_LABELS = _automation_labels_from_document(INITIAL_RULE_DOCUMENT)
+ INITIAL_RULE_ROWS = _rule_rows(INITIAL_RULE_DOCUMENT)
+ INITIAL_RULE_NAMES = _rule_names(INITIAL_RULE_DOCUMENT)
+ INITIAL_SELECTED_RULE = INITIAL_RULE_NAMES[0] if INITIAL_RULE_NAMES else None
+ INITIAL_SELECTED_ENABLED = _rule_enabled(INITIAL_RULE_DOCUMENT, INITIAL_SELECTED_RULE)
+except Exception:
+ INITIAL_RULE_DOCUMENT = None
+ INITIAL_RULE_LABELS = []
+ INITIAL_RULE_ROWS = []
+ INITIAL_RULE_NAMES = []
+ INITIAL_SELECTED_RULE = None
+ INITIAL_SELECTED_ENABLED = False
+
+
+with gr.Blocks(title="Tiny Trigger") as demo:
+ gr.HTML(
+ """
+
+"""
+ )
+
+ with gr.Tabs():
+ with gr.Tab("Monitor"):
+ with gr.Row(elem_classes=["tt-grid"]):
+ with gr.Column(scale=4, min_width=320):
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Clip Intake", elem_classes=["tt-panel-title"])
+ gr.Markdown(
+ "Upload a short clip. Tiny Trigger scans for the labels required by the active automations and renders the event replay.",
+ elem_classes=["tt-flow-copy"],
+ )
+ monitor_clip_input = gr.File(
+ label="Video clip",
+ file_types=[".mp4", ".mov", ".m4v", ".webm"],
+ type="filepath",
+ )
+ monitor_run_button = gr.Button("Run clip", variant="primary")
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Active Watch", elem_classes=["tt-panel-title"])
+ monitor_labels_output = gr.HTML(_labels_card(INITIAL_RULE_LABELS))
+ monitor_status = gr.HTML(
+ _status_card("idle", "Waiting for clip", "Upload a clip to run the active automations.")
+ )
+
+ with gr.Column(scale=7, min_width=520):
+ with gr.Group(elem_classes=["tt-video-panel"]):
+ gr.Markdown("### Event Replay", elem_classes=["tt-panel-title"])
+ automation_video = gr.Video(
+ label="Automation replay",
+ format="mp4",
+ autoplay=True,
+ elem_classes=["tt-result-video"],
+ )
+ with gr.Group(elem_classes=["tt-data-panel"]):
+ gr.Markdown("### Events", elem_classes=["tt-panel-title"])
+ actions_table = gr.Dataframe(
+ headers=["rule", "action", "type", "status", "frame", "time", "response", "error"],
+ datatype=["str", "str", "str", "str", "number", "number", "str", "str"],
+ label="Action timeline",
+ )
+ with gr.Accordion("Run details", open=False):
+ automation_detections = gr.Dataframe(
+ headers=["frame", "time", "label", "confidence", "bbox", "bbox_norm"],
+ datatype=["number", "number", "str", "number", "str", "str"],
+ label="Detections",
+ )
+ automation_json = gr.JSON(label="Run JSON")
+
+ with gr.Tab("Automations"):
+ with gr.Row(elem_classes=["tt-grid"]):
+ with gr.Column(scale=4, min_width=340):
+ with gr.Group(elem_classes=["tt-panel", "tt-automation-panel"]):
+ gr.Markdown("### Generate Automation", elem_classes=["tt-panel-title"])
+ instruction = gr.Textbox(
+ label="Intent",
+ value="If person near steering wheel then you have to turn on pc.",
+ lines=5,
+ elem_classes=["tt-compact"],
+ )
+ label_hints = gr.Textbox(
+ label="Label hints for the generator",
+ value=DEFAULT_CLASSES,
+ lines=3,
+ elem_classes=["tt-compact"],
+ )
+ with gr.Accordion("Compiler endpoint", open=False):
+ llama_base_url = gr.Textbox(label="llama.cpp base URL", value=DEFAULT_LLAMACPP_BASE_URL)
+ llama_model = gr.Textbox(label="llama.cpp model", value=DEFAULT_LLAMACPP_MODEL)
+ with gr.Row(elem_classes=["tt-run-row"]):
+ compile_button = gr.Button("Generate", variant="primary")
+ validate_rules_button = gr.Button("Test", variant="secondary")
+ save_rules_button = gr.Button("Save", variant="secondary")
+
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Manage Automations", elem_classes=["tt-panel-title"])
+ automation_table = gr.Dataframe(
+ headers=["state", "automation", "labels", "when", "then"],
+ datatype=["str", "str", "str", "str", "str"],
+ value=INITIAL_RULE_ROWS,
+ interactive=False,
+ label="Active automations",
+ )
+ managed_rule = gr.Dropdown(
+ label="Automation",
+ choices=INITIAL_RULE_NAMES,
+ value=INITIAL_SELECTED_RULE,
+ )
+ managed_enabled = gr.Checkbox(label="Enabled", value=INITIAL_SELECTED_ENABLED)
+ with gr.Row(elem_classes=["tt-run-row"]):
+ apply_rule_button = gr.Button("Apply", variant="primary")
+ refresh_manager_button = gr.Button("Refresh", variant="secondary")
+ manager_save_button = gr.Button("Save all", variant="secondary")
+
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Audit", elem_classes=["tt-panel-title"])
+ rules_status = gr.HTML(
+ _status_card("idle", "Rules loaded", "Generate or test rules before running a clip.")
+ )
+ rules_labels_output = gr.HTML(_labels_card(INITIAL_RULE_LABELS))
+
+ with gr.Column(scale=7, min_width=520):
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Active Rules", elem_classes=["tt-panel-title"])
+ rules_text = gr.Code(
+ label="Rules JSON/YAML",
+ language="yaml",
+ value=DEFAULT_RULES,
+ lines=22,
+ elem_classes=["tt-code"],
+ )
+ with gr.Group(elem_classes=["tt-data-panel"]):
+ gr.Markdown("### Compile Test", elem_classes=["tt-panel-title"])
+ compiled_json = gr.JSON(label="Rule audit")
+
+ with gr.Tab("Settings"):
+ with gr.Row(elem_classes=["tt-grid"]):
+ with gr.Column(scale=4, min_width=320):
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Detector", elem_classes=["tt-panel-title"])
+ model_name = gr.Textbox(label="YOLOE model", value=DEFAULT_MODEL)
+ device = gr.Dropdown(label="Device", choices=["auto", "mps", "cuda:0", "cpu"], value=DEFAULT_DEVICE)
+ image_size = gr.Slider(label="Image size", minimum=320, maximum=1536, step=32, value=DEFAULT_IMAGE_SIZE)
+ confidence = gr.Slider(label="Sensitivity", minimum=0.05, maximum=0.95, step=0.05, value=0.25)
+ frame_stride = gr.Slider(label="Frame stride", minimum=1, maximum=30, step=1, value=5)
+ max_frames = gr.Slider(label="Max processed frames", minimum=10, maximum=600, step=10, value=120)
+ max_detections = gr.Slider(
+ label="Max detections",
+ minimum=1,
+ maximum=300,
+ step=1,
+ value=DEFAULT_MAX_DETECTIONS,
+ )
+ with gr.Column(scale=4, min_width=320):
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Dispatch", elem_classes=["tt-panel-title"])
+ enable_webhooks = gr.Checkbox(label="Enable webhook POSTs", value=False)
+ webhook_url = gr.Textbox(label="Fallback webhook URL", value=DEFAULT_WEBHOOK_URL)
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### Demo State", elem_classes=["tt-panel-title"])
+ demo_state_output = gr.HTML(
+ _status_card("idle", "Ready for replay", "Cooldown memory is kept between automation runs."),
+ elem_classes=["tt-demo-state"],
+ )
+ reset_state_button = gr.Button("Reset cooldowns and history", variant="secondary")
+ demo_state_json = gr.JSON(label="State event", visible=False)
+ with gr.Column(scale=4, min_width=320):
+ with gr.Group(elem_classes=["tt-panel"]):
+ gr.Markdown("### IP Camera Skeleton", elem_classes=["tt-panel-title"])
+ gr.Markdown(
+ "The MVP runs uploaded clips. This section reserves the next product path: a persistent camera feed that runs the same active automations on a schedule.",
+ elem_classes=["tt-flow-copy"],
+ )
+ camera_url = gr.Textbox(label="Camera / RTSP URL", value=LOCAL_CONFIG.camera_url or "")
+ poll_interval = gr.Slider(label="Polling interval seconds", minimum=1, maximum=60, step=1, value=5)
+ gr.HTML(_status_card("idle", "Not active in MVP", "Prepared for continuous monitoring after the hackathon demo."))
+
+ monitor_inputs = [
+ monitor_clip_input,
+ rules_text,
+ confidence,
+ frame_stride,
+ max_frames,
+ model_name,
+ image_size,
+ device,
+ max_detections,
+ enable_webhooks,
+ webhook_url,
+ ]
+ monitor_outputs = [
+ automation_video,
+ actions_table,
+ automation_detections,
+ automation_json,
+ monitor_status,
+ monitor_labels_output,
+ ]
+ generated_rule_outputs = [
+ rules_text,
+ compiled_json,
+ rules_status,
+ rules_labels_output,
+ automation_table,
+ managed_rule,
+ managed_enabled,
+ monitor_labels_output,
+ ]
+ tested_rule_outputs = [
+ compiled_json,
+ rules_status,
+ rules_labels_output,
+ automation_table,
+ managed_rule,
+ managed_enabled,
+ monitor_labels_output,
+ ]
+ manager_refresh_outputs = [
+ automation_table,
+ managed_rule,
+ managed_enabled,
+ compiled_json,
+ rules_status,
+ rules_labels_output,
+ monitor_labels_output,
+ ]
+ manager_apply_outputs = [
+ rules_text,
+ automation_table,
+ managed_rule,
+ managed_enabled,
+ compiled_json,
+ rules_status,
+ rules_labels_output,
+ monitor_labels_output,
+ ]
+ monitor_clip_input.change(run_monitor_clip, inputs=monitor_inputs, outputs=monitor_outputs)
+ monitor_run_button.click(run_monitor_clip, inputs=monitor_inputs, outputs=monitor_outputs)
+ compile_button.click(
+ compile_rules,
+ inputs=[instruction, rules_text, label_hints, llama_base_url, llama_model],
+ outputs=generated_rule_outputs,
+ )
+ validate_rules_button.click(
+ validate_rules,
+ inputs=[rules_text],
+ outputs=tested_rule_outputs,
+ )
+ save_rules_button.click(
+ save_rules,
+ inputs=[rules_text],
+ outputs=generated_rule_outputs,
+ )
+ manager_save_button.click(save_rules, inputs=[rules_text], outputs=generated_rule_outputs)
+ refresh_manager_button.click(refresh_automation_manager, inputs=[rules_text], outputs=manager_refresh_outputs)
+ managed_rule.change(select_managed_rule, inputs=[rules_text, managed_rule], outputs=[managed_enabled, rules_status])
+ apply_rule_button.click(
+ set_rule_enabled,
+ inputs=[rules_text, managed_rule, managed_enabled],
+ outputs=manager_apply_outputs,
+ )
+ reset_state_button.click(reset_demo_state, outputs=[demo_state_output, demo_state_json])
+
+
+if __name__ == "__main__":
+ demo.launch(ssr_mode=False, css=APP_CSS)
diff --git a/examples/feed-cat.yaml b/examples/feed-cat.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0dfb72aa63fbc67e6916d3a8763162dbd1a55f38
--- /dev/null
+++ b/examples/feed-cat.yaml
@@ -0,0 +1,10 @@
+rules:
+ - name: feed-cat
+ when:
+ all:
+ - present: {label: cat, min_count: 1}
+ - near: {a: cat, b: feeder robot, max_distance: 0.35}
+ - cooldown: {key: feed-cat, minutes: 30}
+ then:
+ - type: webhook
+ name: feed cat
diff --git a/examples/turn-on-pc.yaml b/examples/turn-on-pc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..45029a1a838b604e7e61fa3213879310dfa840c6
--- /dev/null
+++ b/examples/turn-on-pc.yaml
@@ -0,0 +1,12 @@
+rules:
+ - name: person-near-steering-wheel
+ when:
+ all:
+ - present: {label: person, min_count: 1}
+ - near: {a: person, b: steering wheel, max_distance: 0.45}
+ gate:
+ enabled: true
+ cooldown: {key: turn-on-pc, minutes: 5}
+ then:
+ - type: webhook
+ name: turn on pc
diff --git a/frontend/.gitignore b/frontend/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..631f35568602810d5418dd3dc62ff8990bbdabec
--- /dev/null
+++ b/frontend/.gitignore
@@ -0,0 +1,4 @@
+node_modules
+dist
+*.local
+.vite
diff --git a/frontend/index.html b/frontend/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..3c2e1b01f633690d1039db1fb45ebb78e16ff905
--- /dev/null
+++ b/frontend/index.html
@@ -0,0 +1,24 @@
+
+
+
+
+
+ Tiny Trigger — Automation Console
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/frontend/package.json b/frontend/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..1858e404af709438f6f1c5175b5d75eb10fff0d3
--- /dev/null
+++ b/frontend/package.json
@@ -0,0 +1,41 @@
+{
+ "name": "tiny-trigger-ui",
+ "private": true,
+ "version": "0.1.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "tsc -b && vite build",
+ "preview": "vite preview",
+ "typecheck": "tsc -b --noEmit"
+ },
+ "dependencies": {
+ "@gradio/client": "^1.14.0",
+ "@radix-ui/react-label": "^2.1.2",
+ "@radix-ui/react-scroll-area": "^1.2.3",
+ "@radix-ui/react-separator": "^1.1.2",
+ "@radix-ui/react-slider": "^1.2.3",
+ "@radix-ui/react-slot": "^1.1.2",
+ "@radix-ui/react-tabs": "^1.1.3",
+ "@radix-ui/react-tooltip": "^1.1.8",
+ "class-variance-authority": "^0.7.1",
+ "clsx": "^2.1.1",
+ "lucide-react": "^0.475.0",
+ "motion": "^12.4.0",
+ "react": "^19.0.0",
+ "react-dom": "^19.0.0",
+ "sonner": "^2.0.0",
+ "tailwind-merge": "^3.0.1"
+ },
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diff --git a/frontend/pnpm-lock.yaml b/frontend/pnpm-lock.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3ee3537b361d7ae419590429e62a1a3bfdca7b7b
--- /dev/null
+++ b/frontend/pnpm-lock.yaml
@@ -0,0 +1,2746 @@
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+ esbuild@0.25.12:
+ optionalDependencies:
+ '@esbuild/aix-ppc64': 0.25.12
+ '@esbuild/android-arm': 0.25.12
+ '@esbuild/android-arm64': 0.25.12
+ '@esbuild/android-x64': 0.25.12
+ '@esbuild/darwin-arm64': 0.25.12
+ '@esbuild/darwin-x64': 0.25.12
+ '@esbuild/freebsd-arm64': 0.25.12
+ '@esbuild/freebsd-x64': 0.25.12
+ '@esbuild/linux-arm': 0.25.12
+ '@esbuild/linux-arm64': 0.25.12
+ '@esbuild/linux-ia32': 0.25.12
+ '@esbuild/linux-loong64': 0.25.12
+ '@esbuild/linux-mips64el': 0.25.12
+ '@esbuild/linux-ppc64': 0.25.12
+ '@esbuild/linux-riscv64': 0.25.12
+ '@esbuild/linux-s390x': 0.25.12
+ '@esbuild/linux-x64': 0.25.12
+ '@esbuild/netbsd-arm64': 0.25.12
+ '@esbuild/netbsd-x64': 0.25.12
+ '@esbuild/openbsd-arm64': 0.25.12
+ '@esbuild/openbsd-x64': 0.25.12
+ '@esbuild/openharmony-arm64': 0.25.12
+ '@esbuild/sunos-x64': 0.25.12
+ '@esbuild/win32-arm64': 0.25.12
+ '@esbuild/win32-ia32': 0.25.12
+ '@esbuild/win32-x64': 0.25.12
+
+ escalade@3.2.0: {}
+
+ eventsource@2.0.2: {}
+
+ fast-string-truncated-width@3.0.3: {}
+
+ fast-string-width@3.0.2:
+ dependencies:
+ fast-string-truncated-width: 3.0.3
+
+ fast-wrap-ansi@0.2.2:
+ dependencies:
+ fast-string-width: 3.0.2
+
+ fdir@6.5.0(picomatch@4.0.4):
+ optionalDependencies:
+ picomatch: 4.0.4
+
+ fetch-event-stream@0.1.6: {}
+
+ framer-motion@12.40.0(react-dom@19.2.7(react@19.2.7))(react@19.2.7):
+ dependencies:
+ motion-dom: 12.40.0
+ motion-utils: 12.39.0
+ tslib: 2.8.1
+ optionalDependencies:
+ react: 19.2.7
+ react-dom: 19.2.7(react@19.2.7)
+
+ fsevents@2.3.3:
+ optional: true
+
+ gensync@1.0.0-beta.2: {}
+
+ get-caller-file@2.0.5: {}
+
+ graceful-fs@4.2.11: {}
+
+ graphql@16.14.2: {}
+
+ headers-polyfill@5.0.1:
+ dependencies:
+ '@types/set-cookie-parser': 2.4.10
+ set-cookie-parser: 3.1.0
