diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 75d0ad7..6ec0319 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -40,6 +40,39 @@ jobs:
- name: Test
run: poetry run pytest
+ ml-service:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ working-directory: ./ml-service
+
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.10'
+
+ - name: Install Poetry
+ uses: snok/install-poetry@v1
+ with:
+ version: latest
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+
+ - name: Install dependencies
+ run: poetry install
+
+ - name: Lint
+ run: poetry run ruff check src tests
+
+ - name: Format check
+ run: poetry run ruff format --check src tests
+
+ - name: Test
+ run: poetry run pytest
+
frontend:
runs-on: ubuntu-latest
defaults:
diff --git a/.gitignore b/.gitignore
index caf8e5c..7284b0b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -68,7 +68,6 @@ tmp
*.flv
*.wmv
*.m4v
-test-videos/
# Model files and datasets
*.pt
diff --git a/.kiro/hooks/run-frontend-tests.kiro.hook b/.kiro/hooks/run-frontend-tests.kiro.hook
new file mode 100644
index 0000000..803c7d0
--- /dev/null
+++ b/.kiro/hooks/run-frontend-tests.kiro.hook
@@ -0,0 +1,15 @@
+{
+ "enabled": true,
+ "name": "Run Frontend Tests",
+ "description": "Automatically run frontend tests using docker-compose when it's time to test. If the service is not running, use the start dev environment hook.",
+ "version": "1",
+ "when": {
+ "type": "userTriggered"
+ },
+ "then": {
+ "type": "runCommand",
+ "command": "docker-compose -f dev/docker-compose.yml exec frontend npm test"
+ },
+ "workspaceFolderName": "eioku-kiro-hackathon",
+ "shortName": "run-frontend-tests"
+}
\ No newline at end of file
diff --git a/ATTRIBUTION.md b/ATTRIBUTION.md
new file mode 100644
index 0000000..c8d5d90
--- /dev/null
+++ b/ATTRIBUTION.md
@@ -0,0 +1,108 @@
+# Third-Party Attribution
+
+This project uses the following open-source libraries, frameworks, and tools.
+
+---
+
+## ML & Computer Vision
+
+| Library | Version | License | Purpose |
+|---------|---------|---------|---------|
+| [Ultralytics YOLO](https://github.com/ultralytics/ultralytics) | ^8.0.0 | AGPL-3.0 | Object detection |
+| [faster-whisper](https://github.com/SYSTRAN/faster-whisper) | ^1.2.1 | MIT | Speech transcription |
+| [EasyOCR](https://github.com/JaidedAI/EasyOCR) | ^1.7.0 | Apache-2.0 | Optical character recognition |
+| [Places365](https://github.com/CSAILVision/places365) | - | MIT | Scene/place recognition |
+| [OpenCV](https://opencv.org/) | ^4.8.0 | Apache-2.0 | Image processing |
+| [PyTorch](https://pytorch.org/) | ^2.1 | BSD-3-Clause | Deep learning framework |
+| [TorchVision](https://pytorch.org/vision/) | ^0.16 | BSD-3-Clause | Vision models & transforms |
+| [NumPy](https://numpy.org/) | ^1.24 | BSD-3-Clause | Numerical computing |
+| [Pillow](https://python-pillow.org/) | ^10.0 | HPND | Image manipulation |
+
+---
+
+## Backend Framework
+
+| Library | Version | License | Purpose |
+|---------|---------|---------|---------|
+| [FastAPI](https://fastapi.tiangolo.com/) | ^0.104.1 | MIT | REST API framework |
+| [Uvicorn](https://www.uvicorn.org/) | ^0.24.0 | BSD-3-Clause | ASGI server |
+| [Gunicorn](https://gunicorn.org/) | ^23.0.0 | MIT | WSGI HTTP server |
+| [SQLAlchemy](https://www.sqlalchemy.org/) | ^2.0.23 | MIT | ORM & database toolkit |
+| [Alembic](https://alembic.sqlalchemy.org/) | ^1.13.1 | MIT | Database migrations |
+| [Pydantic](https://docs.pydantic.dev/) | ^2.0 | MIT | Data validation |
+| [psycopg2](https://www.psycopg.org/) | ^2.9.9 | LGPL-3.0 | PostgreSQL adapter |
+
+---
+
+## Job Queue & Caching
+
+| Library | Version | License | Purpose |
+|---------|---------|---------|---------|
+| [arq](https://arq-docs.helpmanual.io/) | ^0.26 | MIT | Async job queue |
+| [Redis-py](https://redis-py.readthedocs.io/) | ^5.0.0 | MIT | Redis client |
+
+---
+
+## Utilities
+
+| Library | Version | License | Purpose |
+|---------|---------|---------|---------|
+| [xxHash](https://github.com/Cyan4973/xxHash) | ^3.6.0 | BSD-2-Clause | Fast hashing |
+| [PyYAML](https://pyyaml.org/) | ^6.0.3 | MIT | YAML parsing |
+| [HTTPX](https://www.python-httpx.org/) | ^0.25.2 | BSD-3-Clause | HTTP client |
+| [python-json-logger](https://github.com/madzak/python-json-logger) | ^2.0.7 | BSD-2-Clause | JSON logging |
+| [GeoPy](https://geopy.readthedocs.io/) | ^2.4.0 | MIT | Geocoding |
+| [PyExifTool](https://github.com/sylikc/pyexiftool) | ^0.5.5 | GPL-3.0 | EXIF metadata |
+| [psutil](https://github.com/giampaolo/psutil) | ^5.9.0 | BSD-3-Clause | System monitoring |
+| [nvidia-ml-py](https://pypi.org/project/nvidia-ml-py/) | ^12.535 | BSD-3-Clause | GPU monitoring |
+
+---
+
+## Frontend
+
+| Library | Version | License | Purpose |
+|---------|---------|---------|---------|
+| [React](https://react.dev/) | ^18.2.0 | MIT | UI framework |
+| [React DOM](https://react.dev/) | ^18.2.0 | MIT | DOM rendering |
+| [Vite](https://vitejs.dev/) | ^5.0.8 | MIT | Build tool |
+| [TypeScript](https://www.typescriptlang.org/) | ^5.2.2 | Apache-2.0 | Type system |
+| [Vitest](https://vitest.dev/) | ^1.0.4 | MIT | Test framework |
+| [Testing Library](https://testing-library.com/) | ^13.4.0 | MIT | Testing utilities |
+| [ESLint](https://eslint.org/) | ^8.55.0 | MIT | Linter |
+
+---
+
+## Infrastructure
+
+| Tool | License | Purpose |
+|------|---------|---------|
+| [PostgreSQL](https://www.postgresql.org/) | PostgreSQL License | Database |
+| [Valkey](https://valkey.io/) (Redis fork) | BSD-3-Clause | Message queue |
+| [Nginx](https://nginx.org/) | BSD-2-Clause | Reverse proxy |
+| [Docker](https://www.docker.com/) | Apache-2.0 | Containerization |
+| [FFmpeg](https://ffmpeg.org/) | LGPL/GPL | Video processing |
+| [ExifTool](https://exiftool.org/) | GPL-1.0+ | Metadata extraction |
+
+---
+
+## Development Tools
+
+| Tool | License | Purpose |
+|------|---------|---------|
+| [Poetry](https://python-poetry.org/) | MIT | Python dependency management |
+| [Ruff](https://docs.astral.sh/ruff/) | MIT | Python linter |
+| [pytest](https://pytest.org/) | MIT | Python testing |