+
+ is-fullwidth-code-point@3.0.0: {}
+
+ is-node-process@1.2.0: {}
+
+ jiti@2.7.0: {}
+
+ js-tokens@4.0.0: {}
+
+ jsesc@3.1.0: {}
+
+ json5@2.2.3: {}
+
+ lightningcss-android-arm64@1.32.0:
+ optional: true
+
+ lightningcss-darwin-arm64@1.32.0:
+ optional: true
+
+ lightningcss-darwin-x64@1.32.0:
+ optional: true
+
+ lightningcss-freebsd-x64@1.32.0:
+ optional: true
+
+ lightningcss-linux-arm-gnueabihf@1.32.0:
+ optional: true
+
+ lightningcss-linux-arm64-gnu@1.32.0:
+ optional: true
+
+ lightningcss-linux-arm64-musl@1.32.0:
+ optional: true
+
+ lightningcss-linux-x64-gnu@1.32.0:
+ optional: true
+
+ lightningcss-linux-x64-musl@1.32.0:
+ optional: true
+
+ lightningcss-win32-arm64-msvc@1.32.0:
+ optional: true
+
+ lightningcss-win32-x64-msvc@1.32.0:
+ optional: true
+
+ lightningcss@1.32.0:
+ dependencies:
+ detect-libc: 2.1.2
+ optionalDependencies:
+ lightningcss-android-arm64: 1.32.0
+ lightningcss-darwin-arm64: 1.32.0
+ lightningcss-darwin-x64: 1.32.0
+ lightningcss-freebsd-x64: 1.32.0
+ lightningcss-linux-arm-gnueabihf: 1.32.0
+ lightningcss-linux-arm64-gnu: 1.32.0
+ lightningcss-linux-arm64-musl: 1.32.0
+ lightningcss-linux-x64-gnu: 1.32.0
+ lightningcss-linux-x64-musl: 1.32.0
+ lightningcss-win32-arm64-msvc: 1.32.0
+ lightningcss-win32-x64-msvc: 1.32.0
+
+ lru-cache@5.1.1:
+ dependencies:
+ yallist: 3.1.1
+
+ lucide-react@0.475.0(react@19.2.7):
+ dependencies:
+ react: 19.2.7
+
+ magic-string@0.30.21:
+ dependencies:
+ '@jridgewell/sourcemap-codec': 1.5.5
+
+ motion-dom@12.40.0:
+ dependencies:
+ motion-utils: 12.39.0
+
+ motion-utils@12.39.0: {}
+
+ motion@12.40.0(react-dom@19.2.7(react@19.2.7))(react@19.2.7):
+ dependencies:
+ framer-motion: 12.40.0(react-dom@19.2.7(react@19.2.7))(react@19.2.7)
+ tslib: 2.8.1
+ optionalDependencies:
+ react: 19.2.7
+ react-dom: 19.2.7(react@19.2.7)
+
+ ms@2.1.3: {}
+
+ msw@2.14.6(@types/node@22.19.21)(typescript@5.9.3):
+ dependencies:
+ '@inquirer/confirm': 6.1.1(@types/node@22.19.21)
+ '@mswjs/interceptors': 0.41.9
+ '@open-draft/deferred-promise': 3.0.0
+ '@types/statuses': 2.0.6
+ cookie: 1.1.1
+ graphql: 16.14.2
+ headers-polyfill: 5.0.1
+ is-node-process: 1.2.0
+ outvariant: 1.4.3
+ path-to-regexp: 6.3.0
+ picocolors: 1.1.1
+ rettime: 0.11.11
+ statuses: 2.0.2
+ strict-event-emitter: 0.5.1
+ tough-cookie: 6.0.1
+ type-fest: 5.7.0
+ until-async: 3.0.2
+ yargs: 17.7.2
+ optionalDependencies:
+ typescript: 5.9.3
+ transitivePeerDependencies:
+ - '@types/node'
+
+ mute-stream@3.0.0: {}
+
+ nanoid@3.3.12: {}
+
+ node-gyp-build@4.8.4: {}
+
+ node-releases@2.0.47: {}
+
+ outvariant@1.4.3: {}
+
+ path-to-regexp@6.3.0: {}
+
+ picocolors@1.1.1: {}
+
+ picomatch@4.0.4: {}
+
+ postcss@8.5.15:
+ dependencies:
+ nanoid: 3.3.12
+ picocolors: 1.1.1
+ source-map-js: 1.2.1
+
+ react-dom@19.2.7(react@19.2.7):
+ dependencies:
+ react: 19.2.7
+ scheduler: 0.27.0
+
+ react-refresh@0.17.0: {}
+
+ react@19.2.7: {}
+
+ require-directory@2.1.1: {}
+
+ rettime@0.11.11: {}
+
+ rollup@4.61.1:
+ dependencies:
+ '@types/estree': 1.0.9
+ optionalDependencies:
+ '@rollup/rollup-android-arm-eabi': 4.61.1
+ '@rollup/rollup-android-arm64': 4.61.1
+ '@rollup/rollup-darwin-arm64': 4.61.1
+ '@rollup/rollup-darwin-x64': 4.61.1
+ '@rollup/rollup-freebsd-arm64': 4.61.1
+ '@rollup/rollup-freebsd-x64': 4.61.1
+ '@rollup/rollup-linux-arm-gnueabihf': 4.61.1
+ '@rollup/rollup-linux-arm-musleabihf': 4.61.1
+ '@rollup/rollup-linux-arm64-gnu': 4.61.1
+ '@rollup/rollup-linux-arm64-musl': 4.61.1
+ '@rollup/rollup-linux-loong64-gnu': 4.61.1
+ '@rollup/rollup-linux-loong64-musl': 4.61.1
+ '@rollup/rollup-linux-ppc64-gnu': 4.61.1
+ '@rollup/rollup-linux-ppc64-musl': 4.61.1
+ '@rollup/rollup-linux-riscv64-gnu': 4.61.1
+ '@rollup/rollup-linux-riscv64-musl': 4.61.1
+ '@rollup/rollup-linux-s390x-gnu': 4.61.1
+ '@rollup/rollup-linux-x64-gnu': 4.61.1
+ '@rollup/rollup-linux-x64-musl': 4.61.1
+ '@rollup/rollup-openbsd-x64': 4.61.1
+ '@rollup/rollup-openharmony-arm64': 4.61.1
+ '@rollup/rollup-win32-arm64-msvc': 4.61.1
+ '@rollup/rollup-win32-ia32-msvc': 4.61.1
+ '@rollup/rollup-win32-x64-gnu': 4.61.1
+ '@rollup/rollup-win32-x64-msvc': 4.61.1
+ fsevents: 2.3.3
+
+ scheduler@0.27.0: {}
+
+ semiver@1.1.0: {}
+
+ semver@6.3.1: {}
+
+ set-cookie-parser@3.1.0: {}
+
+ signal-exit@4.1.0: {}
+
+ sonner@2.0.7(react-dom@19.2.7(react@19.2.7))(react@19.2.7):
+ dependencies:
+ react: 19.2.7
+ react-dom: 19.2.7(react@19.2.7)
+
+ source-map-js@1.2.1: {}
+
+ statuses@2.0.2: {}
+
+ strict-event-emitter@0.5.1: {}
+
+ string-width@4.2.3:
+ dependencies:
+ emoji-regex: 8.0.0
+ is-fullwidth-code-point: 3.0.0
+ strip-ansi: 6.0.1
+
+ strip-ansi@6.0.1:
+ dependencies:
+ ansi-regex: 5.0.1
+
+ tagged-tag@1.0.0: {}
+
+ tailwind-merge@3.6.0: {}
+
+ tailwindcss@4.3.0: {}
+
+ tapable@2.3.3: {}
+
+ textlinestream@1.1.1: {}
+
+ tinyglobby@0.2.17:
+ dependencies:
+ fdir: 6.5.0(picomatch@4.0.4)
+ picomatch: 4.0.4
+
+ tldts-core@7.4.2: {}
+
+ tldts@7.4.2:
+ dependencies:
+ tldts-core: 7.4.2
+
+ tough-cookie@6.0.1:
+ dependencies:
+ tldts: 7.4.2
+
+ tslib@2.8.1: {}
+
+ tw-animate-css@1.4.0: {}
+
+ type-fest@5.7.0:
+ dependencies:
+ tagged-tag: 1.0.0
+
+ typescript@5.9.3: {}
+
+ undici-types@6.21.0: {}
+
+ until-async@3.0.2: {}
+
+ update-browserslist-db@1.2.3(browserslist@4.28.2):
+ dependencies:
+ browserslist: 4.28.2
+ escalade: 3.2.0
+ picocolors: 1.1.1
+
+ vite@6.4.3(@types/node@22.19.21)(jiti@2.7.0)(lightningcss@1.32.0):
+ dependencies:
+ esbuild: 0.25.12
+ fdir: 6.5.0(picomatch@4.0.4)
+ picomatch: 4.0.4
+ postcss: 8.5.15
+ rollup: 4.61.1
+ tinyglobby: 0.2.17
+ optionalDependencies:
+ '@types/node': 22.19.21
+ fsevents: 2.3.3
+ jiti: 2.7.0
+ lightningcss: 1.32.0
+
+ wrap-ansi@7.0.0:
+ dependencies:
+ ansi-styles: 4.3.0
+ string-width: 4.2.3
+ strip-ansi: 6.0.1
+
+ ws@8.21.0(bufferutil@4.1.0):
+ optionalDependencies:
+ bufferutil: 4.1.0
+
+ y18n@5.0.8: {}
+
+ yallist@3.1.1: {}
+
+ yargs-parser@21.1.1: {}
+
+ yargs@17.7.2:
+ dependencies:
+ cliui: 8.0.1
+ escalade: 3.2.0
+ get-caller-file: 2.0.5
+ require-directory: 2.1.1
+ string-width: 4.2.3
+ y18n: 5.0.8
+ yargs-parser: 21.1.1
diff --git a/frontend/pnpm-workspace.yaml b/frontend/pnpm-workspace.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4709e046a9ec304ad412079e9d6f8b65c1788e69
--- /dev/null
+++ b/frontend/pnpm-workspace.yaml
@@ -0,0 +1,4 @@
+allowBuilds:
+ bufferutil: false
+ esbuild: true
+ msw: false
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..560eed9e01fe97aa0c7ec23a59973f9df4ca208a
--- /dev/null
+++ b/frontend/src/App.tsx
@@ -0,0 +1,43 @@
+import { DashboardProvider } from "@/lib/dashboard"
+import { Sidebar } from "@/components/layout/Sidebar"
+import { TopBar } from "@/components/layout/TopBar"
+import { Section } from "@/components/layout/Section"
+import { ModulePanel } from "@/components/layout/ModulePanel"
+import { Toaster } from "@/components/ui/sonner"
+import { SECTIONS, SPAN_CLASS, modulesForSection } from "@/modules/registry"
+
+export default function App() {
+ let delay = 0
+ const nextDelay = () => `${(delay++ * 60).toString()}ms`
+
+ return (
+
+
+
+
+
+
+ {SECTIONS.sort((a, b) => a.order - b.order).map((section) => (
+
+ {modulesForSection(section.id).map((m) => (
+
+ ))}
+
+ ))}
+
+
+
+
+
+
+
+ )
+}
diff --git a/frontend/src/components/layout/ModulePanel.tsx b/frontend/src/components/layout/ModulePanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..eda32f7c1988c4d8be5b6ca3b76a9be2163558d7
--- /dev/null
+++ b/frontend/src/components/layout/ModulePanel.tsx
@@ -0,0 +1,40 @@
+import { Card, CardContent, CardHeader } from "@/components/ui/card"
+import { cn } from "@/lib/utils"
+import type { DashboardModule } from "@/modules/registry"
+
+export function ModulePanel({
+ module,
+ style,
+ className,
+}: {
+ module: DashboardModule
+ style?: React.CSSProperties
+ className?: string
+}) {
+ const { icon: Icon, Panel } = module
+ return (
+
+
+
+
+
+
+
+
+ {module.eyebrow}
+
+
+ {module.title}
+
+
+
+
+ {module.index}
+
+
+
+
+
+
+ )
+}
diff --git a/frontend/src/components/layout/Section.tsx b/frontend/src/components/layout/Section.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..c056076724599063f9eaaf8a7a588c04103c3763
--- /dev/null
+++ b/frontend/src/components/layout/Section.tsx
@@ -0,0 +1,32 @@
+import type { ReactNode } from "react"
+import type { DashboardSection } from "@/modules/registry"
+
+export function Section({
+ section,
+ children,
+}: {
+ section: DashboardSection
+ children: ReactNode
+}) {
+ return (
+
+ {/* labeled horizontal separator */}
+
+
+
+ {String(section.order).padStart(2, "0")}
+
+
+ {section.label}
+
+
+ {section.hint}
+
+
+
+
+
+ {children}
+
+ )
+}
diff --git a/frontend/src/components/layout/Sidebar.tsx b/frontend/src/components/layout/Sidebar.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a3804b44812d24cd58d65ef5e91313117e70cc7b
--- /dev/null
+++ b/frontend/src/components/layout/Sidebar.tsx
@@ -0,0 +1,54 @@
+import { ListChecks, SlidersHorizontal, TestTube, Zap } from "lucide-react"
+import type { LucideIcon } from "lucide-react"
+import { SECTIONS, type SectionId } from "@/modules/registry"
+import {
+ Tooltip,
+ TooltipContent,
+ TooltipProvider,
+ TooltipTrigger,
+} from "@/components/ui/tooltip"
+
+const SECTION_ICON: Record = {
+ test: TestTube,
+ automation: ListChecks,
+ settings: SlidersHorizontal,
+}
+
+export function Sidebar() {
+ return (
+
+ )
+}
diff --git a/frontend/src/components/layout/TopBar.tsx b/frontend/src/components/layout/TopBar.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..b155e27b2e11c59308e0636f2f66866118e26b99
--- /dev/null
+++ b/frontend/src/components/layout/TopBar.tsx
@@ -0,0 +1,48 @@
+import { useDashboard } from "@/lib/dashboard"
+import { Badge } from "@/components/ui/badge"
+
+export function TopBar() {
+ const { params, result, running } = useDashboard()
+
+ return (
+
+ )
+}
diff --git a/frontend/src/components/ui/badge.tsx b/frontend/src/components/ui/badge.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..5e0fb86b887c1f617914549fcd8af5419e275c1f
--- /dev/null
+++ b/frontend/src/components/ui/badge.tsx
@@ -0,0 +1,29 @@
+import * as React from "react"
+import { cva, type VariantProps } from "class-variance-authority"
+import { cn } from "@/lib/utils"
+
+const badgeVariants = cva(
+ "inline-flex items-center gap-1.5 rounded-full border px-2.5 py-0.5 text-[0.6875rem] font-medium font-mono uppercase tracking-wider transition-colors",
+ {
+ variants: {
+ variant: {
+ default: "border-border bg-white/5 text-muted-foreground",
+ live: "border-primary/40 bg-primary/10 text-primary",
+ warning: "border-warning/40 bg-warning/10 text-warning",
+ info: "border-info/40 bg-info/10 text-info",
+ danger: "border-destructive/40 bg-destructive/10 text-destructive",
+ },
+ },
+ defaultVariants: { variant: "default" },
+ },
+)
+
+function Badge({
+ className,
+ variant,
+ ...props
+}: React.ComponentProps<"span"> & VariantProps) {
+ return
+}
+
+export { Badge, badgeVariants }
diff --git a/frontend/src/components/ui/button.tsx b/frontend/src/components/ui/button.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..73387d5711e75fe306e7f6703c45583fd6755544
--- /dev/null
+++ b/frontend/src/components/ui/button.tsx
@@ -0,0 +1,46 @@
+import * as React from "react"
+import { Slot } from "@radix-ui/react-slot"
+import { cva, type VariantProps } from "class-variance-authority"
+import { cn } from "@/lib/utils"
+
+const buttonVariants = cva(
+ "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-all focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-40 [&_svg]:size-4 [&_svg]:shrink-0 active:scale-[0.98] select-none",
+ {
+ variants: {
+ variant: {
+ default:
+ "bg-primary text-primary-foreground font-semibold shadow-[0_0_0_1px_oklch(0.885_0.19_122_/_0.4),0_8px_24px_-12px_oklch(0.885_0.19_122_/_0.6)] hover:brightness-110",
+ secondary:
+ "bg-secondary text-secondary-foreground border border-border hover:bg-accent hover:border-white/15",
+ ghost: "text-muted-foreground hover:text-foreground hover:bg-white/5",
+ outline:
+ "border border-border bg-transparent hover:bg-white/5 hover:text-foreground",
+ destructive:
+ "bg-destructive/15 text-destructive border border-destructive/40 hover:bg-destructive/25",
+ },
+ size: {
+ default: "h-9 px-4 py-2",
+ sm: "h-8 rounded-md px-3 text-xs",
+ lg: "h-11 rounded-lg px-6 text-[0.95rem]",
+ icon: "size-9",
+ },
+ },
+ defaultVariants: { variant: "default", size: "default" },
+ },
+)
+
+function Button({
+ className,
+ variant,
+ size,
+ asChild = false,
+ ...props
+}: React.ComponentProps<"button"> &
+ VariantProps & { asChild?: boolean }) {
+ const Comp = asChild ? Slot : "button"
+ return (
+
+ )
+}
+
+export { Button, buttonVariants }
diff --git a/frontend/src/components/ui/card.tsx b/frontend/src/components/ui/card.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..6acdd8ba96ce3d574a611e18a68572c88c0b0b52
--- /dev/null
+++ b/frontend/src/components/ui/card.tsx
@@ -0,0 +1,45 @@
+import * as React from "react"
+import { cn } from "@/lib/utils"
+
+function Card({ className, ...props }: React.ComponentProps<"div">) {
+ return (
+
+ )
+}
+
+function CardHeader({ className, ...props }: React.ComponentProps<"div">) {
+ return (
+
+ )
+}
+
+function CardEyebrow({ className, ...props }: React.ComponentProps<"div">) {
+ return (
+
+ )
+}
+
+function CardContent({ className, ...props }: React.ComponentProps<"div">) {
+ return
+}
+
+export { Card, CardHeader, CardEyebrow, CardContent }
diff --git a/frontend/src/components/ui/input.tsx b/frontend/src/components/ui/input.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..cf6b15bafe3aa42dfcc6e00d9e8455f4e754b65b
--- /dev/null
+++ b/frontend/src/components/ui/input.tsx
@@ -0,0 +1,20 @@
+import * as React from "react"
+import { cn } from "@/lib/utils"
+
+function Input({ className, type, ...props }: React.ComponentProps<"input">) {
+ return (
+
+ )
+}
+
+export { Input }
diff --git a/frontend/src/components/ui/label.tsx b/frontend/src/components/ui/label.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..b40796dcd73b054123140c26caabee38cd528cf6
--- /dev/null
+++ b/frontend/src/components/ui/label.tsx
@@ -0,0 +1,21 @@
+import * as React from "react"
+import * as LabelPrimitive from "@radix-ui/react-label"
+import { cn } from "@/lib/utils"
+
+function Label({
+ className,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+ )
+}
+
+export { Label }
diff --git a/frontend/src/components/ui/separator.tsx b/frontend/src/components/ui/separator.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..83eaa42315c182d2a3407869ed7e09863cc0a7e6
--- /dev/null
+++ b/frontend/src/components/ui/separator.tsx
@@ -0,0 +1,25 @@
+import * as React from "react"
+import * as SeparatorPrimitive from "@radix-ui/react-separator"
+import { cn } from "@/lib/utils"
+
+function Separator({
+ className,
+ orientation = "horizontal",
+ decorative = true,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+ )
+}
+
+export { Separator }
diff --git a/frontend/src/components/ui/slider.tsx b/frontend/src/components/ui/slider.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..f0a0280a03ddc9573a9f51f34c64c3e947a91cde
--- /dev/null
+++ b/frontend/src/components/ui/slider.tsx
@@ -0,0 +1,30 @@
+import * as React from "react"
+import * as SliderPrimitive from "@radix-ui/react-slider"
+import { cn } from "@/lib/utils"
+
+function Slider({
+ className,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+
+
+
+
+
+ )
+}
+
+export { Slider }
diff --git a/frontend/src/components/ui/sonner.tsx b/frontend/src/components/ui/sonner.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..4dce3d7a263c2cfeeaed9459f0dd35086823dd88
--- /dev/null
+++ b/frontend/src/components/ui/sonner.tsx
@@ -0,0 +1,20 @@
+import { Toaster as Sonner } from "sonner"
+
+function Toaster() {
+ return (
+
+ )
+}
+
+export { Toaster }
diff --git a/frontend/src/components/ui/switch.tsx b/frontend/src/components/ui/switch.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..e42a7c120ee25547bf5c31234189c8c9e52a84bf
--- /dev/null
+++ b/frontend/src/components/ui/switch.tsx
@@ -0,0 +1,31 @@
+import { cn } from "@/lib/utils"
+
+export function Switch({
+ checked,
+ onCheckedChange,
+ id,
+}: {
+ checked: boolean
+ onCheckedChange: (v: boolean) => void
+ id?: string
+}) {
+ return (
+
+ )
+}
diff --git a/frontend/src/components/ui/table.tsx b/frontend/src/components/ui/table.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0f51893af486f4f0891a7786cd3311d70da37447
--- /dev/null
+++ b/frontend/src/components/ui/table.tsx
@@ -0,0 +1,50 @@
+import * as React from "react"
+import { cn } from "@/lib/utils"
+
+function Table({ className, ...props }: React.ComponentProps<"table">) {
+ return (
+
+ )
+}
+
+function TableHeader({ className, ...props }: React.ComponentProps<"thead">) {
+ return
+}
+
+function TableBody({ className, ...props }: React.ComponentProps<"tbody">) {
+ return
+}
+
+function TableRow({ className, ...props }: React.ComponentProps<"tr">) {
+ return (
+
+ )
+}
+
+function TableHead({ className, ...props }: React.ComponentProps<"th">) {
+ return (
+ |
+ )
+}
+
+function TableCell({ className, ...props }: React.ComponentProps<"td">) {
+ return (
+ |
+ )
+}
+
+export { Table, TableHeader, TableBody, TableRow, TableHead, TableCell }
diff --git a/frontend/src/components/ui/tabs.tsx b/frontend/src/components/ui/tabs.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..b1be23222f4c2b90aa4b45f098f405e5f3b91ded
--- /dev/null
+++ b/frontend/src/components/ui/tabs.tsx
@@ -0,0 +1,52 @@
+import * as React from "react"
+import * as TabsPrimitive from "@radix-ui/react-tabs"
+import { cn } from "@/lib/utils"
+
+const Tabs = TabsPrimitive.Root
+
+function TabsList({
+ className,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+ )
+}
+
+function TabsTrigger({
+ className,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+ )
+}
+
+function TabsContent({
+ className,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+ )
+}
+
+export { Tabs, TabsList, TabsTrigger, TabsContent }
diff --git a/frontend/src/components/ui/textarea.tsx b/frontend/src/components/ui/textarea.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..5e1de2606475be9462ced7464c3bc5434664c34a
--- /dev/null
+++ b/frontend/src/components/ui/textarea.tsx
@@ -0,0 +1,19 @@
+import * as React from "react"
+import { cn } from "@/lib/utils"
+
+function Textarea({ className, ...props }: React.ComponentProps<"textarea">) {
+ return (
+
+ )
+}
+
+export { Textarea }
diff --git a/frontend/src/components/ui/tooltip.tsx b/frontend/src/components/ui/tooltip.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0fe9fdd6aec5881f3e135ec598570753b4758cc5
--- /dev/null
+++ b/frontend/src/components/ui/tooltip.tsx
@@ -0,0 +1,29 @@
+import * as React from "react"
+import * as TooltipPrimitive from "@radix-ui/react-tooltip"
+import { cn } from "@/lib/utils"
+
+const TooltipProvider = TooltipPrimitive.Provider
+const Tooltip = TooltipPrimitive.Root
+const TooltipTrigger = TooltipPrimitive.Trigger
+
+function TooltipContent({
+ className,
+ sideOffset = 6,
+ ...props
+}: React.ComponentProps) {
+ return (
+
+
+
+ )
+}
+
+export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }
diff --git a/frontend/src/lib/dashboard.tsx b/frontend/src/lib/dashboard.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..6f88fc36bab9d6a3e2c45e2e925d59fcb16cae2b
--- /dev/null
+++ b/frontend/src/lib/dashboard.tsx
@@ -0,0 +1,244 @@
+import {
+ createContext,
+ useCallback,
+ useContext,
+ useEffect,
+ useMemo,
+ useRef,
+ useState,
+} from "react"
+import type { ReactNode } from "react"
+import { toast } from "sonner"
+import * as api from "./api"
+import type {
+ CompileResult,
+ DetectParams,
+ RunResult,
+ ValidationResult,
+} from "./types"
+
+const DEFAULT_CLASSES = "person, steering wheel, monitor, chair, bag, sticker"
+
+const DEFAULT_RULES = `rules:
+ - name: person-near-steering-wheel
+ when:
+ all:
+ - present: {label: person, min_count: 1}
+ - near: {a: person, b: steering wheel, max_distance: 0.45}
+ gate:
+ enabled: true
+ cooldown: {key: turn-on-pc, minutes: 5}
+ then:
+ - type: simulate
+ name: turn on pc
+`
+
+const DEFAULT_PARAMS: DetectParams = {
+ classes: DEFAULT_CLASSES,
+ confidence: 0.25,
+ frameStride: 5,
+ maxFrames: 120,
+ modelName: "yoloe-26s-seg.pt",
+ imageSize: 0,
+ device: "auto",
+ maxDetections: 0,
+ enableWebhooks: false,
+ webhookUrl: "",
+}
+
+interface DashboardState {
+ params: DetectParams
+ setParam: (key: K, value: DetectParams[K]) => void
+ rulesText: string
+ setRulesText: (text: string) => void
+ videoFile: File | null
+ videoPreviewUrl: string | null
+ setVideo: (file: File | null) => void
+ result: RunResult | null
+ running: boolean
+ error: string | null
+ run: () => Promise
+ validation: ValidationResult | null
+ validate: () => Promise
+ save: () => Promise
+ compile: (instruction: string) => Promise
+ compiling: boolean
+}
+
+const Ctx = createContext(null)
+
+export function DashboardProvider({ children }: { children: ReactNode }) {
+ const [params, setParams] = useState(DEFAULT_PARAMS)
+ const [rulesText, setRulesText] = useState(DEFAULT_RULES)
+ const [videoFile, setVideoFile] = useState(null)
+ const [videoPreviewUrl, setVideoPreviewUrl] = useState(null)
+ const [result, setResult] = useState(null)
+ const [running, setRunning] = useState(false)
+ const [error, setError] = useState(null)
+ const [validation, setValidation] = useState(null)
+ const [compiling, setCompiling] = useState(false)
+ const previewRef = useRef(null)
+
+ // Hydrate defaults from backend config + saved rules on mount.
+ useEffect(() => {
+ api
+ .getConfig()
+ .then((cfg) => {
+ setParams((p) => ({
+ ...p,
+ classes: cfg.default_classes ?? p.classes,
+ modelName: cfg.default_detector_model ?? p.modelName,
+ device: cfg.default_device ?? p.device,
+ imageSize: cfg.default_image_size ?? p.imageSize,
+ maxDetections: cfg.default_max_detections ?? p.maxDetections,
+ webhookUrl: cfg.webhook_url ?? p.webhookUrl,
+ }))
+ })
+ .catch(() => void 0)
+ api
+ .loadRules()
+ .then((r) => r.rules_text && setRulesText(r.rules_text))
+ .catch(() => void 0)
+ }, [])
+
+ const setParam = useCallback(
+ (key: K, value: DetectParams[K]) => {
+ setParams((p) => ({ ...p, [key]: value }))
+ },
+ [],
+ )
+
+ const setVideo = useCallback((file: File | null) => {
+ if (previewRef.current) URL.revokeObjectURL(previewRef.current)
+ if (file) {
+ const url = URL.createObjectURL(file)
+ previewRef.current = url
+ setVideoPreviewUrl(url)
+ } else {
+ previewRef.current = null
+ setVideoPreviewUrl(null)
+ }
+ setVideoFile(file)
+ }, [])
+
+ const run = useCallback(async () => {
+ if (!videoFile) {
+ toast.error("Upload a video first")
+ return
+ }
+ setRunning(true)
+ setError(null)
+ try {
+ const res = await api.detectAndAutomate(videoFile, rulesText, params)
+ setResult(res)
+ if (res.status === "fired") {
+ toast.success(`${res.stats.actions} action${res.stats.actions === 1 ? "" : "s"} fired`)
+ } else {
+ toast.message("No frame matched the automation", {
+ description: `${res.stats.detections} detections across ${res.stats.processed_frames} frames`,
+ })
+ }
+ } catch (e) {
+ const message = e instanceof Error ? e.message : String(e)
+ setError(message)
+ toast.error("Run failed", { description: message })
+ } finally {
+ setRunning(false)
+ }
+ }, [videoFile, rulesText, params])
+
+ const validate = useCallback(async () => {
+ try {
+ const v = await api.validateRules(rulesText)
+ setValidation(v)
+ if (v.ok) toast.success(`Validated — ${v.rules.length} rule${v.rules.length === 1 ? "" : "s"}`)
+ else toast.error("Validation failed")
+ } catch (e) {
+ toast.error("Validation error", {
+ description: e instanceof Error ? e.message : String(e),
+ })
+ }
+ }, [rulesText])
+
+ const save = useCallback(async () => {
+ try {
+ const r = await api.saveRules(rulesText)
+ toast.success(`Saved ${r.rule_count} rule${r.rule_count === 1 ? "" : "s"}`)
+ } catch (e) {
+ toast.error("Save failed", {
+ description: e instanceof Error ? e.message : String(e),
+ })
+ }
+ }, [rulesText])
+
+ const compile = useCallback(
+ async (instruction: string): Promise => {
+ setCompiling(true)
+ try {
+ const c = await api.compileRules(
+ instruction,
+ params.classes,
+ "http://127.0.0.1:8080/v1",
+ "ggml-org/Qwen3-1.7B-GGUF:Q4_K_M",
+ )
+ setRulesText(c.rules_text)
+ toast.success(`Compiled ${c.rule_count} rule${c.rule_count === 1 ? "" : "s"}`)
+ return c
+ } catch (e) {
+ toast.error("Compile failed", {
+ description: e instanceof Error ? e.message : String(e),
+ })
+ return null
+ } finally {
+ setCompiling(false)
+ }
+ },
+ [params.classes],
+ )
+
+ const value = useMemo(
+ () => ({
+ params,
+ setParam,
+ rulesText,
+ setRulesText,
+ videoFile,
+ videoPreviewUrl,
+ setVideo,
+ result,
+ running,
+ error,
+ run,
+ validation,
+ validate,
+ save,
+ compile,
+ compiling,
+ }),
+ [
+ params,
+ setParam,
+ rulesText,
+ videoFile,
+ videoPreviewUrl,
+ setVideo,
+ result,
+ running,
+ error,
+ run,
+ validation,
+ validate,
+ save,
+ compile,
+ compiling,
+ ],
+ )
+
+ return {children}
+}
+
+export function useDashboard(): DashboardState {
+ const ctx = useContext(Ctx)
+ if (!ctx) throw new Error("useDashboard must be used within DashboardProvider")
+ return ctx
+}
diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a40b99ae2005bdcd9af8d0216548d4f316d47277
--- /dev/null
+++ b/frontend/src/main.tsx
@@ -0,0 +1,10 @@
+import { StrictMode } from "react"
+import { createRoot } from "react-dom/client"
+import "@/styles/index.css"
+import App from "@/App"
+
+createRoot(document.getElementById("root")!).render(
+
+
+ ,
+)
diff --git a/frontend/src/modules/activity/ActivityPanel.tsx b/frontend/src/modules/activity/ActivityPanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..eaea014440df7eabc754c239bc5aca92c9f1a748
--- /dev/null
+++ b/frontend/src/modules/activity/ActivityPanel.tsx
@@ -0,0 +1,212 @@
+import { useMemo } from "react"
+import { Inbox } from "lucide-react"
+import { useDashboard } from "@/lib/dashboard"
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"
+import {
+ Table,
+ TableBody,
+ TableCell,
+ TableHead,
+ TableHeader,
+ TableRow,
+} from "@/components/ui/table"
+import { Badge } from "@/components/ui/badge"
+
+// deterministic label → hue (matches the spirit of the backend's md5 colouring)
+function hueFor(label: string): number {
+ let h = 0
+ for (let i = 0; i < label.length; i++) h = (h * 31 + label.charCodeAt(i)) % 360
+ return h
+}
+
+function StatusBadge({ status }: { status: string }) {
+ const variant = status.includes("fail") || status.includes("missing")
+ ? "danger"
+ : status.includes("posted")
+ ? "info"
+ : status.includes("block")
+ ? "warning"
+ : "live"
+ return {status}
+}
+
+export function ActivityPanel() {
+ const { result } = useDashboard()
+ const detections = result?.detections ?? []
+ const events = result?.events ?? []
+
+ const maxT = useMemo(
+ () =>
+ Math.max(
+ 0.001,
+ ...detections.map((d) => d.timestamp_sec),
+ ...events.map((e) => e.timestamp_sec),
+ ),
+ [detections, events],
+ )
+
+ const labels = useMemo(
+ () => Array.from(new Set(detections.map((d) => d.label))),
+ [detections],
+ )
+
+ if (!result) {
+ return (
+
+
+
Run detection to populate the activity timeline.
+
+ )
+ }
+
+ return (
+
+ {/* timeline track */}
+
+
+ 0.0s
+ detection timeline
+ {maxT.toFixed(1)}s
+
+
+ {/* gridlines */}
+ {[0.25, 0.5, 0.75].map((f) => (
+
+ ))}
+ {/* detection ticks */}
+ {detections.map((d, i) => (
+
+ ))}
+ {/* event markers */}
+ {events.map((e, i) => (
+
+
+
+ ))}
+
+ {/* legend */}
+
+ {labels.map((l) => (
+
+
+ {l}
+
+ ))}
+
+
+
+ {/* tables */}
+
+
+
+ Actions
+
+ {events.length}
+
+
+
+ Detections
+
+ {detections.length}
+
+
+
+
+
+ {events.length ? (
+
+
+
+ Time
+ Rule
+ Action
+ Status
+ Frame
+
+
+
+ {events.map((e, i) => (
+
+
+ {e.timestamp_sec.toFixed(1)}s
+
+ {e.rule}
+ {e.action}
+
+
+
+
+ {e.frame_index}
+
+
+ ))}
+
+
+ ) : (
+
+ No actions fired this run.