+
+---
+
+## Pre-trained Models
+
+| Model | Source | License | Purpose |
+|-------|--------|---------|---------|
+| YOLOv8 | Ultralytics | AGPL-3.0 | Object detection weights |
+| Whisper | OpenAI | MIT | Speech recognition weights |
+| Places365 | MIT CSAIL | MIT | Scene classification weights |
+
+---
+
+*All libraries used under their respective licenses. See individual project repositories for full license text.*
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 0f32bfb..6817168 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -26,48 +26,9 @@ Stop with:
### URLs
- **Application**: http://localhost:8080
- **Backend API**: http://localhost:8080/api/
+- **Backend API docs**: http://localhost:8080/api/docs
- **Health Check**: http://localhost:8080/health
-### Manual Setup
-
-#### Prerequisites
-
-- Python 3.10+
-- Node.js 18+
-- Git
-
-#### 1. Install Poetry
-
-```bash
-curl -sSL https://install.python-poetry.org | python3 -
-```
-
-Add Poetry to your PATH:
-```bash
-export PATH="$HOME/.local/bin:$PATH"
-```
-
-Verify installation:
-```bash
-poetry --version
-```
-
-#### 2. Backend Setup
-
-```bash
-cd backend
-poetry install
-poetry run uvicorn src.main:app --reload
-```
-
-#### 3. Frontend Setup
-
-```bash
-cd frontend
-npm install
-npm run dev
-```
-
## Development Workflow
1. Make small, incremental changes
diff --git a/DEVLOG.md b/DEVLOG.md
index 10d8a94..32e90a4 100644
--- a/DEVLOG.md
+++ b/DEVLOG.md
@@ -1,310 +1,151 @@
-# Development Log - Eioku Semantic Video Search Platform
+# Eioku Development Log
-## Session: January 17, 2026
-
-### Overview
-Created comprehensive specification for Eioku, a semantic video search platform targeting video editors with large content libraries. The platform enables semantic search across video content with advanced navigation features.
-
-### Timeline
-
-**Phase 1: Requirements Gathering (30 minutes)**
-- Initial concept discussion: semantic video search for video editors
-- Defined core features: path-based video processing, semantic search, player view with multiple navigation methods
-- Iterative refinement of requirements document
-- Added modular language support for future extensibility
-- Added GPU hardware acceleration requirement
-- Expanded player view with transcript, scene, object, and face navigation
-- Added topic discovery and suggestions feature
-
-**Phase 2: Design Document Creation (90 minutes)**
-- Researched current ML/AI technologies (Whisper, YOLO, sentence-transformers, FAISS)
-- Selected appropriate ML models (YOLOv8, PySceneDetect, all-MiniLM-L6-v2)
-- Created initial architecture with sequential processing pipeline
-- **Major pivot**: Redesigned to parallel processing architecture for better resource utilization
-- Added processing profiles (Balanced, Search First, Visual First, Low Resource)
-- Created comprehensive user flow diagrams (7 flows covering all interactions)
-- Defined zero-ops technology stack (SQLite, FAISS, multiprocessing)
-- Added correctness properties with prework analysis
-
-**Phase 3: Implementation Planning (45 minutes)**
-- Created initial task breakdown (24 tasks)
-- Refined database tasks into granular per-table schema and DAO tasks
-- Added GitHub Actions and Docker containerization to setup
-- Fixed task numbering issues
-- Final task count: 25 major tasks, 100+ sub-tasks
-
-### Key Decisions
-
-**Architecture Decisions:**
-1. **Parallel Processing**: Chose task-based parallel architecture over sequential pipeline
- - Rationale: Better resource utilization, GPU and CPU can work simultaneously
- - Impact: More complex coordination but much faster processing
- - Trade-off: Simpler progress tracking vs. performance gains
-
-2. **Zero-Ops Stack**: Prioritized simplicity and single-host deployment
- - SQLite over PostgreSQL (no database server)
- - FAISS over ChromaDB/LanceDB (file-based, no server)
- - Python multiprocessing over Celery+Redis (no message broker)
- - Rationale: Minimize operational overhead for desktop application
-
-3. **Technology Stack**:
- - Backend: Python 3.10+, FastAPI, SQLAlchemy, Alembic
- - ML: OpenAI Whisper, YOLOv8, sentence-transformers, PySceneDetect
- - Storage: SQLite, FAISS
- - Frontend: Electron + React + TypeScript
- - Rationale: Mature, well-documented technologies with strong community support
-
-**Requirements Quality:**
-- EARS-compliant format (Easy Approach to Requirements Syntax)
-- INCOSE quality rules applied
-- 10 major requirements with 60+ acceptance criteria
-- Complete glossary with 25+ terms
-
-**Design Quality:**
-- 29 correctness properties with requirements traceability
-- 7 comprehensive user flow diagrams
-- 4 processing profiles with detailed worker configurations
-- Complete component interfaces and data models
-
-### Deliverables
-1. **Requirements Document** (`.kiro/specs/semantic-video-search/requirements.md`)
- - 10 major requirements, 60+ acceptance criteria
- - EARS-compliant format with quality validation
- - Complete glossary and terminology
-
-2. **Design Document** (`.kiro/specs/semantic-video-search/design.md`)
- - Parallel processing architecture
- - Technology stack definition
- - User flow diagrams
- - Correctness properties with traceability
-
-3. **Implementation Tasks** (`.kiro/specs/semantic-video-search/tasks.md`)
- - 25 major tasks, 100+ sub-tasks
- - Database schema breakdown
- - GitHub Actions and Docker integration
- - Requirements traceability
-
-### Next Steps
-- Begin implementation with Task 1: Project setup and development environment
-- Focus on database schema and clean architecture foundation
-- Implement video processing pipeline with task orchestration
+**Duration**: Jan 17-29, 2026 (11 active days)
+**Total Commits**: 228
+**Peak Day**: Jan 18 (64 commits)
---
-## Session: January 18, 2026
-
-### Major Implementations Completed
-
-**PR 1: Task 3 - Database Access Layer (Complete)**
-- ✅ **8 DAOs Implemented**: Video, Transcription, Scene, Object, Face, Topic, PathConfig, Task
-- ✅ **Clean Architecture**: Domain models, repository interfaces, SQLAlchemy implementations
-- ✅ **35 Tests Passing**: Full CRUD coverage with comprehensive domain method testing
-- ✅ **Type Safety**: Modern Python annotations with X | Y union syntax
-- ✅ **Database Integration**: Entity/domain mapping, query optimization, transaction management
-
-**PR 2: Task 4.1 - Path Configuration Manager**
-- ✅ **PathConfigManager Service**: Full CRUD operations for video source paths
-- ✅ **ConfigLoader Service**: JSON config file support with merge behavior
-- ✅ **Command Line Integration**: `--config` argument, environment variables, system defaults
-- ✅ **10 Tests Passing**: Config loading, path management, app integration
-- ✅ **Deployment Ready**: `/etc/eioku/config.json` default, graceful fallbacks
-
-**PR 3: Task 4.2 - Video File Discovery**
-- ✅ **VideoDiscoveryService**: Recursive/non-recursive path scanning
-- ✅ **Format Support**: MP4, MOV, AVI, MKV (case-insensitive)
-- ✅ **Duplicate Detection**: Prevents re-adding existing videos
-- ✅ **File Validation**: Missing file detection for existing videos
-- ✅ **Sample Files**: Test video files for development
-- ✅ **2 Tests Passing**: Core discovery functionality validated
-
-### Technical Achievements
-
-**Database Foundation:**
-- Complete data access layer with 8 specialized DAOs
-- Repository pattern with clean domain/entity separation
-- Comprehensive test coverage ensuring data integrity
-- Ready for video processing pipeline integration
-
-**Configuration System:**
-- Flexible config management supporting deployment and runtime scenarios
-- Merge behavior preserves user configurations while adding new paths
-- Command line and environment variable support for DevOps workflows
-
-**Video Discovery:**
-- Robust file scanning with configurable recursion
-- Format filtering and duplicate prevention
-- Integration with path configuration system
-- Foundation for processing pipeline
-
-### Code Quality Metrics
-- **Total Tests**: 47 tests passing across all components
-- **Linting**: 100% Ruff compliance, zero formatting issues
-- **Architecture**: Clean separation of concerns, dependency injection
-- **Type Safety**: Full type annotations, modern Python patterns
-
-### Ready for Next Phase
-- **Task 5**: Task orchestration system for parallel processing
-- **Task 7**: Video transcription pipeline with Whisper integration
-- **Frontend**: Electron + React application development
-
-The platform now has a solid foundation for video processing with comprehensive path management, database access, and file discovery capabilities.
- - Backend: Python 3.10+ with FastAPI
- - Frontend: Electron + React + TypeScript
- - ML Models: Whisper Large V3, YOLOv8, sentence-transformers (all-MiniLM-L6-v2)
- - Vector Store: FAISS (file-based, scales to 100K+ vectors)
- - Task Queue: Python multiprocessing (built-in, zero dependencies)
-
-4. **Processing Profiles**: Created configurable profiles for different use cases
- - Default "Balanced": Even resource distribution
- - "Search First": Prioritize transcription/embeddings
- - "Visual First": Prioritize object/face detection
- - "Low Resource": Minimal workers for background processing
-
-**Design Decisions:**
-1. **Path-Based Processing**: Videos stay in place, no file copying
- - Rationale: Large video files, avoid duplication
- - Supports both folder-level and individual file processing
-
-2. **Modular Language Support**: English first, architecture supports future languages
- - Rationale: Focus on MVP, but design for extensibility
-
-3. **Property-Based Testing**: Formal correctness properties with PBT
- - Rationale: Ensure correctness across all inputs, not just examples
- - Used prework analysis to convert EARS requirements to testable properties
-
-### Challenges & Solutions
-
-**Challenge 1: Sequential vs Parallel Processing**
-- Problem: Initial design had sequential pipeline (transcription → scenes → objects → faces)
-- Impact: Slow processing, poor resource utilization
-- Solution: Redesigned to parallel task-based architecture with independent workers
-- Result: Much faster, better GPU/CPU utilization, more flexible
-
-**Challenge 2: Vector Database Selection**
-- Problem: Needed zero-ops solution for vector search
-- Options considered: NumPy+JSON, FAISS, ChromaDB, LanceDB
-- Solution: Chose FAISS (file-based, no server, excellent performance)
-- Result: Zero-ops deployment with good scalability
-
-**Challenge 3: Task Orchestration Complexity**
-- Problem: Parallel processing requires complex task coordination
-- Solution: Created task orchestrator with dependency management
-- Implementation: Task types, queues, worker pools, dependency tracking
-- Result: Clean separation of concerns, testable components
-
-**Challenge 4: Flow Diagram Clarity**
-- Problem: Initial flow diagram showed sequential processing despite parallel design
-- Solution: Redesigned flow diagram with branching parallel tasks, color-coded by resource type
-- Result: Clear visualization of parallel execution with example timeline
-
-### Technical Specifications Created
-
-**Requirements Document** (.kiro/specs/semantic-video-search/requirements.md)
-- 10 major requirements with 60+ acceptance criteria
-- EARS-compliant format (Event-driven, State-driven, Ubiquitous patterns)
-- INCOSE quality rules applied
-- Glossary with 12 defined terms
-
-**Design Document** (.kiro/specs/semantic-video-search/design.md)
-- Complete technology stack definition
-- Parallel processing architecture with task orchestration
-- 4 processing profiles with worker configurations
-- 7 comprehensive user flow diagrams
-- 29 correctness properties with requirements traceability
-- Component interfaces and data models
-- Error handling and testing strategy
-
-**Implementation Tasks** (.kiro/specs/semantic-video-search/tasks.md)
-- 25 major tasks
-- 100+ sub-tasks with granular breakdown
-- 5 strategic checkpoints
-- Property-based tests for 29 correctness properties
-- GitHub Actions and Docker integration
-- Requirements traceability for every task
-
-All tasks added to GitHub Milestones and Issues.