+
+ )}
+
+
+
+
+
+
+
+ Frame
+ Label
+ Conf.
+ Time
+
+
+
+ {detections.slice(0, 200).map((d, i) => (
+
+
+ {d.frame_index}
+
+
+
+
+ {d.label}
+
+
+
+ {d.confidence.toFixed(2)}
+
+
+ {d.timestamp_sec.toFixed(1)}s
+
+
+ ))}
+
+
+
+
+
+
+ )
+}
diff --git a/frontend/src/modules/detector/DetectorPanel.tsx b/frontend/src/modules/detector/DetectorPanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..9fe0a1d79f1e9da698dc0c4c464bad6aee2b9c7e
--- /dev/null
+++ b/frontend/src/modules/detector/DetectorPanel.tsx
@@ -0,0 +1,221 @@
+import { useRef, useState } from "react"
+import { ChevronDown, Play, RotateCcw, Upload, Loader2 } from "lucide-react"
+import { useDashboard } from "@/lib/dashboard"
+import { Button } from "@/components/ui/button"
+import { Label } from "@/components/ui/label"
+import { Input } from "@/components/ui/input"
+import { Textarea } from "@/components/ui/textarea"
+import { Slider } from "@/components/ui/slider"
+import { Separator } from "@/components/ui/separator"
+import { cn } from "@/lib/utils"
+
+function SliderRow({
+ label,
+ value,
+ display,
+ min,
+ max,
+ step,
+ onChange,
+}: {
+ label: string
+ value: number
+ display: string
+ min: number
+ max: number
+ step: number
+ onChange: (v: number) => void
+}) {
+ return (
+
+
+
+ {display}
+
+
onChange(v)}
+ />
+
+ )
+}
+
+export function DetectorPanel() {
+ const { params, setParam, videoFile, setVideo, run, running } = useDashboard()
+ const fileRef = useRef(null)
+ const [dragging, setDragging] = useState(false)
+ const [advanced, setAdvanced] = useState(false)
+
+ return (
+
+ {/* dropzone */}
+
fileRef.current?.click()}
+ onDragOver={(e) => {
+ e.preventDefault()
+ setDragging(true)
+ }}
+ onDragLeave={() => setDragging(false)}
+ onDrop={(e) => {
+ e.preventDefault()
+ setDragging(false)
+ const f = e.dataTransfer.files?.[0]
+ if (f) setVideo(f)
+ }}
+ className={cn(
+ "group relative flex cursor-pointer flex-col items-center justify-center gap-2 rounded-lg border border-dashed px-4 py-6 text-center transition-colors",
+ dragging
+ ? "border-primary/60 bg-primary/5"
+ : "border-border hover:border-white/20 hover:bg-white/[0.02]",
+ )}
+ >
+
e.target.files?.[0] && setVideo(e.target.files[0])}
+ />
+
+
+
+ {videoFile ? (
+
+
{videoFile.name}
+
+ {(videoFile.size / 1e6).toFixed(1)} MB · click to replace
+
+
+ ) : (
+
+
Drop a clip or click to upload
+
mp4 · webm · mov
+
+ )}
+
+
+
+
+
+
+
+
+ setParam("modelName", e.target.value)}
+ className="font-mono text-xs"
+ />
+
+
+
+ setParam("confidence", Number(v.toFixed(2)))}
+ />
+ setParam("frameStride", v)}
+ />
+ setParam("maxFrames", v)}
+ />
+
+
+
+
+ {advanced && (
+
+
+
+
+ setParam("maxDetections", Number(e.target.value) || 0)}
+ className="font-mono text-xs"
+ placeholder="default"
+ />
+
+
+ )}
+
+
+
+
+
+
+
+
+
+ )
+}
diff --git a/frontend/src/modules/preview/PreviewPanel.tsx b/frontend/src/modules/preview/PreviewPanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..91bae0889aa2fb2ff17fcc037d2c142f9ce9d0be
--- /dev/null
+++ b/frontend/src/modules/preview/PreviewPanel.tsx
@@ -0,0 +1,135 @@
+import { Activity, Film, Loader2, Zap } from "lucide-react"
+import { useDashboard } from "@/lib/dashboard"
+import { mediaUrl } from "@/lib/api"
+import { Badge } from "@/components/ui/badge"
+import { cn } from "@/lib/utils"
+
+function Stat({
+ label,
+ value,
+ accent,
+}: {
+ label: string
+ value: string
+ accent?: boolean
+}) {
+ return (
+
+
+ {label}
+
+
+ {value}
+
+
+ )
+}
+
+export function PreviewPanel() {
+ const { result, running, videoPreviewUrl } = useDashboard()
+ const fired = result?.status === "fired"
+ const src = result ? mediaUrl(result.video_url) : videoPreviewUrl
+
+ return (
+
+
+ {/* scanline / grid atmosphere */}
+
+
+ {src ? (
+
+ ) : (
+
+
+
Upload a clip and run detection to see the annotated replay
+
+ )}
+
+ {/* top HUD */}
+
+
+ {result?.classes?.slice(0, 4).map((c) => (
+
+ {c}
+
+ ))}
+
+ {result &&
+ (fired ? (
+
+
+ automation fired
+
+ ) : (
+
no match
+ ))}
+
+
+ {/* running overlay */}
+ {running && (
+
+
+
+ running inference
+
+
+ )}
+
+ {/* fired banner */}
+ {fired && result && (
+
+
+
+ {result.events[0]?.rule} → {result.events[0]?.action}
+
+
+ {result.events[0]?.timestamp_sec.toFixed(1)}s
+
+
+ )}
+
+
+ {/* stat strip */}
+
+
+
+
+
+
+
+ {!result && (
+
+
+ Engine idle — awaiting first run.
+
+ )}
+
+ )
+}
diff --git a/frontend/src/modules/rules/RuleStudioPanel.tsx b/frontend/src/modules/rules/RuleStudioPanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a0b80e317966aa632ebd04e57c93bf236bbff81a
--- /dev/null
+++ b/frontend/src/modules/rules/RuleStudioPanel.tsx
@@ -0,0 +1,158 @@
+import { useState } from "react"
+import {
+ CheckCircle2,
+ CircleSlash,
+ Loader2,
+ Save,
+ ShieldCheck,
+ Sparkles,
+ Wand2,
+} from "lucide-react"
+import { useDashboard } from "@/lib/dashboard"
+import { Button } from "@/components/ui/button"
+import { Textarea } from "@/components/ui/textarea"
+import { Badge } from "@/components/ui/badge"
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"
+import { cn } from "@/lib/utils"
+
+export function RuleStudioPanel() {
+ const {
+ rulesText,
+ setRulesText,
+ validate,
+ validation,
+ save,
+ compile,
+ compiling,
+ } = useDashboard()
+ const [instruction, setInstruction] = useState(
+ "If a person is near the steering wheel, turn on the PC. Don't repeat for five minutes.",
+ )
+
+ return (
+
+
+
+
+
+ Compose
+
+ Source
+
+ Rules
+ {validation?.ok && (
+
+ {validation.rules.length}
+
+ )}
+
+
+ {validation &&
+ (validation.ok ? (
+
+
+ valid
+
+ ) : (
+
+
+ invalid
+
+ ))}
+
+
+ {/* Compose: NL → rules via llama.cpp */}
+
+
+
+ Describe the automation in plain language. It compiles to validated rules through
+ llama.cpp — never executable code.
+
+
+
+
+ {/* Source: raw editor */}
+
+
+
+ {validation && !validation.ok && validation.error && (
+
+ {validation.error}
+
+ )}
+
+
+
+
+
+
+
+ {/* Rules: parsed view */}
+
+ {validation?.ok && validation.rules.length > 0 ? (
+
+ {validation.rules.map((r) => (
+
+
+
+
+ {r.name}
+
+
+ {r.conditions} condition{r.conditions === 1 ? "" : "s"} →{" "}
+ {r.actions.map((a) => `${a.type}:${a.name}`).join(", ")}
+
+
+
+ {r.enabled ? "enabled" : "disabled"}
+
+
+ ))}
+
+ ) : (
+
+
+
Validate the source to see parsed rules.
+
+
+ )}
+
+
+ )
+}
diff --git a/frontend/src/modules/settings/SettingsPanel.tsx b/frontend/src/modules/settings/SettingsPanel.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a09c06c0477e728d775a08683a7f8c376c3b213a
--- /dev/null
+++ b/frontend/src/modules/settings/SettingsPanel.tsx
@@ -0,0 +1,70 @@
+import { AlertTriangle } from "lucide-react"
+import { useDashboard } from "@/lib/dashboard"
+import { Label } from "@/components/ui/label"
+import { Input } from "@/components/ui/input"
+import { Switch } from "@/components/ui/switch"
+import { Separator } from "@/components/ui/separator"
+import { cn } from "@/lib/utils"
+
+export function SettingsPanel() {
+ const { params, setParam } = useDashboard()
+
+ return (
+
+ {/* webhook dispatch */}
+
+
+
+
+
+ Off by default — actions only simulate.
+
+
+
setParam("enableWebhooks", v)}
+ />
+
+
+
+
+ setParam("webhookUrl", e.target.value)}
+ className="mt-1.5 font-mono text-xs"
+ placeholder="http://127.0.0.1:8123/api/webhook/example"
+ />
+
+
+ {params.enableWebhooks && (
+
+
+ Live POSTs are enabled. Matching rules will hit the URL above for real.
+
+ )}
+
+
+
+
+
+
+ rule compiler
+
+
+ llama.cpp endpoint:{" "}
+ 127.0.0.1:8080/v1
+
+
+ Start with{" "}
+ llama-server -hf ggml-org/Qwen3-1.7B-GGUF:Q4_K_M
+
+
+
+ )
+}
diff --git a/frontend/src/styles/index.css b/frontend/src/styles/index.css
new file mode 100644
index 0000000000000000000000000000000000000000..50b1fac615fec40c205326d4029754675add9dca
--- /dev/null
+++ b/frontend/src/styles/index.css
@@ -0,0 +1,173 @@
+@import "tailwindcss";
+@import "tw-animate-css";
+
+@custom-variant dark (&:is(.dark *));
+
+/* ───────────────────────────────────────────────────────────────
+ Tiny Trigger — "Dark Ops Console" design system
+ Neutral cool-black surfaces · one electric live-signal accent ·
+ distinctive type (Clash Display / Hanken Grotesk / JetBrains Mono)
+ ─────────────────────────────────────────────────────────────── */
+
+:root {
+ --radius: 0.625rem;
+
+ /* surfaces — cool near-black, layered by elevation */
+ --background: oklch(0.145 0.006 264);
+ --foreground: oklch(0.93 0.004 264);
+ --card: oklch(0.185 0.007 264);
+ --card-foreground: oklch(0.93 0.004 264);
+ --popover: oklch(0.205 0.008 264);
+ --popover-foreground: oklch(0.93 0.004 264);
+
+ /* the single confident accent — electric "live signal" chartreuse */
+ --primary: oklch(0.885 0.19 122);
+ --primary-foreground: oklch(0.18 0.03 130);
+
+ --secondary: oklch(0.235 0.008 264);
+ --secondary-foreground: oklch(0.93 0.004 264);
+ --muted: oklch(0.235 0.008 264);
+ --muted-foreground: oklch(0.64 0.012 264);
+ --accent: oklch(0.235 0.008 264);
+ --accent-foreground: oklch(0.93 0.004 264);
+
+ /* semantic */
+ --destructive: oklch(0.637 0.21 25.3);
+ --destructive-foreground: oklch(0.97 0.01 20);
+ --warning: oklch(0.79 0.15 78);
+ --warning-foreground: oklch(0.2 0.03 78);
+ --info: oklch(0.78 0.12 222);
+ --info-foreground: oklch(0.18 0.03 240);
+
+ --border: oklch(1 0 0 / 8%);
+ --input: oklch(1 0 0 / 12%);
+ --ring: oklch(0.885 0.19 122 / 55%);
+
+ /* fonts */
+ --font-display: "Clash Display", ui-sans-serif, system-ui, sans-serif;
+ --font-sans: "Hanken Grotesk", ui-sans-serif, system-ui, sans-serif;
+ --font-mono: "JetBrains Mono", ui-monospace, monospace;
+}
+
+@theme inline {
+ --color-background: var(--background);
+ --color-foreground: var(--foreground);
+ --color-card: var(--card);
+ --color-card-foreground: var(--card-foreground);
+ --color-popover: var(--popover);
+ --color-popover-foreground: var(--popover-foreground);
+ --color-primary: var(--primary);
+ --color-primary-foreground: var(--primary-foreground);
+ --color-secondary: var(--secondary);
+ --color-secondary-foreground: var(--secondary-foreground);
+ --color-muted: var(--muted);
+ --color-muted-foreground: var(--muted-foreground);
+ --color-accent: var(--accent);
+ --color-accent-foreground: var(--accent-foreground);
+ --color-destructive: var(--destructive);
+ --color-destructive-foreground: var(--destructive-foreground);
+ --color-warning: var(--warning);
+ --color-warning-foreground: var(--warning-foreground);
+ --color-info: var(--info);
+ --color-info-foreground: var(--info-foreground);
+ --color-border: var(--border);
+ --color-input: var(--input);
+ --color-ring: var(--ring);
+
+ --font-display: var(--font-display);
+ --font-sans: var(--font-sans);
+ --font-mono: var(--font-mono);
+
+ --radius-sm: calc(var(--radius) - 4px);
+ --radius-md: calc(var(--radius) - 2px);
+ --radius-lg: var(--radius);
+ --radius-xl: calc(var(--radius) + 4px);
+}
+
+@layer base {
+ * {
+ border-color: var(--color-border);
+ }
+
+ html {
+ -webkit-font-smoothing: antialiased;
+ text-rendering: optimizeLegibility;
+ }
+
+ body {
+ margin: 0;
+ min-height: 100vh;
+ background-color: var(--color-background);
+ color: var(--color-foreground);
+ font-family: var(--font-sans);
+ font-feature-settings: "ss01", "cv01", "tnum";
+ /* atmospheric depth: dual radial glows + faint engineering grid */
+ background-image:
+ radial-gradient(
+ 90rem 60rem at 82% -10%,
+ oklch(0.885 0.19 122 / 0.07),
+ transparent 60%
+ ),
+ radial-gradient(
+ 70rem 50rem at -10% 110%,
+ oklch(0.78 0.12 222 / 0.06),
+ transparent 55%
+ ),
+ linear-gradient(
+ to right,
+ oklch(1 0 0 / 0.018) 1px,
+ transparent 1px
+ ),
+ linear-gradient(
+ to bottom,
+ oklch(1 0 0 / 0.018) 1px,
+ transparent 1px
+ );
+ background-size: 100% 100%, 100% 100%, 56px 56px, 56px 56px;
+ background-attachment: fixed;
+ }
+
+ ::selection {
+ background: oklch(0.885 0.19 122 / 0.28);
+ }
+
+ /* refined dark scrollbars */
+ ::-webkit-scrollbar { width: 10px; height: 10px; }
+ ::-webkit-scrollbar-thumb {
+ background: oklch(1 0 0 / 0.1);
+ border-radius: 999px;
+ border: 2px solid transparent;
+ background-clip: content-box;
+ }
+ ::-webkit-scrollbar-thumb:hover { background: oklch(1 0 0 / 0.18); background-clip: content-box; }
+}
+
+@layer utilities {
+ .font-display { font-family: var(--font-display); }
+ .text-balance { text-wrap: balance; }
+
+ /* the live-signal pulse used for "fired / active" states */
+ .signal-dot {
+ box-shadow: 0 0 0 0 var(--color-primary);
+ animation: signal-pulse 1.8s cubic-bezier(0.4, 0, 0.2, 1) infinite;
+ }
+ @keyframes signal-pulse {
+ 0% { box-shadow: 0 0 0 0 oklch(0.885 0.19 122 / 0.5); }
+ 70% { box-shadow: 0 0 0 8px oklch(0.885 0.19 122 / 0); }
+ 100% { box-shadow: 0 0 0 0 oklch(0.885 0.19 122 / 0); }
+ }
+
+ /* staggered entrance for panels */
+ .reveal {
+ opacity: 0;
+ animation: reveal-up 0.55s cubic-bezier(0.22, 1, 0.36, 1) forwards;
+ }
+ @keyframes reveal-up {
+ from { opacity: 0; transform: translateY(12px); }
+ to { opacity: 1; transform: translateY(0); }
+ }
+
+ .hairline {
+ background-image: linear-gradient(to right, transparent, var(--color-border), transparent);
+ }
+}
diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json
new file mode 100644
index 0000000000000000000000000000000000000000..1e66a7c99a41c46321b4a981a62d6079353dda87
--- /dev/null
+++ b/frontend/tsconfig.app.json
@@ -0,0 +1,22 @@
+{
+ "compilerOptions": {
+ "target": "ES2022",
+ "useDefineForClassFields": true,
+ "lib": ["ES2022", "DOM", "DOM.Iterable"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "verbatimModuleSyntax": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+ "jsx": "react-jsx",
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "noFallthroughCasesInSwitch": true,
+ "baseUrl": ".",
+ "paths": { "@/*": ["./src/*"] }
+ },
+ "include": ["src"]
+}
diff --git a/frontend/tsconfig.app.tsbuildinfo b/frontend/tsconfig.app.tsbuildinfo
new file mode 100644
index 0000000000000000000000000000000000000000..75955180a8c397cd74e02e7ae5496793f8388f1f
--- /dev/null
+++ b/frontend/tsconfig.app.tsbuildinfo
@@ -0,0 +1 @@
+{"root":["./src/app.tsx","./src/main.tsx","./src/vite-env.d.ts","./src/components/layout/modulepanel.tsx","./src/components/layout/section.tsx","./src/components/layout/sidebar.tsx","./src/components/layout/topbar.tsx","./src/components/ui/badge.tsx","./src/components/ui/button.tsx","./src/components/ui/card.tsx","./src/components/ui/input.tsx","./src/components/ui/label.tsx","./src/components/ui/separator.tsx","./src/components/ui/slider.tsx","./src/components/ui/sonner.tsx","./src/components/ui/switch.tsx","./src/components/ui/table.tsx","./src/components/ui/tabs.tsx","./src/components/ui/textarea.tsx","./src/components/ui/tooltip.tsx","./src/lib/api.ts","./src/lib/dashboard.tsx","./src/lib/types.ts","./src/lib/utils.ts","./src/modules/registry.ts","./src/modules/activity/activitypanel.tsx","./src/modules/detector/detectorpanel.tsx","./src/modules/preview/previewpanel.tsx","./src/modules/rules/rulestudiopanel.tsx","./src/modules/settings/settingspanel.tsx"],"version":"5.9.3"}
\ No newline at end of file
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
new file mode 100644
index 0000000000000000000000000000000000000000..50da56ecfc794f8aac0d76117d29142017cfb63b
--- /dev/null
+++ b/frontend/tsconfig.json
@@ -0,0 +1,11 @@
+{
+ "files": [],
+ "references": [
+ { "path": "./tsconfig.app.json" },
+ { "path": "./tsconfig.node.json" }
+ ],
+ "compilerOptions": {
+ "baseUrl": ".",
+ "paths": { "@/*": ["./src/*"] }
+ }
+}
diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a8860f90d30d0b1009245222263eb8a9db57e0c
--- /dev/null
+++ b/frontend/tsconfig.node.json
@@ -0,0 +1,18 @@
+{
+ "compilerOptions": {
+ "target": "ES2022",
+ "lib": ["ES2023"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "verbatimModuleSyntax": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "types": ["node"]
+ },
+ "include": ["vite.config.ts"]
+}
diff --git a/frontend/tsconfig.node.tsbuildinfo b/frontend/tsconfig.node.tsbuildinfo
new file mode 100644
index 0000000000000000000000000000000000000000..62c7bf924f317e15d595554818351f3ac278c108
--- /dev/null
+++ b/frontend/tsconfig.node.tsbuildinfo
@@ -0,0 +1 @@
+{"root":["./vite.config.ts"],"version":"5.9.3"}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..36c8def556ee0499b1301036482b77ded335c2c0
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,4316 @@
+# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand.