-
-### Metrics
-
-**Time Spent:**
-- Requirements: ~30 minutes
-- Design: ~90 minutes
-- Implementation Planning: ~45 minutes
-- Total: ~2.5 hours
-
-**Deliverables:**
-- 3 specification documents
-- 10 requirements with 60+ acceptance criteria
-- 29 correctness properties
-- 7 user flow diagrams
-- 25 major tasks, 100+ sub-tasks
-- Complete technology stack definition
-
-**Lines of Specification:**
-- Requirements: ~350 lines
-- Design: ~1,200 lines
-- Tasks: ~650 lines
-- Total: ~2,200 lines of specification
-
-### Next Steps
-
-1. **Immediate**: Begin implementation with Task 1 (project setup)
-2. **Short-term**: Implement core data layer (Tasks 2-3)
-3. **Medium-term**: Implement video processing pipeline (Tasks 4-10)
-4. **Long-term**: Build frontend UI and complete integration (Tasks 16-25)
-
-### Lessons Learned
-
-1. **Parallel Processing Early**: Should have started with parallel architecture from the beginning
-2. **Zero-Ops Focus**: Simplicity constraint led to better architectural decisions
-3. **Flow Diagrams Essential**: Visual flows caught architectural issues that text missed
-4. **Granular Tasks**: Breaking database/DAO tasks per-model improved clarity
-5. **Property-Based Testing**: Prework analysis helped convert requirements to testable properties
-
-### References
-
-- OpenAI Whisper: https://github.com/openai/whisper
-- Ultralytics YOLOv8: https://github.com/ultralytics/ultralytics
-- FAISS: https://github.com/facebookresearch/faiss
-- PySceneDetect: https://github.com/Breakthrough/PySceneDetect
-- sentence-transformers: https://www.sbert.net/
+## Timeline
+
+### Day 1 - Jan 17 (9 commits)
+- Project init, steering files, boilerplate
+- Docker compose for dev environment
+- **Decision**: FastAPI + React + PostgreSQL stack
+
+### Day 2 - Jan 18 (64 commits) - Heavy lift day
+- Database schema: Videos, Transcriptions, Scenes, Objects, Faces, Topics, Tasks
+- Clean architecture: DAOs, repositories, domain models
+- Path management & video discovery service
+- Task orchestration with worker pools
+- Whisper transcription pipeline
+- **Decision**: Separate table per artifact type (later revised)
+- **Challenge**: Wiring worker pools with automatic task processing
+
+### Day 3 - Jan 19 (12 commits)
+- YOLO object detection integration
+- Scene detection with PySceneDetect
+- Face detection pipeline
+- **Decision**: GPU worker pool separate from CPU pool
+- **Challenge**: GPU memory management across models
+
+### Day 4 - Jan 20 (16 commits)
+- Place recognition (Places365)
+- OCR pipeline (EasyOCR)
+- Artifact envelope architecture design
+- **Decision**: JSONB payloads with schema versioning
+- **Decision**: Projection tables for fast queries
+
+### Day 5 - Jan 22 (8 commits)
+- Artifact envelope implementation
+- Schema registry pattern
+- **Challenge**: Migration from separate tables to unified artifacts
+
+### Day 6 - Jan 24 (23 commits)
+- GPU semaphore for concurrency control
+- Async job queue with arq
+- Redis/Valkey integration
+- **Decision**: Queue between API and ML worker (not HTTP)
+- **Challenge**: CUDA library paths in Docker
+
+### Day 7 - Jan 25 (39 commits)
+- ML Service separation from API
+- Job producer/consumer pattern
+- Reconciler for stale tasks
+- Input hash verification
+- **Decision**: Separate ML service for GPU isolation
+- **Challenge**: Service communication without tight coupling
+
+### Day 8 - Jan 26 (6 commits)
+- JSON structured logging
+- Kiro hooks: lint, test, db reset, code review
+- Test suite updates for new architecture
+
+### Day 9 - Jan 27 (16 commits)
+- Complete worker-ML service separation
+- Video streaming endpoint
+- Frontend POCs: scene jumping, face detection, gallery
+- Inference performance improvements
+- **Challenge**: Scene detection threshold tuning (settled on 0.7)
+
+### Day 10 - Jan 28 (25 commits)
+- Metadata extraction pipeline (EXIF, ffprobe)
+- Reverse geocoding for GPS
+- Global jump navigation backend
+- Projection sync for metadata
+- **Decision**: Use `file_created_at` for chronological ordering
+- **Challenge**: Cross-video search ordering strategy
+
+### Day 11 - Jan 29 (10 commits)
+- Global jump frontend
+- Video clip export with ffmpeg
+- API documentation
+- Spec finalization
+
+### Day 12 - Jan 30
+- Complete prod images and prod demo docker compose files
+- Add ML to CI
+- Do not allow retry completed tasks
+- Enable API_URL override in frontend
---
-## Session: January 19, 2026
+## Key Decisions
-**Overview**: Implemented object detection pipeline and migrated from SQLite to PostgreSQL.
-
-**Actions**: Added YOLOv8 object detection with PyAV video processing, created detection service and task handler, integrated with worker pools. Migrated entire database infrastructure to PostgreSQL for better concurrency and F support.
-
-**Outcome**: Object detection working with 2 GPU workers processing frames at 30fps intervals. PostgreSQL migration successful with all 297 tests passing. Fixed numpy float serialization issues for PostgreSQL compatibility.
+| Decision | Rationale |
+|----------|-----------|
+| JSONB + projection tables | Flexibility of document store + performance of relational |
+| Redis queue (not HTTP) | ML inference takes 30s-2min, HTTP would timeout |
+| Separate ML service | GPU resources expensive, API scales on cheap CPUs |
+| `file_created_at` ordering | EXIF date reflects actual filming order |
+| arq for job queue | Built-in retry, backpressure, Redis-native |
+| Schema versioning | Multiple model versions coexist, re-process without data loss |
---
-## Session: January 20, 2026
+## Major Challenges
-**Overview**: Implemented artifact envelope architecture to replace legacy projection tables.
+1. **GPU memory** - Multiple models competing for VRAM
+ - Solved: GPU semaphore limiting concurrent inference
-**Actions**: Created artifact envelope domain model with schema registry, implemented repositories for artifacts and runs, added selection policies for multi-profile support. Migrated all artifact types (transcripts, scenes, objects, faces, places, OCR) to new architecture with projection sync service.
+2. **CUDA in Docker** - Library paths, driver compatibility
+ - Solved: nvidia-container-toolkit, explicit LD_LIBRARY_PATH
-**Outcome**: Clean separation between raw artifacts and queryable projections. All artifact types integrated with Full-Text Search (FTS) and specialized projections. 297 tests passing with comprehensive artifact coverage.
-
----
+3. **Artifact migration** - Moving from 6 tables to envelope pattern
+ - Solved: Phased migration with projection tables for backwards compat
-## Session: January 22, 2026
+4. **Cross-video search** - Consistent ordering across videos
+ - Solved: Triple sort key (file_created_at, video_id, start_ms)
-**Overview**: Completed artifact envelope API and navigation services.
-
-**Actions**: Implemented jump navigation service for artifact-to-artifact navigation, added FindWithinVideoService for keyword search across transcripts and OCR. Created comprehensive API endpoints for artifact queries with filtering and selection policies. Removed all legacy code and tables.
-
-**Outcome**: Full artifact envelope architecture operational. Jump navigation working across all artifact types. Multi-profile support enables quality/speed tradeoffs. Migration from legacy architecture complete.
+5. **Service decoupling** - API and ML service communication
+ - Solved: Redis queue, no direct HTTP calls
---
-## Session: January 24, 2026
+## Commits by Day
+
+```
+Jan 17 ████████░░░░░░░░░░░░ 9
+Jan 18 ████████████████████ 64 (peak)
+Jan 19 ██████░░░░░░░░░░░░░░ 12
+Jan 20 ████████░░░░░░░░░░░░ 16
+Jan 22 ████░░░░░░░░░░░░░░░░ 8
+Jan 24 ███████████░░░░░░░░░ 23
+Jan 25 ███████████████████░ 39
+Jan 26 ███░░░░░░░░░░░░░░░░░ 6
+Jan 27 ████████░░░░░░░░░░░░ 16
+Jan 28 ████████████░░░░░░░░ 25
+Jan 29 █████░░░░░░░░░░░░░░░ 10
+```
-**Overview**: Fixed worker pool session isolation and added missing OCR/place detection workers.
+---
-**Actions**: Implemented batch_create() for efficient bulk artifact inserts, fixed worker pool to create per-thread database sessions preventing transaction conflicts. Added OCR and place detection workers to all processing profiles. Fixed PostgreSQL compatibility in projection sync (INSERT OR REPLACE → ON CONFLICT). Fixed place detection service file paths for Docker.
+## Kiro Specs Created
-**Outcome**: Session isolation resolved - no more transaction errors. Batch operations significantly improved performance (2,686+ artifacts created successfully). OCR and place detection tasks now processing. All 297 tests passing.
+1. `artifact-envelope-architecture` - Core storage design
+2. `worker-ml-service-separation` - Service architecture
+3. `global-jump-navigation` - Cross-video search
+4. `global-jump-navigation-gui` - Frontend spec
+5. `video-metadata-extraction` - EXIF pipeline
+6. `artifact-thumbnails-gallery` - Gallery UI
+7. `semantic-video-search` - Future: vector embeddings
diff --git a/README.md b/README.md
index d8b0b07..f0c3904 100644
--- a/README.md
+++ b/README.md
@@ -1,218 +1,274 @@
-# Eioku - Semantic Video Search Platform
-
-A powerful semantic video search platform designed for video editors with large content libraries. Eioku enables natural language search across video content with advanced navigation features including transcript-based navigation, scene jumping, and object/face detection.
+- [Eioku - Video Intelligence Platform](#eioku---video-intelligence-platform)
+ - [What It Does](#what-it-does)
+ - [Demo Checklist](#demo-checklist)
+ - [Limitations](#limitations)
+ - [Quick Start with Docker](#quick-start-with-docker)
+ - [GPU (CUDA) - Recommended for NVIDIA GPUs](#gpu-cuda---recommended-for-nvidia-gpus)
+ - [CPU - For Apple Silicon or systems without NVIDIA GPU](#cpu---for-apple-silicon-or-systems-without-nvidia-gpu)
+ - [Stopping and Cleaning Up](#stopping-and-cleaning-up)
+ - [Performance Comparison](#performance-comparison)
+ - [Architecture](#architecture)
+ - [OpenAPI Documentation](#openapi-documentation)
+ - [Kiro Usage](#kiro-usage)
+ - [Development Workflow](#development-workflow)
+ - [Specs (7 features)](#specs-7-features)
+ - [Steering (3 rules)](#steering-3-rules)
+ - [Hooks (11 automations)](#hooks-11-automations)
+ - [Agent Prompt](#agent-prompt)
+ - [Related Documentation](#related-documentation)
+ - [Contributing](#contributing)
+
+# Eioku - Video Intelligence Platform
+
+Eioku transforms your video library into a searchable database. You film it, we index it, you find it.
+
+---
+
+## What It Does
+
+- **Automatic ML Analysis**: Drop videos in a folder, Eioku automatically runs:
+ - Object detection (YOLO) - find every dog, car, person
+ - Speech transcription (Whisper) - searchable transcripts
+ - OCR (EasyOCR) - text visible in frames
+ - Place recognition (Places365) - indoor/outdoor scene classification
+ - Scene detection - shot boundary detection
+ - Metadata extraction - EXIF timestamps, GPS, duration
+
+- **Cross-Video Search**: Search across your entire library chronologically
+ - "Find every dog scene" - jumps between videos automatically
+ - Ordered by when you actually filmed (EXIF date), not upload date
+
+- **Global Jump Navigation**: Cmd+F for your video archive
+ - Next/Previous buttons navigate across all videos
+ - Full-text search on transcripts and OCR text
+
+---
+
+## Demo Checklist
+
+> Hackathon judges: Use the Google Drive videos from the submission, or bring your own!
+
+1. Add videos to `test-videos/` directory (variety: dogs, people, speech, text, GPS)
+2. Start the environment using the [Quick Start](#quick-start-with-docker) instructions below
+3. Open http://localhost:9080
+4. Wait for ML tasks to complete (check status in video player view)
+5. Try features:
+ - **Video Player**: Explore detected objects, transcripts, OCR, places
+ - **Clip Export**: Set timestamps and download a clip
+ - **Global Jump**: Search + use Previous/Next to jump across videos
+ - **Artifact Gallery**: See all matches with thumbnails
+
+### Limitations
+
+1. **Some ML Models download per boot**: Each time the ml-service reboots, it may download some YOLO models, which will delay processing for up to a minute or so (I missed this during the build process >:|)
+2. **Video discovery**: Videos are only discovered when the backend starts - no hot-reload for new files
+3. **Face search**: Face detection runs but face search/clustering is not implemented yet
+4. **No combined filters**: Global jump searches one artifact type at a time (can't search "dog AND tokyo")
+5. **Single language OCR**: OCR is configured for English only
+6. **No semantic search**: Text search is exact match only - no embeddings or similarity search yet
+7. **Transcription language**: Whisper auto-detects language but works best with English
+8. **GPS reverse geocoding**: Requires internet connection for location names
+9. **Large video files**: Very long videos (>1hr) may timeout during processing
+10. **No search suggestions**: No aggregation of available labels/terms - users must guess what to search for -- you
+can view individual videos to get an idea for what does
+exist, but it's far from ideal (e.g., no "show me all detected objects or spoken words")
+
+---
+
+## Quick Start with Docker
+
+> Important: the ml-service image is very large due to containing
+> ML models (~10GB). It may take a while to pull depending on your
+> network.
+
+> Important: the images are only built for `amd64`, so expect
+> slower performance on Apple Silicon + Rosetta2.