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+description = "Document parameters, class attributes, return types, and variables inline, with Annotated."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+ {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"},
+ {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"},
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.13.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+ {file = "anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708"},
+ {file = "anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+trio = ["trio (>=0.32.0)"]
+
+[[package]]
+name = "audioop-lts"
+version = "0.2.2"
+description = "LTS Port of Python audioop"
+optional = false
+python-versions = ">=3.13"
+groups = ["main"]
+markers = "python_version >= \"3.13\""
+files = [
+ {file = "audioop_lts-0.2.2-cp313-abi3-macosx_10_13_universal2.whl", hash = "sha256:fd3d4602dc64914d462924a08c1a9816435a2155d74f325853c1f1ac3b2d9800"},
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..469662ea482d06255238dd8946b6ddeb96df3dd6
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,31 @@
+[project]
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+version = "0.1.0"
+description = "Open-vocabulary video automations with YOLOE and llama.cpp"
+authors = [
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+ "torchaudio (>=2.11.0,<3.0.0)",
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+
+[tool.poetry.group.dev.dependencies]
+pytest = ">=9.0"
+
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diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..4584de7e860925957a2daa58cfb4de8acc1e4802
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,3 @@
+[pytest]
+testpaths = tests
+pythonpath = .
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3ccce80b3f20b74aecb5020adaef49b08b7a418
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,2 @@
+-r requirements.txt
+pytest>=9.0
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e425f8c04926569220f8ec297fda84836fbc8c37
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
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+requests>=2.32
+numpy>=1.26
diff --git a/server.py b/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d91df69c9ef1d2cf8115e59fe367ee811abf5d1
--- /dev/null
+++ b/server.py
@@ -0,0 +1,262 @@
+"""gradio.Server backend for the custom Tiny Trigger dashboard.
+
+Runs independently of ``app.py`` (the legacy Gradio Blocks UI). It serves the
+built React frontend (``frontend/dist``) and exposes the Tiny Trigger engine as
+``@app.api`` endpoints that keep Gradio's queuing / SSE / gradio_client
+compatibility. The frontend talks to these via the ``@gradio/client`` JS library.
+
+ poetry run python server.py # serves the dashboard on :7860
+
+The heavy ML imports stay lazy (inside ``tiny_trigger``); importing this module
+does not require torch/ultralytics.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from gradio import FileData, Server
+from fastapi.responses import FileResponse, HTMLResponse, JSONResponse
+
+from tiny_trigger import (
+ compile_automation_with_llamacpp,
+ evaluate_video_detections,
+ load_automation_text,
+ parse_class_prompt,
+ process_video,
+)
+from tiny_trigger.actions import dispatch_events
+from tiny_trigger.store import (
+ load_local_config,
+ load_runtime_state,
+ load_saved_automations,
+ save_automations,
+ save_runtime_state,
+ append_events,
+)
+from tiny_trigger.video import render_automation_video
+
+# ── paths ──────────────────────────────────────────────────────────────────
+ROOT = Path(__file__).parent
+DIST = ROOT / "frontend" / "dist"
+ASSETS = DIST / "assets"
+RENDERS = ROOT / ".local" / "renders"
+RENDERS.mkdir(parents=True, exist_ok=True)
+
+DEFAULT_MODEL = "yoloe-26s-seg.pt"
+
+
+# ── serialization helpers (JSON-shaped, for the custom frontend) ─────────────
+def _detection_dict(d: Any) -> dict[str, Any]:
+ return {
+ "frame_index": d.frame_index,
+ "timestamp_sec": round(d.timestamp_sec, 3),
+ "label": d.label,
+ "confidence": round(d.confidence, 4),
+ "bbox_xyxy": [round(v, 1) for v in d.bbox_xyxy],
+ "bbox_xyxy_norm": [round(v, 4) for v in d.bbox_xyxy_norm],
+ }
+
+
+def _event_dict(e: Any) -> dict[str, Any]:
+ return {
+ "rule": e.rule,
+ "action": e.action,
+ "type": e.type,
+ "frame_index": e.frame_index,
+ "timestamp_sec": round(e.timestamp_sec, 3),
+ "status": e.status,
+ "url": e.url,
+ "response_status": e.response_status,
+ "error": e.error,
+ }
+
+
+def _media_url(path_str: str) -> str:
+ """Map a rendered file path under RENDERS to a served /media URL."""
+ return f"/media/{Path(path_str).name}"
+
+
+def _file_path(video: Any) -> str | None:
+ """FileData arrives as a dict over the wire; tolerate the object form too."""
+ if video is None:
+ return None
+ if isinstance(video, dict):
+ return video.get("path")
+ return getattr(video, "path", None)
+
+
+# ── the server ───────────────────────────────────────────────────────────────
+app = Server(title="Tiny Trigger", description="Open-vocabulary video automations")
+
+
+@app.api(name="detect_and_automate")
+def detect_and_automate(
+ video: FileData,
+ classes: str,
+ rules_text: str,
+ confidence: float = 0.25,
+ frame_stride: int = 5,
+ max_frames: int = 120,
+ model_name: str = DEFAULT_MODEL,
+ image_size: int = 0,
+ device: str = "auto",
+ max_detections: int = 0,
+ enable_webhooks: bool = False,
+ webhook_url: str = "",
+) -> dict:
+ """Detect once, evaluate rules, dispatch actions, render an overlay clip."""
+ video_path = _file_path(video)
+ if not video_path:
+ raise ValueError("A video file is required.")
+
+ rules = load_automation_text(rules_text)
+ state = load_runtime_state()
+
+ result = process_video(
+ video_path=video_path,
+ class_prompt=classes,
+ confidence=confidence,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ model_name=model_name or DEFAULT_MODEL,
+ image_size=image_size or None,
+ device=None if device in ("", "auto") else device,
+ max_detections=max_detections or None,
+ output_dir=str(RENDERS),
+ )
+
+ events, last_fired = evaluate_video_detections(
+ rules.rules, result.detections, last_fired=state.last_fired
+ )
+ dispatched = dispatch_events(
+ events, enable_webhooks=enable_webhooks, webhook_url=webhook_url or None
+ )
+ state.last_fired = last_fired
+ save_runtime_state(state)
+ append_events(dispatched)
+
+ automation_path = render_automation_video(
+ source_video_path=video_path,
+ detections=result.detections,
+ events=dispatched,
+ frame_stride=frame_stride,
+ max_frames=max_frames,
+ output_dir=str(RENDERS),
+ )
+
+ fired = len(dispatched)
+ return {
+ "status": "fired" if fired else "no_match",
+ "video_url": _media_url(automation_path),
+ "annotated_url": _media_url(result.output_video_path),
+ "classes": result.classes,
+ "stats": {
+ "detections": len(result.detections),
+ "rules": len(rules.rules),
+ "actions": fired,
+ "processed_frames": result.processed_frames,
+ "source_fps": round(result.source_fps, 2),
+ "output_fps": round(result.output_fps, 2),
+ },
+ "detections": [_detection_dict(d) for d in result.detections],
+ "events": [_event_dict(e) for e in dispatched],
+ }
+
+
+@app.api(name="compile_rules")
+def compile_rules(
+ instruction: str,
+ classes: str = "",
+ base_url: str = "http://127.0.0.1:8080/v1",
+ model: str = "ggml-org/Qwen3-1.7B-GGUF:Q4_K_M",
+) -> dict:
+ """Compile a natural-language request into validated automation rules."""
+ class_names = parse_class_prompt(classes) if classes else []
+ compiled = compile_automation_with_llamacpp(
+ instruction=instruction,
+ class_names=class_names,
+ base_url=base_url,
+ model=model,
+ )
+ return {
+ "rules_text": compiled.document.model_dump_json(by_alias=True, indent=2),
+ "raw_text": compiled.raw_text,
+ "rule_count": len(compiled.document.rules),
+ }
+
+
+@app.api(name="validate_rules")
+def validate_rules(rules_text: str) -> dict:
+ """Validate JSON/YAML rules; return a structured summary or the error."""
+ try:
+ document = load_automation_text(rules_text)
+ except Exception as exc: # noqa: BLE001 — surface any validation error to the UI
+ return {"ok": False, "error": str(exc), "rules": []}
+ return {
+ "ok": True,
+ "error": None,
+ "rules": [
+ {
+ "name": r.name,
+ "enabled": r.gate.enabled,
+ "conditions": len(r.when.all_conditions) + len(r.when.any_conditions),
+ "actions": [{"type": a.type, "name": a.name} for a in r.then],
+ }
+ for r in document.rules
+ ],
+ }
+
+
+@app.api(name="save_rules")
+def save_rules(rules_text: str) -> dict:
+ document = load_automation_text(rules_text)
+ save_automations(document)
+ return {"ok": True, "rule_count": len(document.rules)}
+
+
+@app.api(name="load_rules")
+def load_rules() -> dict:
+ document = load_saved_automations()
+ if document is None:
+ return {"rules_text": None}
+ return {"rules_text": document.model_dump_json(by_alias=True, indent=2)}
+
+
+@app.api(name="get_config")
+def get_config() -> dict:
+ cfg = load_local_config()
+ return cfg.model_dump()
+
+
+# ── static frontend + media (custom routes take priority over gradio's) ──────
+@app.get("/", response_class=HTMLResponse)
+def index() -> Any:
+ index_html = DIST / "index.html"
+ if not index_html.exists():
+ return HTMLResponse(
+ "Frontend not built
Run pnpm --dir frontend build.
",
+ status_code=503,
+ )
+ return FileResponse(index_html)
+
+
+@app.get("/assets/{file_path:path}")
+def assets(file_path: str) -> Any:
+ target = (ASSETS / file_path).resolve()
+ if not str(target).startswith(str(ASSETS.resolve())) or not target.is_file():
+ return JSONResponse({"error": "not found"}, status_code=404)
+ return FileResponse(target)
+
+
+@app.get("/media/{file_path:path}")
+def media(file_path: str) -> Any:
+ target = (RENDERS / file_path).resolve()
+ if not str(target).startswith(str(RENDERS.resolve())) or not target.is_file():
+ return JSONResponse({"error": "not found"}, status_code=404)
+ return FileResponse(target, media_type="video/mp4")
+
+
+if __name__ == "__main__":
+ app.launch(server_name="127.0.0.1", server_port=7860, show_error=True)
diff --git a/tests/test_automation.py b/tests/test_automation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e47aec8879a63b0df912a569a9a92dbde86d803
--- /dev/null
+++ b/tests/test_automation.py
@@ -0,0 +1,172 @@
+from __future__ import annotations
+
+import json
+
+import pytest
+from pydantic import ValidationError
+
+from tiny_trigger.automation import RuleEngine, evaluate_video_detections, load_automation_text
+from tiny_trigger.models import Detection
+
+
+def detection(label: str, box: tuple[float, float, float, float], frame: int = 0, time: float = 0.0) -> Detection:
+ return Detection(
+ frame_index=frame,
+ timestamp_sec=time,
+ label=label,
+ confidence=0.9,
+ bbox_xyxy=(0.0, 0.0, 10.0, 10.0),
+ bbox_xyxy_norm=box,
+ )
+
+
+def test_presence_near_and_cooldown_fire_once_per_window() -> None:
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "feed-cat",
+ "when": {
+ "all": [
+ {"present": {"label": "cat", "min_count": 1}},
+ {"near": {"a": "cat", "b": "feeder robot", "max_distance": 0.35}},
+ {"cooldown": {"key": "feed-cat", "seconds": 60}},
+ ]
+ },
+ "then": [{"type": "simulate", "name": "feed cat"}],
+ }
+ ]
+ }
+ )
+ )
+ engine = RuleEngine(document.rules)
+ frame = [
+ detection("cat", (0.10, 0.10, 0.20, 0.20), time=0.0),
+ detection("feeder robot", (0.25, 0.12, 0.35, 0.22), time=0.0),
+ ]
+
+ assert len(engine.evaluate_frame(frame, frame_index=0, timestamp_sec=0.0)) == 1
+ assert engine.evaluate_frame(frame, frame_index=1, timestamp_sec=30.0) == []
+ assert len(engine.evaluate_frame(frame, frame_index=2, timestamp_sec=61.0)) == 1
+
+
+def test_count_condition() -> None:
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "two-packages",
+ "when": {"all": [{"count": {"label": "package", "min": 2}}]},
+ "then": [{"type": "simulate", "name": "notify"}],
+ }
+ ]
+ }
+ )
+ )
+ engine = RuleEngine(document.rules)
+ one = [detection("package", (0.1, 0.1, 0.2, 0.2))]
+ two = [*one, detection("package", (0.4, 0.4, 0.5, 0.5))]
+
+ assert engine.evaluate_frame(one, frame_index=0, timestamp_sec=0.0) == []
+ assert len(engine.evaluate_frame(two, frame_index=1, timestamp_sec=1.0)) == 1
+
+
+def test_gate_cooldown_fire_once_per_window() -> None:
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "turn-on-pc",
+ "when": {
+ "all": [
+ {"present": {"label": "person"}},
+ {"near": {"a": "person", "b": "steering wheel", "max_distance": 0.45}},
+ ]
+ },
+ "gate": {"enabled": True, "cooldown": {"key": "turn-on-pc", "seconds": 60}},
+ "then": [{"type": "simulate", "name": "turn on pc"}],
+ }
+ ]
+ }
+ )
+ )
+ engine = RuleEngine(document.rules)
+ frame = [
+ detection("person", (0.10, 0.10, 0.20, 0.20), time=0.0),
+ detection("steering wheel", (0.25, 0.12, 0.35, 0.22), time=0.0),
+ ]
+
+ assert len(engine.evaluate_frame(frame, frame_index=0, timestamp_sec=0.0)) == 1
+ assert engine.evaluate_frame(frame, frame_index=1, timestamp_sec=30.0) == []
+ assert len(engine.evaluate_frame(frame, frame_index=2, timestamp_sec=61.0)) == 1
+
+
+def test_disabled_gate_blocks_rule() -> None:
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "disabled",
+ "when": {"all": [{"present": {"label": "person"}}]},
+ "gate": {"enabled": False},
+ "then": [{"type": "simulate", "name": "turn on pc"}],
+ }
+ ]
+ }
+ )
+ )
+ engine = RuleEngine(document.rules)
+
+ assert engine.evaluate_frame([detection("person", (0.1, 0.1, 0.2, 0.2))], frame_index=0, timestamp_sec=0.0) == []
+
+
+def test_evaluate_video_detections_returns_updated_last_fired() -> None:
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "turn-on-pc",
+ "when": {"all": [{"present": {"label": "person"}}]},
+ "gate": {"cooldown": {"key": "turn-on-pc", "seconds": 60}},
+ "then": [{"type": "simulate", "name": "turn on pc"}],
+ }
+ ]
+ }
+ )
+ )
+ events, last_fired = evaluate_video_detections(
+ document.rules,
+ [detection("person", (0.1, 0.1, 0.2, 0.2), frame=2, time=12.0)],
+ )
+ blocked_events, unchanged_last_fired = evaluate_video_detections(
+ document.rules,
+ [detection("person", (0.1, 0.1, 0.2, 0.2), frame=3, time=30.0)],
+ last_fired=last_fired,
+ )
+
+ assert len(events) == 1
+ assert last_fired == {"turn-on-pc": 12.0}
+ assert blocked_events == []
+ assert unchanged_last_fired == last_fired
+
+
+def test_invalid_rule_rejected() -> None:
+ with pytest.raises(ValidationError):
+ load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "bad",
+ "when": {"all": [{"present": {"label": "cat"}, "near": {"a": "cat", "b": "bowl"}}]},
+ "then": [{"type": "simulate", "name": "bad"}],
+ }
+ ]
+ }
+ )
+ )
diff --git a/tests/test_detector.py b/tests/test_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0c9fd1be70cd68543b2c156f1f569bebb3e2390
--- /dev/null
+++ b/tests/test_detector.py
@@ -0,0 +1,7 @@
+from __future__ import annotations
+
+from tiny_trigger.detector import parse_class_prompt
+
+
+def test_parse_class_prompt_splits_and_dedupes() -> None:
+ assert parse_class_prompt(" cat, feeder robot\npackage; cat ") == ["cat", "feeder robot", "package"]
diff --git a/tests/test_llm.py b/tests/test_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..6051d014af59d4f36a126a0dacd210c431e6a83b
--- /dev/null
+++ b/tests/test_llm.py
@@ -0,0 +1,126 @@
+from __future__ import annotations
+
+import json
+import sys
+
+from tiny_trigger.automation import AutomationDocument
+from tiny_trigger.llm import (
+ SYSTEM_PROMPT,
+ _build_user_prompt,
+ _chat_payload,
+ compile_automation_with_llamacpp,
+ _post_chat_completion,
+ _validate_compile_result,
+ extract_json_object,
+)
+
+
+def test_extract_json_object_from_fenced_response() -> None:
+ raw = """```json
+{"rules": [{"name": "notify", "when": {"all": [{"present": {"label": "package"}}]}, "then": [{"type": "simulate", "name": "notify"}]}]}
+```"""
+ data = json.loads(extract_json_object(raw))
+ document = AutomationDocument.model_validate(data)
+
+ assert document.rules[0].name == "notify"
+
+
+def test_llamacpp_payload_uses_json_object() -> None:
+ payload = _chat_payload(model="qwen", user_prompt="compile this", response_format="json_object")
+
+ assert payload["response_format"] == {"type": "json_object"}
+
+
+def test_prompt_includes_validation_schema() -> None:
+ prompt = _build_user_prompt(instruction="if package at door notify me", class_names=["package", "door"])
+
+ assert "Available detection labels: package, door" in prompt
+ assert "Full validation schema" in prompt
+ assert "AutomationDocument" in prompt
+
+
+def test_prompt_teaches_near_relations() -> None:
+ prompt = _build_user_prompt(
+ instruction="If person near steering wheel then turn on pc.",
+ class_names=["person", "steering wheel"],
+ )
+
+ assert '"near": {"a": "person", "b": "steering wheel", "max_distance": 0.45}' in prompt
+ assert "Do not replace a near relation with two present conditions." in SYSTEM_PROMPT
+ assert "Use state gates in gate: enabled, cooldown." in SYSTEM_PROMPT
+ assert '"gate": {"enabled": true}' in prompt
+ assert '"gate": {"enabled": true, "cooldown": {"key": "package-at-door", "minutes": 15}}' in prompt
+
+
+def test_invalid_empty_object_fails_validation() -> None:
+ try:
+ _validate_compile_result("{}")
+ except Exception as exc:
+ assert "rules" in str(exc)
+ else:
+ raise AssertionError("{} should not validate as an automation document")
+
+
+def test_post_chat_completion_extracts_content() -> None:
+ class Response:
+ def raise_for_status(self) -> None:
+ return None
+
+ def json(self):
+ return {"choices": [{"message": {"content": "{\"rules\": []}"}}]}
+
+ class Requests:
+ def post(self, endpoint, json, timeout):
+ assert endpoint == "http://localhost/v1/chat/completions"
+ assert json["response_format"] == {"type": "json_object"}
+ assert timeout == 1
+ return Response()
+
+ assert (
+ _post_chat_completion(
+ endpoint="http://localhost/v1/chat/completions",
+ model="qwen",
+ user_prompt="compile",
+ timeout=1,
+ requests_module=Requests(),
+ )
+ == "{\"rules\": []}"
+ )
+
+
+def test_compile_retries_after_invalid_response(monkeypatch) -> None:
+ class Response:
+ def __init__(self, content: str) -> None:
+ self.content = content
+
+ def raise_for_status(self) -> None:
+ return None
+
+ def json(self):
+ return {"choices": [{"message": {"content": self.content}}]}
+
+ class Requests:
+ calls = 0
+
+ @classmethod
+ def post(cls, endpoint, json, timeout):
+ cls.calls += 1
+ if cls.calls == 1:
+ return Response("{}")
+ return Response(
+ '{"rules":[{"name":"notify","when":{"all":[{"present":{"label":"person"}}]},'
+ '"then":[{"type":"simulate","name":"notify"}]}]}'
+ )
+
+ monkeypatch.setitem(sys.modules, "requests", Requests)
+
+ result = compile_automation_with_llamacpp(
+ instruction="if person present notify",
+ class_names=["person"],
+ base_url="http://localhost/v1",
+ model="qwen",
+ timeout=1,
+ )
+
+ assert Requests.calls == 2
+ assert result.document.rules[0].name == "notify"
diff --git a/tests/test_store.py b/tests/test_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c6994d79e8319f91aa02b0ecf915c50c21b128b
--- /dev/null
+++ b/tests/test_store.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import json
+
+from tiny_trigger.automation import load_automation_text
+from tiny_trigger.models import ActionEvent
+from tiny_trigger.store import (
+ RuntimeState,
+ append_events,
+ load_local_config,
+ load_runtime_state,
+ load_saved_automations,
+ save_automations,
+ save_runtime_state,
+)
+
+
+def test_local_config_yaml(tmp_path) -> None:
+ path = tmp_path / "config.yaml"
+ path.write_text("default_device: cuda:0\nwebhook_url: http://example.test\n", encoding="utf-8")
+
+ config = load_local_config(path)
+
+ assert config.default_device == "cuda:0"
+ assert config.webhook_url == "http://example.test"
+
+
+def test_save_and_load_automations(tmp_path) -> None:
+ path = tmp_path / "automations.json"
+ document = load_automation_text(
+ json.dumps(
+ {
+ "rules": [
+ {
+ "name": "notify",
+ "when": {"all": [{"present": {"label": "person"}}]},
+ "then": [{"type": "simulate", "name": "notify"}],
+ }
+ ]
+ }
+ )
+ )
+
+ save_automations(document, path)
+ loaded = load_saved_automations(path)
+
+ assert loaded is not None
+ assert loaded.rules[0].name == "notify"
+
+
+def test_runtime_state_roundtrip(tmp_path) -> None:
+ path = tmp_path / "state.json"
+ save_runtime_state(RuntimeState(last_fired={"turn-on-pc": 12.0}), path)
+
+ assert load_runtime_state(path).last_fired == {"turn-on-pc": 12.0}
+
+
+def test_append_events_jsonl(tmp_path) -> None:
+ path = tmp_path / "events.jsonl"
+ append_events(
+ [
+ ActionEvent(
+ rule="turn-on-pc",
+ action="turn on pc",
+ type="simulate",
+ frame_index=0,
+ timestamp_sec=0.0,
+ )
+ ],
+ path,
+ )
+
+ rows = path.read_text(encoding="utf-8").splitlines()
+ assert len(rows) == 1
+ assert json.loads(rows[0])["rule"] == "turn-on-pc"
diff --git a/tests/test_video.py b/tests/test_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f4c5087ea256cedfe376fa4af7e37f52e5208d5
--- /dev/null
+++ b/tests/test_video.py
@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from tiny_trigger.models import ActionEvent, Detection
+from tiny_trigger.video import process_video, render_automation_video
+
+
+class FakeDetector:
+ class_names = ["cat"]
+
+ def detect(
+ self,
+ frame: Any,
+ *,
+ frame_index: int,
+ timestamp_sec: float,
+ confidence: float,
+ image_size: int | None = None,
+ max_detections: int | None = None,
+ ) -> list[Detection]:
+ assert image_size == 960
+ assert max_detections == 20
+ return [
+ Detection(
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ label="cat",
+ confidence=0.99,
+ bbox_xyxy=(2.0, 2.0, 12.0, 12.0),
+ bbox_xyxy_norm=(0.1, 0.1, 0.6, 0.6),
+ )
+ ]
+
+
+def test_process_video_with_fake_detector(tmp_path: Path) -> None:
+ video_path = _make_video(tmp_path)
+
+ result = process_video(
+ video_path=str(video_path),
+ class_prompt="cat",
+ frame_stride=2,
+ max_frames=2,
+ image_size=960,
+ max_detections=20,
+ detector=FakeDetector(),
+ output_dir=str(tmp_path),
+ )
+
+ assert Path(result.output_video_path).exists()
+ assert result.processed_frames == 2
+ assert [item.frame_index for item in result.detections] == [0, 2]
+
+
+def test_render_automation_video_with_fired_event(tmp_path: Path) -> None:
+ video_path = _make_video(tmp_path)
+ detections = [
+ Detection(
+ frame_index=0,
+ timestamp_sec=0.0,
+ label="cat",
+ confidence=0.99,
+ bbox_xyxy=(2.0, 2.0, 12.0, 12.0),
+ bbox_xyxy_norm=(0.1, 0.1, 0.6, 0.6),
+ )
+ ]
+ events = [
+ ActionEvent(
+ rule="cat-rule",
+ action="feed cat",
+ type="simulate",
+ frame_index=0,
+ timestamp_sec=0.0,
+ status="simulated",
+ )
+ ]
+
+ output_path = render_automation_video(
+ source_video_path=str(video_path),
+ detections=detections,
+ events=events,
+ frame_stride=2,
+ max_frames=2,
+ output_dir=str(tmp_path),
+ )
+
+ assert Path(output_path).exists()
+ assert output_path.endswith("-automated.mp4")
+
+
+def test_process_video_repeats_low_fps_sampled_frames_for_playback(tmp_path: Path) -> None:
+ cv2 = __import__("cv2")
+ video_path = _make_video(tmp_path, fps=5.0, frames=15)
+
+ result = process_video(
+ video_path=str(video_path),
+ class_prompt="cat",
+ frame_stride=5,
+ max_frames=3,
+ image_size=960,
+ max_detections=20,
+ detector=FakeDetector(),
+ output_dir=str(tmp_path),
+ )
+
+ capture = cv2.VideoCapture(result.output_video_path)
+ try:
+ assert capture.get(cv2.CAP_PROP_FPS) == 8.0
+ assert capture.get(cv2.CAP_PROP_FRAME_COUNT) == 24
+ finally:
+ capture.release()
+
+
+def test_process_video_writes_faststart_mp4(tmp_path: Path) -> None:
+ video_path = _make_video(tmp_path, fps=5.0, frames=6)
+
+ result = process_video(
+ video_path=str(video_path),
+ class_prompt="cat",
+ frame_stride=2,
+ max_frames=2,
+ image_size=960,
+ max_detections=20,
+ detector=FakeDetector(),
+ output_dir=str(tmp_path),
+ )
+
+ data = Path(result.output_video_path).read_bytes()
+ assert data.find(b"moov") < data.find(b"mdat")
+
+
+def _make_video(tmp_path: Path, *, fps: float = 10.0, frames: int = 4) -> Path:
+ cv2 = __import__("cv2")
+ video_path = tmp_path / "input.mp4"
+ writer = cv2.VideoWriter(str(video_path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (32, 32))
+ for index in range(frames):
+ frame = __import__("numpy").zeros((32, 32, 3), dtype="uint8")
+ frame[:] = (index * 20) % 256
+ writer.write(frame)
+ writer.release()
+ return video_path
diff --git a/tiny_trigger/__init__.py b/tiny_trigger/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc9377e3b19c4a8c93d1050161ab96971d7c996f
--- /dev/null
+++ b/tiny_trigger/__init__.py
@@ -0,0 +1,16 @@
+"""Open-vocabulary video automations for Tiny Trigger."""
+
+from .automation import RuleEngine, evaluate_video_detections, load_automation_text
+from .detector import UltralyticsYOLOEDetector, parse_class_prompt
+from .llm import compile_automation_with_llamacpp
+from .video import process_video
+
+__all__ = [
+ "RuleEngine",
+ "UltralyticsYOLOEDetector",
+ "compile_automation_with_llamacpp",
+ "evaluate_video_detections",
+ "load_automation_text",
+ "parse_class_prompt",
+ "process_video",
+]
diff --git a/tiny_trigger/actions.py b/tiny_trigger/actions.py
new file mode 100644
index 0000000000000000000000000000000000000000..987b8bca201a124fed0f7c0865a79819c1cd29fc
--- /dev/null
+++ b/tiny_trigger/actions.py
@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from .models import ActionEvent
+
+
+def dispatch_events(
+ events: list[ActionEvent],
+ *,
+ enable_webhooks: bool = False,
+ webhook_url: str | None = None,
+ timeout: float = 5.0,
+) -> list[ActionEvent]:
+ """Mark simulated actions and optionally POST webhook actions."""
+ dispatched: list[ActionEvent] = []
+
+ for event in events:
+ if event.type != "webhook":
+ dispatched.append(event.model_copy(update={"status": "simulated"}))
+ continue
+
+ target_url = event.url or webhook_url
+ if not enable_webhooks:
+ dispatched.append(event.model_copy(update={"status": "simulated_webhook"}))
+ continue
+ if not target_url:
+ dispatched.append(event.model_copy(update={"status": "webhook_missing_url"}))
+ continue
+
+ try:
+ import requests
+
+ response = requests.post(target_url, json=event.payload, timeout=timeout)
+ dispatched.append(
+ event.model_copy(
+ update={
+ "status": "webhook_posted",
+ "response_status": response.status_code,
+ }
+ )
+ )
+ except Exception as exc: # pragma: no cover - network failure path
+ dispatched.append(
+ event.model_copy(
+ update={
+ "status": "webhook_failed",
+ "error": str(exc),
+ }
+ )
+ )
+
+ return dispatched
diff --git a/tiny_trigger/automation.py b/tiny_trigger/automation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c65ed370920cbf289819f3283145ab8ff6fb2ad6
--- /dev/null
+++ b/tiny_trigger/automation.py
@@ -0,0 +1,274 @@
+from __future__ import annotations
+
+import json
+import math
+from collections import defaultdict
+from typing import Any, Literal
+
+from pydantic import AliasChoices, BaseModel, ConfigDict, Field, ValidationError, model_validator
+
+from .models import ActionEvent, Detection
+
+
+def _label(value: str) -> str:
+ return value.strip().lower()
+
+
+class PresentCondition(BaseModel):
+ label: str
+ min_count: int = Field(default=1, ge=1)
+
+
+class CountCondition(BaseModel):
+ label: str
+ minimum: int = Field(default=1, ge=1, validation_alias=AliasChoices("min", "minimum", "min_count"))
+
+
+class NearCondition(BaseModel):
+ a: str
+ b: str
+ max_distance: float = Field(default=0.35, ge=0.0)
+
+
+class CooldownCondition(BaseModel):
+ key: str | None = None
+ seconds: float | None = Field(default=None, ge=0.0)
+ minutes: float | None = Field(default=None, ge=0.0)
+
+ @property
+ def duration_seconds(self) -> float:
+ if self.seconds is not None:
+ return self.seconds
+ if self.minutes is not None:
+ return self.minutes * 60.0
+ return 0.0
+
+
+class ConditionBlock(BaseModel):
+ present: PresentCondition | None = None
+ count: CountCondition | None = None
+ near: NearCondition | None = None
+ cooldown: CooldownCondition | None = None
+
+ @model_validator(mode="after")
+ def exactly_one_condition(self) -> "ConditionBlock":
+ selected = [self.present, self.count, self.near, self.cooldown]
+ if sum(item is not None for item in selected) != 1:
+ raise ValueError("Each condition block must contain exactly one condition.")