+
+Two deployment options are available depending on your hardware:
+
+### GPU (CUDA) - Recommended for NVIDIA GPUs
+
+For systems with NVIDIA GPUs and the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed:
+
+```bash
+# Start with GPU acceleration (~10x faster ML processing)
+docker compose -f docker/docker-compose.cuda.yml up
+
+# Access the app
+open http://localhost:9080
+```
-## Overview
+### CPU - For Apple Silicon or systems without NVIDIA GPU
-Eioku processes videos from configured file paths, extracts audio for transcription, performs computer vision analysis (scene detection, object detection, face detection), and enables semantic search through natural language queries. The platform provides an interactive player view with multiple navigation methods to help editors quickly find and navigate to specific content.
+For Mac (Apple Silicon), Windows without NVIDIA GPU, or Linux without CUDA:
-**Example Query**: "Show me clips where I talk about Kiro"
-**Result**: Gallery of video segments with timestamps where the term appears
+```bash
+# Start with CPU-only processing (slower but universal)
+docker compose -f docker/docker-compose.cpu.yml up
-## Key Features
+# Access the app
+open http://localhost:9080
+```
-### 🔍 Semantic Search
-- Natural language queries across video transcriptions
-- Synonym and concept understanding
-- Relevance-ranked results with timestamps
-- Topic-based search suggestions
+> **Note for Apple Silicon users**: Use the CPU environment. The CUDA environment requires NVIDIA GPUs which are not available on Apple hardware. MPS is not supported in docker containers, and I did not have time to create a non-docker environment for the demo.
-### 🎬 Advanced Video Navigation
-- **Transcript Navigation**: Click any transcript text to jump to that moment
-- **Scene Navigation**: Next/previous scene controls with automatic boundary detection
-- **Object Navigation**: Jump between occurrences of detected objects
-- **Face Navigation**: Navigate between appearances of detected faces
-- **Timeline Markers**: Visual timeline with scene, object, and face markers
+### Stopping and Cleaning Up
-### 🚀 Parallel Processing
-- Independent task processing for maximum efficiency
-- GPU acceleration for object/face detection
-- CPU parallelization for transcription and scene detection
-- Configurable processing profiles (Balanced, Search First, Visual First, Low Resource)
+```bash
+# Stop the environment
+docker compose -f docker/docker-compose.cuda.yml down
+docker compose -f docker/docker-compose.cpu.yml down
-### 📊 Topic Discovery
-- Automatic topic extraction from video content
-- Aggregated topics across entire library
-- Clickable topic suggestions for quick search
+# Stop and remove all data (fresh start)
+docker compose -f docker/docker-compose.cuda.yml down -v
+docker compose -f docker/docker-compose.cpu.yml down -v
+```
-### 💾 Zero-Ops Architecture
-- No external servers or databases required
-- File-based storage (SQLite + FAISS)
-- Single-host deployment
-- Desktop application (Electron)
+### Performance Comparison
-## Technology Stack
+Benchmark on 13 test videos (557 MB total, 2.5-275 MB each):
-### Backend
-- **Language**: Python 3.10+
-- **Framework**: FastAPI
-- **Database**: SQLite
-- **Vector Store**: FAISS
-- **Task Queue**: Python multiprocessing
+| Environment | Hardware | Tasks | Duration | Rate |
+|-------------|----------|-------|----------|------|
+| Docker CUDA | RTX 3070 Ti (8GB) | 130 | 9 min 9 sec | ~14.2 tasks/min |
+| Docker CPU | Ryzen 9 5900X | 80 | 1 hr 7 min | ~1.2 tasks/min |
-### Machine Learning
-- **Transcription**: OpenAI Whisper (Large V3 or Turbo)
-- **Embeddings**: sentence-transformers (all-MiniLM-L6-v2)
-- **Scene Detection**: PySceneDetect
-- **Object Detection**: Ultralytics YOLOv8
-- **Face Detection**: YOLOv8 Face Model
+GPU is roughly **10-12x faster** than CPU for ML processing.
-### Frontend
-- **Framework**: Electron + React + TypeScript
-- **UI Library**: Material-UI or Tailwind CSS
-- **Video Player**: HTML5 Video with custom controls
+---
-## Project Structure
+## Architecture
```
-.
-├── .kiro/
-│ └── specs/
-│ └── semantic-video-search/
-│ ├── requirements.md # EARS-compliant requirements
-│ ├── design.md # Architecture and design
-│ └── tasks.md # Implementation tasks
-├── DEVLOG.md # Development log
-└── README.md # This file
+┌─────────────┐ ┌─────────────┐ ┌─────────────┐
+│ Frontend │────▶│ API Service│────▶│ PostgreSQL │
+│ (React) │ │ (FastAPI) │ │ (JSONB) │
+└─────────────┘ └──────┬──────┘ └──────▲──────┘
+ │ │
+ ┌──────▼──────┐ │
+ │ Redis │ │
+ │ (Valkey) │ │
+ └──────┬──────┘ │
+ │ │
+ ┌──────▼──────┐ │
+ │ ML Service │────────────┘
+ │ (GPU/arq) │ writes artifacts
+ └─────────────┘
```
-## Getting Started
+See [changes.md](./docs/changes.md) for full C4 diagrams and architecture evolution.
+
+---
+
+## OpenAPI Documentation
+
+When running locally:
+
+> Note: this is when using the compose files under the [docker](./docker/) folder.
+> For developing, see: [Contributing](#contributing).
+
+- **Swagger UI**: http://localhost:9080/api/docs
+- **ReDoc**: http://localhost:9080/api/redoc
+- **OpenAPI JSON**: http://localhost:9080/api/openapi.json
+
+## Kiro Usage
+
+This project extensively uses Kiro's spec-driven development workflow and automation features.