+ return self
+
+
+class WhenClause(BaseModel):
+ model_config = ConfigDict(populate_by_name=True)
+
+ all_conditions: list[ConditionBlock] = Field(default_factory=list, alias="all")
+ any_conditions: list[ConditionBlock] = Field(default_factory=list, alias="any")
+
+ @model_validator(mode="after")
+ def has_conditions(self) -> "WhenClause":
+ if not self.all_conditions and not self.any_conditions:
+ raise ValueError("A rule needs at least one condition.")
+ return self
+
+
+class ActionSpec(BaseModel):
+ type: Literal["simulate", "webhook"] = "simulate"
+ name: str
+ url: str | None = None
+ payload: dict[str, Any] = Field(default_factory=dict)
+
+
+class GateClause(BaseModel):
+ enabled: bool = True
+ cooldown: CooldownCondition | None = None
+
+
+class AutomationRule(BaseModel):
+ name: str
+ when: WhenClause
+ gate: GateClause = Field(default_factory=GateClause)
+ then: list[ActionSpec] = Field(min_length=1)
+
+
+class AutomationDocument(BaseModel):
+ rules: list[AutomationRule] = Field(min_length=1)
+
+
+def load_automation_text(text: str) -> AutomationDocument:
+ """Load a JSON/YAML automation document and validate it."""
+ raw = text.strip()
+ if not raw:
+ raise ValueError("Rules are empty.")
+
+ try:
+ data = json.loads(raw)
+ except json.JSONDecodeError:
+ try:
+ import yaml
+ except ImportError as exc: # pragma: no cover - dependency guard
+ raise RuntimeError("Install PyYAML to load YAML automation rules.") from exc
+ data = yaml.safe_load(raw)
+
+ if isinstance(data, list):
+ data = {"rules": data}
+ if isinstance(data, dict) and "rules" not in data and "name" in data:
+ data = {"rules": [data]}
+
+ try:
+ return AutomationDocument.model_validate(data)
+ except ValidationError:
+ raise
+ except Exception as exc:
+ raise ValueError(f"Invalid automation document: {exc}") from exc
+
+
+def automation_schema() -> dict[str, Any]:
+ return AutomationDocument.model_json_schema()
+
+
+class RuleEngine:
+ def __init__(self, rules: list[AutomationRule], last_fired: dict[str, float] | None = None) -> None:
+ self.rules = rules
+ self.last_fired: dict[str, float] = dict(last_fired or {})
+
+ def evaluate_frame(
+ self,
+ detections: list[Detection],
+ *,
+ frame_index: int,
+ timestamp_sec: float,
+ ) -> list[ActionEvent]:
+ events: list[ActionEvent] = []
+ for rule in self.rules:
+ if not self._gate_allows(rule, timestamp_sec):
+ continue
+ if not self._rule_matches(rule, detections, timestamp_sec):
+ continue
+
+ self._mark_cooldowns(rule, timestamp_sec)
+ for action in rule.then:
+ payload = {
+ "rule": rule.name,
+ "action": action.name,
+ "frame_index": frame_index,
+ "timestamp_sec": timestamp_sec,
+ "detections": [item.model_dump(mode="json") for item in detections],
+ }
+ payload.update(action.payload)
+ events.append(
+ ActionEvent(
+ rule=rule.name,
+ action=action.name,
+ type=action.type,
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ url=action.url,
+ payload=payload,
+ )
+ )
+ return events
+
+ def _rule_matches(self, rule: AutomationRule, detections: list[Detection], timestamp_sec: float) -> bool:
+ all_ok = all(
+ self._condition_matches(condition, detections, rule.name, timestamp_sec)
+ for condition in rule.when.all_conditions
+ )
+ any_ok = True
+ if rule.when.any_conditions:
+ any_ok = any(
+ self._condition_matches(condition, detections, rule.name, timestamp_sec)
+ for condition in rule.when.any_conditions
+ )
+ return all_ok and any_ok
+
+ def _gate_allows(self, rule: AutomationRule, timestamp_sec: float) -> bool:
+ if not rule.gate.enabled:
+ return False
+ if not rule.gate.cooldown:
+ return True
+ return self._cooldown_allows(rule.gate.cooldown, rule.name, timestamp_sec)
+
+ def _condition_matches(
+ self,
+ condition: ConditionBlock,
+ detections: list[Detection],
+ rule_name: str,
+ timestamp_sec: float,
+ ) -> bool:
+ by_label = _group_by_label(detections)
+
+ if condition.present:
+ return len(by_label[_label(condition.present.label)]) >= condition.present.min_count
+
+ if condition.count:
+ return len(by_label[_label(condition.count.label)]) >= condition.count.minimum
+
+ if condition.near:
+ left = by_label[_label(condition.near.a)]
+ right = by_label[_label(condition.near.b)]
+ if not left or not right:
+ return False
+ return _min_center_distance(left, right) <= condition.near.max_distance
+
+ if condition.cooldown:
+ return self._cooldown_allows(condition.cooldown, rule_name, timestamp_sec)
+
+ return False
+
+ def _mark_cooldowns(self, rule: AutomationRule, timestamp_sec: float) -> None:
+ if rule.gate.cooldown:
+ self.last_fired[rule.gate.cooldown.key or rule.name] = timestamp_sec
+ for condition in [*rule.when.all_conditions, *rule.when.any_conditions]:
+ if condition.cooldown:
+ self.last_fired[condition.cooldown.key or rule.name] = timestamp_sec
+
+ def _cooldown_allows(self, cooldown: CooldownCondition, rule_name: str, timestamp_sec: float) -> bool:
+ key = cooldown.key or rule_name
+ last = self.last_fired.get(key)
+ return last is None or (timestamp_sec - last) >= cooldown.duration_seconds
+
+
+def evaluate_video_detections(
+ rules: list[AutomationRule],
+ detections: list[Detection],
+ *,
+ last_fired: dict[str, float] | None = None,
+) -> tuple[list[ActionEvent], dict[str, float]]:
+ engine = RuleEngine(rules, last_fired=last_fired)
+ events: list[ActionEvent] = []
+ grouped: dict[tuple[int, float], list[Detection]] = defaultdict(list)
+
+ for detection in detections:
+ grouped[(detection.frame_index, detection.timestamp_sec)].append(detection)
+
+ for (frame_index, timestamp_sec), frame_detections in sorted(grouped.items()):
+ events.extend(
+ engine.evaluate_frame(
+ frame_detections,
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ )
+ )
+ return events, dict(engine.last_fired)
+
+
+def _group_by_label(detections: list[Detection]) -> dict[str, list[Detection]]:
+ grouped: dict[str, list[Detection]] = defaultdict(list)
+ for detection in detections:
+ grouped[_label(detection.label)].append(detection)
+ return grouped
+
+
+def _center(detection: Detection) -> tuple[float, float]:
+ x1, y1, x2, y2 = detection.bbox_xyxy_norm
+ return ((x1 + x2) / 2.0, (y1 + y2) / 2.0)
+
+
+def _min_center_distance(left: list[Detection], right: list[Detection]) -> float:
+ best = float("inf")
+ for left_detection in left:
+ lx, ly = _center(left_detection)
+ for right_detection in right:
+ rx, ry = _center(right_detection)
+ best = min(best, math.dist((lx, ly), (rx, ry)))
+ return best
diff --git a/tiny_trigger/detector.py b/tiny_trigger/detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b0c1d3c5f190aa608ccc92f6619876eedaf19be
--- /dev/null
+++ b/tiny_trigger/detector.py
@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import re
+from typing import Any, Protocol
+
+from .models import Detection
+
+
+def parse_class_prompt(class_prompt: str | list[str] | tuple[str, ...]) -> list[str]:
+ if isinstance(class_prompt, (list, tuple)):
+ parts = [str(item) for item in class_prompt]
+ else:
+ parts = re.split(r"[,;\n]+", class_prompt)
+
+ seen: set[str] = set()
+ classes: list[str] = []
+ for part in parts:
+ label = " ".join(part.strip().split())
+ key = label.lower()
+ if label and key not in seen:
+ seen.add(key)
+ classes.append(label)
+ return classes
+
+
+class Detector(Protocol):
+ class_names: list[str]
+
+ def detect(
+ self,
+ frame: Any,
+ *,
+ frame_index: int,
+ timestamp_sec: float,
+ confidence: float,
+ image_size: int | None = None,
+ max_detections: int | None = None,
+ ) -> list[Detection]:
+ ...
+
+
+class UltralyticsYOLOEDetector:
+ def __init__(
+ self,
+ *,
+ class_names: list[str],
+ model_name: str = "yoloe-26s-seg.pt",
+ device: str | None = None,
+ ) -> None:
+ if not class_names:
+ raise ValueError("YOLOE needs at least one open-vocabulary class.")
+
+ try:
+ from ultralytics import YOLOE
+ except ImportError as exc: # pragma: no cover - optional heavy dependency
+ raise RuntimeError("Install ultralytics to use the YOLOE detector.") from exc
+
+ self.class_names = class_names
+ self.model_name = model_name
+ self.device = device
+ self.model = YOLOE(model_name)
+ self.model.set_classes(class_names)
+
+ def detect(
+ self,
+ frame: Any,
+ *,
+ frame_index: int,
+ timestamp_sec: float,
+ confidence: float,
+ image_size: int | None = None,
+ max_detections: int | None = None,
+ ) -> list[Detection]:
+ kwargs: dict[str, Any] = {"conf": confidence, "verbose": False}
+ if self.device:
+ kwargs["device"] = self.device
+ if image_size:
+ kwargs["imgsz"] = image_size
+ if max_detections:
+ kwargs["max_det"] = max_detections
+
+ results = self.model.predict(frame, **kwargs)
+ if not results:
+ return []
+ return detections_from_ultralytics_result(
+ results[0],
+ frame_shape=frame.shape,
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ fallback_names=self.class_names,
+ )
+
+
+def detections_from_ultralytics_result(
+ result: Any,
+ *,
+ frame_shape: tuple[int, int, int],
+ frame_index: int,
+ timestamp_sec: float,
+ fallback_names: list[str],
+) -> list[Detection]:
+ boxes = getattr(result, "boxes", None)
+ if boxes is None or boxes.xyxy is None:
+ return []
+
+ height, width = frame_shape[:2]
+ xyxy_values = boxes.xyxy.cpu().tolist()
+ confidences = boxes.conf.cpu().tolist()
+ class_ids = boxes.cls.cpu().tolist()
+ names = getattr(result, "names", {}) or {}
+
+ detections: list[Detection] = []
+ for bbox, score, class_id in zip(xyxy_values, confidences, class_ids):
+ label = _label_from_names(names, int(class_id), fallback_names)
+ detections.append(
+ Detection(
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ label=label,
+ confidence=float(score),
+ bbox_xyxy=tuple(float(value) for value in bbox),
+ bbox_xyxy_norm=_normalize_box(bbox, width, height),
+ )
+ )
+ return detections
+
+
+def _label_from_names(names: Any, class_id: int, fallback_names: list[str]) -> str:
+ if isinstance(names, dict) and class_id in names:
+ return str(names[class_id])
+ if isinstance(names, list) and 0 <= class_id < len(names):
+ return str(names[class_id])
+ if 0 <= class_id < len(fallback_names):
+ return fallback_names[class_id]
+ return f"class_{class_id}"
+
+
+def _normalize_box(bbox: list[float], width: int, height: int) -> tuple[float, float, float, float]:
+ x1, y1, x2, y2 = bbox
+ if width <= 0 or height <= 0:
+ return (0.0, 0.0, 0.0, 0.0)
+ return (
+ _clamp01(float(x1) / width),
+ _clamp01(float(y1) / height),
+ _clamp01(float(x2) / width),
+ _clamp01(float(y2) / height),
+ )
+
+
+def _clamp01(value: float) -> float:
+ return max(0.0, min(1.0, value))
diff --git a/tiny_trigger/llm.py b/tiny_trigger/llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..40d8ce5f5e51206374a90279ddf3562b090125b1
--- /dev/null
+++ b/tiny_trigger/llm.py
@@ -0,0 +1,208 @@
+from __future__ import annotations
+
+import json
+import re
+from typing import Any
+
+from pydantic import BaseModel, ValidationError
+
+from .automation import AutomationDocument, automation_schema
+
+
+SYSTEM_PROMPT = """You compile home automation requests into Tiny Trigger rules.
+Return JSON only. Never return code, markdown, explanations, or tool calls.
+The root object MUST include a non-empty "rules" array.
+Use video conditions in when: present, count, near.
+Use state gates in gate: enabled, cooldown.
+Use only these action types: simulate, webhook.
+When the request says one object is near, next to, beside, at, by, close to, or in front of another object, you MUST emit a near condition.
+Do not replace a near relation with two present conditions.
+If the user mentions elapsed time since an action or limiting repeat fires, encode it as gate.cooldown.
+"""
+
+
+class LLMCompileResult(BaseModel):
+ raw_text: str
+ document: AutomationDocument
+
+
+def compile_automation_with_llamacpp(
+ *,
+ instruction: str,
+ class_names: list[str],
+ base_url: str = "http://127.0.0.1:8080/v1",
+ model: str = "ggml-org/Qwen3-1.7B-GGUF:Q4_K_M",
+ timeout: float = 60.0,
+) -> LLMCompileResult:
+ """Compile natural language into validated rules through llama.cpp server."""
+ try:
+ import requests
+ except ImportError as exc: # pragma: no cover - dependency guard
+ raise RuntimeError("Install requests to use the llama.cpp compiler.") from exc
+
+ endpoint = base_url.rstrip("/") + "/chat/completions"
+ user_prompt = _build_user_prompt(instruction=instruction, class_names=class_names)
+ raw_text = _post_chat_completion(
+ endpoint=endpoint,
+ model=model,
+ user_prompt=user_prompt,
+ timeout=timeout,
+ requests_module=requests,
+ )
+ try:
+ return _validate_compile_result(raw_text)
+ except (json.JSONDecodeError, ValidationError, ValueError) as first_error:
+ repair_prompt = _build_repair_prompt(
+ original_prompt=user_prompt,
+ bad_response=raw_text,
+ error=str(first_error),
+ )
+ repaired_text = _post_chat_completion(
+ endpoint=endpoint,
+ model=model,
+ user_prompt=repair_prompt,
+ timeout=timeout,
+ requests_module=requests,
+ )
+ try:
+ return _validate_compile_result(repaired_text)
+ except (json.JSONDecodeError, ValidationError, ValueError) as second_error:
+ raise ValueError(
+ "llama.cpp returned invalid automation JSON after a repair attempt. "
+ f"Last validation error: {second_error}. Last response: {repaired_text}"
+ ) from second_error
+
+
+def _post_chat_completion(
+ *,
+ endpoint: str,
+ model: str,
+ user_prompt: str,
+ timeout: float,
+ requests_module: Any,
+) -> str:
+ payload = _chat_payload(model=model, user_prompt=user_prompt, response_format="json_object")
+ response = requests_module.post(endpoint, json=payload, timeout=timeout)
+ response.raise_for_status()
+ body = response.json()
+ return body["choices"][0]["message"]["content"]
+
+
+def _validate_compile_result(raw_text: str) -> LLMCompileResult:
+ data = json.loads(extract_json_object(raw_text))
+ return LLMCompileResult(raw_text=raw_text, document=AutomationDocument.model_validate(data))
+
+
+def extract_json_object(text: str) -> str:
+ stripped = text.strip()
+ if stripped.startswith("```"):
+ stripped = re.sub(r"^```(?:json)?", "", stripped, flags=re.IGNORECASE).strip()
+ stripped = re.sub(r"```$", "", stripped).strip()
+ if stripped.startswith("{") and stripped.endswith("}"):
+ return stripped
+
+ match = re.search(r"\{.*\}", stripped, flags=re.DOTALL)
+ if not match:
+ raise ValueError("llama.cpp response did not contain a JSON object.")
+ return match.group(0)
+
+
+def _build_user_prompt(*, instruction: str, class_names: list[str]) -> str:
+ class_hint = ", ".join(class_names) if class_names else "Use labels from the request."
+ return f"""Available detection labels: {class_hint}
+
+Automation request:
+{instruction}
+
+Return a JSON object matching this high-level shape:
+{{
+ "rules": [
+ {{
+ "name": "short-kebab-case-name",
+ "when": {{"all": [{{"present": {{"label": "cat", "min_count": 1}}}}]}},
+ "gate": {{"enabled": true}},
+ "then": [{{"type": "simulate", "name": "action name"}}]
+ }}
+ ]
+}}
+
+Examples:
+
+User: If person near steering wheel then you have to turn on pc.
+JSON:
+{{
+ "rules": [
+ {{
+ "name": "person-near-steering-wheel",
+ "when": {{
+ "all": [
+ {{"present": {{"label": "person", "min_count": 1}}}},
+ {{"near": {{"a": "person", "b": "steering wheel", "max_distance": 0.45}}}}
+ ]
+ }},
+ "gate": {{"enabled": true}},
+ "then": [{{"type": "simulate", "name": "turn on pc"}}]
+ }}
+ ]
+}}
+
+User: If package is at door and 15 minutes since last notification, notify me.