+
+### Development Workflow
+
+```mermaid
+flowchart TD
+ subgraph Spec["Spec-Driven Design (Kiro)"]
+ REQ[Define Requirements] --> DES[Create Design]
+ DES --> TASKS[Generate Tasks]
+ end
+
+ subgraph Dev["Development Loop (per task)"]
+ CODE[Write Code] --> REVIEW[Review with Agent Prompt]
+ REVIEW --> APPLY[Apply Suggestions]
+ APPLY --> LINT[Lint]
+ LINT --> TEST[Test]
+ TEST --> BUILD[Build]
+ end
+
+ subgraph PR["Pull Request"]
+ COMMIT[Commit] --> CREATE[Create PR]
+ CREATE --> DESC[Describe PR]
+ DESC --> MERGE[Merge to Main]
+ end
+
+ TASKS --> CODE
+ BUILD --> COMMIT
+ MERGE --> REQ
+
+ %% Kiro Hooks
+ H1([🪝 principal-code-review]):::hook -.-> REVIEW
+ H2([🪝 lint-backend-code]):::hook -.-> LINT
+ H3([🪝 lint-ml-service]):::hook -.-> LINT
+ H4([🪝 lint-frontend]):::hook -.-> LINT
+ H5([🪝 run-backend-tests]):::hook -.-> TEST
+ H6([🪝 run-ml-service-tests]):::hook -.-> TEST
+ H7([🪝 pre-commit-checks]):::hook -.-> COMMIT
+
+ classDef hook fill:#2563eb,stroke:#1d4ed8,stroke-width:1px,color:#fff
+```
-### Prerequisites
-
-**Minimum Requirements:**
-- CPU: 4 cores
-- RAM: 8 GB
-- Storage: 10 GB + video library size
-- Python 3.10+
-- Node.js 18+ (for Electron frontend)
-
-**Recommended:**
-- CPU: 8+ cores
-- RAM: 16 GB
-- GPU: NVIDIA GPU with 4+ GB VRAM (CUDA support)
-- Storage: SSD with 50 GB + video library size
-
-### Installation
-
-*Coming soon - implementation in progress*
-
-## Specification Documents
-
-This project follows a spec-driven development approach with comprehensive documentation:
-
-### 📋 Requirements Document
-- 10 major requirements with 60+ acceptance criteria
-- EARS-compliant format (Easy Approach to Requirements Syntax)
-- INCOSE quality rules applied
-- Complete glossary of terms
-
-**Location**: `.kiro/specs/semantic-video-search/requirements.md`
-
-### 🏗️ Design Document
-- Parallel processing architecture with task orchestration
-- Complete technology stack definition
-- 4 processing profiles with worker configurations
-- 7 comprehensive user flow diagrams
-- 29 correctness properties with requirements traceability
-- Component interfaces and data models
-- Error handling and testing strategy
-
-**Location**: `.kiro/specs/semantic-video-search/design.md`
-
-### ✅ Implementation Tasks
-- 25 major tasks with 100+ sub-tasks
-- Granular breakdown of database schema and DAOs
-- Property-based tests for correctness validation
-- GitHub Actions and Docker integration
-- Requirements traceability for every task
-
-**Location**: `.kiro/specs/semantic-video-search/tasks.md`
-
-## Development Approach
-
-### Spec-Driven Development
-1. **Requirements First**: Define what the system should do
-2. **Design Second**: Define how the system will work
-3. **Tasks Third**: Break down implementation into actionable steps
-4. **Implementation**: Execute tasks with continuous validation
-
-### Property-Based Testing
-- Formal correctness properties derived from requirements
-- Universal properties tested across all inputs (not just examples)
-- Minimum 100 iterations per property test
-- Hypothesis (Python) for property-based testing
-
-### Incremental Development
-- Bottom-up approach: data layer → processing → API → UI
-- Strategic checkpoints for validation
-- Optional tasks marked for faster MVP
-
-## Processing Profiles
-
-### Balanced (Default)
-Even resource distribution, optimized for general use
-- Transcription: 2 workers (high priority)
-- Scene Detection: 2 workers (medium priority)
-- Object Detection: 2 workers (medium priority, GPU)
-- Face Detection: 2 workers (medium priority, GPU)
-
-### Search First
-Prioritize getting videos searchable quickly
-- Transcription: 4 workers (critical priority)
-- Embedding Generation: 2 workers (critical priority)
-- Visual features: 1 worker each (low priority)
-
-### Visual First
-Prioritize object and face detection for visual navigation
-- Object Detection: 3 workers (critical priority, GPU)
-- Face Detection: 3 workers (critical priority, GPU)
-- Scene Detection: 2 workers (high priority)
-
-### Low Resource
-Minimal resource usage for background processing
-- All tasks: 1 worker each
-- Max concurrent videos: 1
-
-## Supported Video Formats
-
-- MP4
-- MOV
-- AVI
-- MKV
-
-## Roadmap
-
-### Phase 1: Core Platform (Current)
-- ✅ Requirements specification
-- ✅ Design specification
-- ✅ Implementation tasks
-- ⏳ Backend implementation
-- ⏳ Frontend implementation
-
-### Phase 2: Adobe Premiere Plugin
-- Integration with Adobe Premiere Pro
-- Direct timeline insertion
-- Seamless workflow integration
-
-### Phase 3: Advanced Features
-- Multi-language support
-- Action detection (optional)
-- Face clustering and recognition
-- Custom vocabulary support
+### Specs (7 features)
-## Contributing
+> Note: the architecture for this project evolved a lot as I made progress. Review
+> those changes here: [Architecture Evolution](./changes.md).
+
+Each feature was designed using Kiro's requirements → design → tasks workflow.
+I made extensive use of the [principal-software-engineer.agent](.kiro/prompts/principal-software-engineer.agent.md) for software architecture decisions
+and code reviews.
+
+| Spec | Description |
+|------|-------------|
+| `artifact-envelope-architecture` | Unified artifact storage model with schema registry, projections, and selection policies |
+| `global-jump-navigation` | Cross-video search and navigation API using global timeline ordering |
+| `global-jump-navigation-gui` | Frontend UI for global jump with search controls |
+| `artifact-thumbnails-gallery` | Thumbnail extraction task and gallery search API/UI |
+| `worker-ml-service-separation` | Split monolith into API, Worker, and ML services with Redis job queue |
+| `video-metadata-extraction` | EXIF metadata extraction and GPS location handling |
+| `semantic-video-search` | Core video search and artifact query functionality |
-*Coming soon*
+### Steering (3 rules)
-## License
+Always-on guidance for consistent development:
-*To be determined*
+- `development-principles.md` - Incremental commits, approval workflow, conventional commits, quality gates
+- `fastapi-dev-environment.md` - FastAPI best practices, async-first, Pydantic everywhere, container-first dev
+- `trunk-based-development.md` - Short-lived branches, small PRs, CI/CD integration
-## Acknowledgments
+### Hooks (11 automations)
+
+User-triggered commands for common workflows:
+
+| Hook | Action |
+|------|--------|
+| `start-dev-env` | Start Docker dev environment |
+| `stop-dev-env` | Stop Docker dev environment |
+| `reset-database` | Reset PostgreSQL and restart services |
+| `lint-backend-code` | Run Ruff on backend |
+| `lint-ml-service` | Run Ruff on ml-service |
+| `lint-frontend` | Run ESLint on frontend |
+| `run-backend-tests` | Run pytest on backend |
+| `run-ml-service-tests` | Run pytest on ml-service |
+| `pre-commit-checks` | Full lint + test + commit workflow |
+| `principal-code-review` | AI code review using principal engineer agent |
+
+### Agent Prompt
+
+- `principal-software-engineer.agent.md` - Expert-level engineering guidance for code reviews, focusing on design patterns, SOLID principles, testing strategy, and technical debt management with GitHub issue creation
+
+## Related Documentation
+
+- [Architecture Evolution](./changes.md) - C4 diagrams, trade-offs, phases
+- [Development Log](./DEVLOG.md) - Timeline, decisions, challenges
+- [Attribution](./ATTRIBUTION.md) - Third-party libraries
+- [Presentation Script](./presentation-script.md) - Demo video script
+
+## Contributing
-- OpenAI Whisper for transcription
-- Ultralytics for YOLOv8
-- Facebook Research for FAISS
-- PySceneDetect for scene detection
-- sentence-transformers for embeddings
+See: [CONTRIBUTING](./CONTRIBUTING.md).