+JSON:
+{{
+ "rules": [
+ {{
+ "name": "package-at-door",
+ "when": {{
+ "all": [
+ {{"present": {{"label": "package", "min_count": 1}}}},
+ {{"near": {{"a": "package", "b": "door", "max_distance": 0.45}}}}
+ ]
+ }},
+ "gate": {{"enabled": true, "cooldown": {{"key": "package-at-door", "minutes": 15}}}},
+ "then": [{{"type": "simulate", "name": "notify me"}}]
+ }}
+ ]
+}}
+
+Full validation schema:
+{json.dumps(automation_schema(), indent=2)}
+"""
+
+
+def _build_repair_prompt(*, original_prompt: str, bad_response: str, error: str) -> str:
+ return f"""{original_prompt}
+
+Your previous response failed validation.
+
+Validation error:
+{error}
+
+Previous response:
+{bad_response}
+
+Return corrected JSON only. The root object MUST include a non-empty "rules" array.
+"""
+
+
+def _chat_payload(*, model: str, user_prompt: str, response_format: str) -> dict[str, Any]:
+ payload: dict[str, Any] = {
+ "model": model,
+ "messages": [
+ {"role": "system", "content": SYSTEM_PROMPT},
+ {"role": "user", "content": user_prompt},
+ ],
+ "temperature": 0,
+ "stream": False,
+ }
+ if response_format == "json_schema":
+ payload["response_format"] = {
+ "type": "json_schema",
+ "json_schema": {
+ "name": "tiny_trigger_automation",
+ "strict": True,
+ "schema": automation_schema(),
+ },
+ }
+ else:
+ payload["response_format"] = {"type": "json_object"}
+ return payload
diff --git a/tiny_trigger/models.py b/tiny_trigger/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55af7beed650c740c219a96fd97269397622b0e
--- /dev/null
+++ b/tiny_trigger/models.py
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class Detection(BaseModel):
+ frame_index: int = Field(ge=0)
+ timestamp_sec: float = Field(ge=0.0)
+ label: str
+ confidence: float = Field(ge=0.0, le=1.0)
+ bbox_xyxy: tuple[float, float, float, float]
+ bbox_xyxy_norm: tuple[float, float, float, float]
+
+
+class VideoProcessResult(BaseModel):
+ output_video_path: str
+ classes: list[str]
+ detections: list[Detection]
+ processed_frames: int
+ source_fps: float
+ output_fps: float
+
+
+class ActionEvent(BaseModel):
+ rule: str
+ action: str
+ type: Literal["simulate", "webhook"]
+ frame_index: int
+ timestamp_sec: float
+ status: str = "pending"
+ url: str | None = None
+ payload: dict[str, Any] = Field(default_factory=dict)
+ response_status: int | None = None
+ error: str | None = None
diff --git a/tiny_trigger/store.py b/tiny_trigger/store.py
new file mode 100644
index 0000000000000000000000000000000000000000..93e25b1c2d75efbab43646401ee8346e3727a5c0
--- /dev/null
+++ b/tiny_trigger/store.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from .automation import AutomationDocument
+from .models import ActionEvent
+
+
+LOCAL_DIR = Path(".local")
+CONFIG_PATH = LOCAL_DIR / "config.yaml"
+AUTOMATIONS_PATH = LOCAL_DIR / "automations.json"
+STATE_PATH = LOCAL_DIR / "state.json"
+EVENTS_PATH = LOCAL_DIR / "events.jsonl"
+
+
+class LocalConfig(BaseModel):
+ camera_url: str | None = None
+ webhook_url: str | None = None
+ default_classes: str | None = None
+ default_detector_model: str | None = None
+ default_device: str | None = None
+ default_image_size: int | None = None
+ default_max_detections: int | None = None
+ llamacpp_base_url: str | None = None
+ llamacpp_model: str | None = None
+
+
+class RuntimeState(BaseModel):
+ last_fired: dict[str, float] = Field(default_factory=dict)
+ snoozed_until: dict[str, float] = Field(default_factory=dict)
+
+
+def load_local_config(path: Path = CONFIG_PATH) -> LocalConfig:
+ if not path.exists():
+ return LocalConfig()
+ try:
+ import yaml
+ except ImportError as exc: # pragma: no cover - dependency guard
+ raise RuntimeError("Install PyYAML to load local config.") from exc
+ data = yaml.safe_load(path.read_text(encoding="utf-8")) or {}
+ return LocalConfig.model_validate(data)
+
+
+def load_saved_automations(path: Path = AUTOMATIONS_PATH) -> AutomationDocument | None:
+ if not path.exists():
+ return None
+ return AutomationDocument.model_validate(json.loads(path.read_text(encoding="utf-8")))
+
+
+def save_automations(document: AutomationDocument, path: Path = AUTOMATIONS_PATH) -> None:
+ _ensure_parent(path)
+ path.write_text(document.model_dump_json(by_alias=True, indent=2), encoding="utf-8")
+
+
+def load_runtime_state(path: Path = STATE_PATH) -> RuntimeState:
+ if not path.exists():
+ return RuntimeState()
+ return RuntimeState.model_validate(json.loads(path.read_text(encoding="utf-8")))
+
+
+def save_runtime_state(state: RuntimeState, path: Path = STATE_PATH) -> None:
+ _ensure_parent(path)
+ path.write_text(state.model_dump_json(indent=2), encoding="utf-8")
+
+
+def append_events(events: list[ActionEvent], path: Path = EVENTS_PATH) -> None:
+ if not events:
+ return
+ _ensure_parent(path)
+ with path.open("a", encoding="utf-8") as file:
+ for event in events:
+ payload: dict[str, Any] = event.model_dump(mode="json")
+ payload["recorded_at"] = datetime.now(timezone.utc).isoformat()
+ file.write(json.dumps(payload, ensure_ascii=False) + "\n")
+
+
+def _ensure_parent(path: Path) -> None:
+ path.parent.mkdir(parents=True, exist_ok=True)
diff --git a/tiny_trigger/video.py b/tiny_trigger/video.py
new file mode 100644
index 0000000000000000000000000000000000000000..df83e842c85da1220740b856b7cc431a1bc7874a
--- /dev/null
+++ b/tiny_trigger/video.py
@@ -0,0 +1,310 @@
+from __future__ import annotations
+
+import hashlib
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Callable
+from uuid import uuid4
+
+from .detector import Detector, UltralyticsYOLOEDetector, parse_class_prompt
+from .models import ActionEvent, Detection, VideoProcessResult
+
+
+ProgressCallback = Callable[[int, int | None], None]
+MIN_PREVIEW_FPS = 8.0
+MAX_PREVIEW_FPS = 30.0
+MP4_CODEC_CANDIDATES = ("avc1", "H264", "h264", "mp4v")
+
+
+def process_video(
+ *,
+ video_path: str,
+ class_prompt: str | list[str],
+ confidence: float = 0.25,
+ frame_stride: int = 5,
+ max_frames: int = 120,
+ model_name: str = "yoloe-26s-seg.pt",
+ image_size: int | None = None,
+ device: str | None = None,
+ max_detections: int | None = None,
+ detector: Detector | None = None,
+ output_dir: str | None = None,
+ progress: ProgressCallback | None = None,
+) -> VideoProcessResult:
+ """Sample a video, run open-vocabulary detection, and write an annotated clip."""
+ try:
+ import cv2
+ except ImportError as exc: # pragma: no cover - optional heavy dependency
+ raise RuntimeError("Install opencv-python-headless to process videos.") from exc
+
+ classes = parse_class_prompt(class_prompt)
+ if not classes:
+ raise ValueError("At least one class prompt is required.")
+ if frame_stride < 1:
+ raise ValueError("frame_stride must be at least 1.")
+ if max_frames < 1:
+ raise ValueError("max_frames must be at least 1.")
+ if image_size is not None and image_size < 32:
+ raise ValueError("image_size must be at least 32.")
+ if max_detections is not None and max_detections < 1:
+ raise ValueError("max_detections must be at least 1.")
+
+ detector = detector or UltralyticsYOLOEDetector(class_names=classes, model_name=model_name, device=device or None)
+ capture = cv2.VideoCapture(video_path)
+ if not capture.isOpened():
+ raise ValueError(f"Could not open video: {video_path}")
+
+ source_fps = float(capture.get(cv2.CAP_PROP_FPS) or 30.0)
+ width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 0)
+ height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT) or 0)
+ output_fps, repeat_count = _preview_timing(source_fps, frame_stride)
+ output_size = _browser_frame_size(width, height)
+ output_path = _output_path(video_path, output_dir)
+ writer = _create_browser_mp4_writer(output_path, output_fps, output_size)
+ if writer is None:
+ capture.release()
+ raise ValueError(f"Could not create annotated video: {output_path}")
+
+ detections: list[Detection] = []
+ processed_frames = 0
+ frame_index = -1
+ try:
+ while True:
+ ok, frame = capture.read()
+ if not ok:
+ break
+ frame_index += 1
+ if frame_index % frame_stride != 0:
+ continue
+ if processed_frames >= max_frames:
+ break
+
+ timestamp_sec = frame_index / source_fps
+ frame_detections = detector.detect(
+ frame,
+ frame_index=frame_index,
+ timestamp_sec=timestamp_sec,
+ confidence=confidence,
+ image_size=image_size,
+ max_detections=max_detections,
+ )
+ detections.extend(frame_detections)
+ _draw_detections(frame, frame_detections)
+ _write_preview_frame(writer, _fit_frame_to_output(frame, output_size), repeat_count)
+ processed_frames += 1
+ if progress:
+ progress(processed_frames, max_frames)
+ finally:
+ writer.release()
+ capture.release()
+ _finalize_browser_mp4(output_path)
+
+ return VideoProcessResult(
+ output_video_path=str(output_path),
+ classes=classes,
+ detections=detections,
+ processed_frames=processed_frames,
+ source_fps=source_fps,
+ output_fps=output_fps,
+ )
+
+
+def render_automation_video(
+ *,
+ source_video_path: str,
+ detections: list[Detection],
+ events: list[ActionEvent],
+ frame_stride: int,
+ max_frames: int,
+ output_dir: str | None = None,
+) -> str:
+ """Render detections plus fired automation events without rerunning inference."""
+ try:
+ import cv2
+ except ImportError as exc: # pragma: no cover - optional heavy dependency
+ raise RuntimeError("Install opencv-python-headless to render videos.") from exc
+
+ if frame_stride < 1:
+ raise ValueError("frame_stride must be at least 1.")
+ if max_frames < 1:
+ raise ValueError("max_frames must be at least 1.")
+
+ capture = cv2.VideoCapture(source_video_path)
+ if not capture.isOpened():
+ raise ValueError(f"Could not open video: {source_video_path}")
+
+ source_fps = float(capture.get(cv2.CAP_PROP_FPS) or 30.0)
+ width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 0)
+ height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT) or 0)
+ output_fps, repeat_count = _preview_timing(source_fps, frame_stride)
+ output_size = _browser_frame_size(width, height)
+ output_path = _output_path(source_video_path, output_dir, suffix="automated")
+ writer = _create_browser_mp4_writer(output_path, output_fps, output_size)
+ if writer is None:
+ capture.release()
+ raise ValueError(f"Could not create automation video: {output_path}")
+
+ detections_by_frame = _group_detections_by_frame(detections)
+ events_by_frame = _group_events_by_frame(events)
+ processed_frames = 0
+ frame_index = -1
+ try:
+ while True:
+ ok, frame = capture.read()
+ if not ok:
+ break
+ frame_index += 1
+ if frame_index % frame_stride != 0:
+ continue
+ if processed_frames >= max_frames:
+ break
+
+ _draw_detections(frame, detections_by_frame.get(frame_index, []))
+ frame_events = events_by_frame.get(frame_index, [])
+ if frame_events:
+ _draw_action_events(frame, frame_events)
+ _write_preview_frame(writer, _fit_frame_to_output(frame, output_size), repeat_count)
+ processed_frames += 1
+ finally:
+ writer.release()
+ capture.release()
+ _finalize_browser_mp4(output_path)
+
+ return str(output_path)
+
+
+def _output_path(video_path: str, output_dir: str | None, *, suffix: str = "annotated") -> Path:
+ base_dir = Path(output_dir) if output_dir else Path(tempfile.gettempdir()) / "tiny-trigger"
+ base_dir.mkdir(parents=True, exist_ok=True)
+ return base_dir / f"{Path(video_path).stem}-{uuid4().hex[:8]}-{suffix}.mp4"
+
+
+def _preview_timing(source_fps: float, frame_stride: int) -> tuple[float, int]:
+ sampled_fps = source_fps / frame_stride if frame_stride else source_fps
+ output_fps = min(MAX_PREVIEW_FPS, max(MIN_PREVIEW_FPS, sampled_fps or MIN_PREVIEW_FPS))
+ repeat_count = max(1, round(output_fps / sampled_fps)) if sampled_fps > 0 else 1
+ return output_fps, repeat_count
+
+
+def _preview_fps(source_fps: float, frame_stride: int) -> float:
+ return _preview_timing(source_fps, frame_stride)[0]
+
+
+def _browser_frame_size(width: int, height: int) -> tuple[int, int]:
+ output_width = width - (width % 2)
+ output_height = height - (height % 2)
+ if output_width < 2 or output_height < 2:
+ raise ValueError("Video dimensions are too small to render.")
+ return (output_width, output_height)
+
+
+def _create_browser_mp4_writer(output_path: Path, fps: float, frame_size: tuple[int, int]):
+ import cv2
+
+ for codec in MP4_CODEC_CANDIDATES:
+ writer = cv2.VideoWriter(str(output_path), cv2.VideoWriter_fourcc(*codec), fps, frame_size)
+ if writer.isOpened():
+ return writer
+ writer.release()
+ return None
+
+
+def _finalize_browser_mp4(output_path: Path) -> None:
+ ffmpeg_executable = _ffmpeg_executable()
+ if not ffmpeg_executable or not output_path.exists():
+ return
+
+ faststart_path = output_path.with_name(f"{output_path.stem}-faststart-{uuid4().hex[:8]}{output_path.suffix}")
+ try:
+ subprocess.run(
+ [
+ ffmpeg_executable,
+ "-y",
+ "-loglevel",
+ "error",
+ "-i",
+ str(output_path),
+ "-c",
+ "copy",
+ "-movflags",
+ "+faststart",
+ str(faststart_path),
+ ],
+ check=True,
+ capture_output=True,
+ )
+ if faststart_path.exists() and faststart_path.stat().st_size > 0:
+ faststart_path.replace(output_path)
+ except (OSError, subprocess.CalledProcessError):
+ if faststart_path.exists():
+ faststart_path.unlink()
+
+
+def _ffmpeg_executable() -> str | None:
+ if ffmpeg_path := shutil.which("ffmpeg"):
+ return ffmpeg_path
+ try:
+ import imageio_ffmpeg
+ except ImportError:
+ return None
+ return imageio_ffmpeg.get_ffmpeg_exe()
+
+
+def _fit_frame_to_output(frame, output_size: tuple[int, int]):
+ output_width, output_height = output_size
+ height, width = frame.shape[:2]
+ if width == output_width and height == output_height:
+ return frame
+ return frame[:output_height, :output_width]
+
+
+def _write_preview_frame(writer, frame, repeat_count: int) -> None:
+ for _ in range(repeat_count):
+ writer.write(frame)
+
+
+def _draw_detections(frame, detections: list[Detection]) -> None:
+ import cv2
+
+ for detection in detections:
+ x1, y1, x2, y2 = [int(value) for value in detection.bbox_xyxy]
+ color = _color_for_label(detection.label)
+ label = f"{detection.label} {detection.confidence:.2f}"
+ cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+ text_y = max(18, y1 - 8)
+ cv2.putText(frame, label, (x1, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 2, cv2.LINE_AA)
+
+
+def _draw_action_events(frame, events: list[ActionEvent]) -> None:
+ import cv2
+
+ height, width = frame.shape[:2]
+ banner_height = min(110, max(70, height // 9))
+ overlay = frame.copy()
+ cv2.rectangle(overlay, (0, 0), (width, banner_height), (0, 96, 255), -1)
+ cv2.addWeighted(overlay, 0.78, frame, 0.22, 0, frame)
+
+ cv2.putText(frame, "FIRED", (24, 44), cv2.FONT_HERSHEY_SIMPLEX, 1.15, (255, 255, 255), 3, cv2.LINE_AA)
+ details = " | ".join(f"{event.rule}: {event.action}" for event in events[:3])
+ cv2.putText(frame, details, (24, banner_height - 18), cv2.FONT_HERSHEY_SIMPLEX, 0.58, (255, 255, 255), 2, cv2.LINE_AA)
+
+
+def _color_for_label(label: str) -> tuple[int, int, int]:
+ digest = hashlib.md5(label.encode("utf-8")).digest()
+ return (int(digest[0]), int(digest[1]), int(digest[2]))
+
+
+def _group_detections_by_frame(detections: list[Detection]) -> dict[int, list[Detection]]:
+ grouped: dict[int, list[Detection]] = {}
+ for detection in detections:
+ grouped.setdefault(detection.frame_index, []).append(detection)
+ return grouped
+
+
+def _group_events_by_frame(events: list[ActionEvent]) -> dict[int, list[ActionEvent]]:
+ grouped: dict[int, list[ActionEvent]] = {}
+ for event in events:
+ grouped.setdefault(event.frame_index, []).append(event)
+ return grouped