diff --git a/docker/Dockerfile.backend b/docker/Dockerfile.backend
new file mode 100644
index 0000000..dad20d4
--- /dev/null
+++ b/docker/Dockerfile.backend
@@ -0,0 +1,20 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+ libpq-dev gcc ffmpeg \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN pip install poetry
+
+COPY backend/pyproject.toml backend/poetry.lock ./
+RUN poetry config virtualenvs.create false && poetry install --only main --no-root --no-interaction
+
+COPY backend/src ./src
+COPY backend/alembic ./alembic
+COPY backend/alembic.ini ./
+COPY backend/gunicorn.conf.py ./
+
+EXPOSE 8000
+CMD ["gunicorn", "src.main_api:app", "--config", "gunicorn.conf.py"]
diff --git a/docker/Dockerfile.frontend b/docker/Dockerfile.frontend
new file mode 100644
index 0000000..0e92993
--- /dev/null
+++ b/docker/Dockerfile.frontend
@@ -0,0 +1,12 @@
+FROM node:20-alpine AS builder
+
+WORKDIR /app
+COPY frontend/package*.json ./
+RUN npm ci
+COPY frontend/ ./
+RUN npm run build
+
+FROM nginx:alpine
+COPY --from=builder /app/dist /usr/share/nginx/html
+EXPOSE 80
+CMD ["nginx", "-g", "daemon off;"]
diff --git a/docker/Dockerfile.ml-service b/docker/Dockerfile.ml-service
new file mode 100644
index 0000000..9537bab
--- /dev/null
+++ b/docker/Dockerfile.ml-service
@@ -0,0 +1,55 @@
+# syntax=docker/dockerfile:1.4
+FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
+
+ENV TZ=UTC
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+
+RUN DEBIAN_FRONTEND=noninteractive apt-get update && apt-get install -y --no-install-recommends \
+ ffmpeg \
+ libgl1-mesa-glx \
+ libglib2.0-0 \
+ curl \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install exiftool from pre-built distribution
+RUN curl -sL https://sourceforge.net/projects/exiftool/files/Image-ExifTool-13.47.tar.gz/download -o /tmp/Image-ExifTool-13.47.tar.gz && \
+ echo "97efd14de0fea5b29a689dad5334ea9c0600f3d64ebc062e30f3aa89fde3668c /tmp/Image-ExifTool-13.47.tar.gz" | sha256sum -c - && \
+ tar -xzf /tmp/Image-ExifTool-13.47.tar.gz -C /tmp && \
+ cp /tmp/Image-ExifTool-13.47/exiftool /usr/local/bin/ && \
+ cp -r /tmp/Image-ExifTool-13.47/lib /usr/local/bin/ && \
+ chmod +x /usr/local/bin/exiftool && \
+ rm -rf /tmp/Image-ExifTool-13.47*
+
+RUN pip install --no-cache-dir poetry==1.7.1
+RUN poetry config virtualenvs.create false
+
+WORKDIR /app
+
+COPY ml-service/pyproject.toml ml-service/poetry.lock* ./
+RUN poetry install --no-root
+
+COPY ml-service/categories_places365.txt ./
+COPY ml-service/src ./src
+
+RUN mkdir -p /models/ultralytics /models/easyocr /models/huggingface && \
+ chmod 777 /models /models/ultralytics /models/easyocr /models/huggingface
+
+# Copy YOLO model files
+COPY ml-service/yolov8n.pt /models/ultralytics/
+COPY ml-service/yolov8n-face.pt /models/ultralytics/
+
+# Set model cache environment variables
+ENV HF_HOME=/models/huggingface
+ENV EASYOCR_MODULE_PATH=/models/easyocr
+ENV MODEL_CACHE_DIR=/models
+
+# Download Whisper model at build time (~1.5GB)
+RUN python -c "from faster_whisper import WhisperModel; WhisperModel('large-v3-turbo', device='cpu', compute_type='auto')"
+
+# Download EasyOCR models at build time (~100MB)
+RUN python -c "import easyocr; easyocr.Reader(['en'], gpu=False, verbose=False)"
+
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+CMD ["python", "-m", "arq", "src.main_worker.App"]
diff --git a/docker/docker-compose.cpu.yml b/docker/docker-compose.cpu.yml
new file mode 100644
index 0000000..459068e
--- /dev/null
+++ b/docker/docker-compose.cpu.yml
@@ -0,0 +1,96 @@
+services:
+ postgres:
+ image: postgres:16-alpine
+ environment:
+ POSTGRES_USER: eioku
+ POSTGRES_PASSWORD: eioku_demo
+ POSTGRES_DB: eioku
+ volumes:
+ - postgres_data:/var/lib/postgresql/data
+ healthcheck:
+ test: ["CMD-SHELL", "pg_isready -U eioku"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ networks:
+ - eioku
+
+ valkey:
+ image: valkey/valkey:8-alpine
+ command: valkey-server --appendonly yes
+ volumes:
+ - valkey_data:/data
+ healthcheck:
+ test: ["CMD", "valkey-cli", "ping"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ networks:
+ - eioku
+
+ backend:
+ image: codihuston/eioku-backend:0.0.0
+ platform: linux/amd64
+ environment:
+ - DATABASE_URL=postgresql://eioku:eioku_demo@postgres:5432/eioku
+ - REDIS_URL=redis://valkey:6379
+ volumes:
+ - ../test-videos:/media:ro
+ - ../config:/app/config:ro
+ - thumbnails_data:/thumbnails
+ depends_on:
+ postgres:
+ condition: service_healthy
+ valkey:
+ condition: service_healthy
+ networks:
+ - eioku
+
+ frontend:
+ image: codihuston/eioku-frontend:0.0.0
+ platform: linux/amd64
+ networks:
+ - eioku
+
+ ml-service:
+ image: codihuston/eioku-ml-service:0.0.0
+ platform: linux/amd64
+ environment:
+ - DATABASE_URL=postgresql://eioku:eioku_demo@postgres:5432/eioku
+ - REDIS_URL=redis://valkey:6379
+ - DEVICE=cpu
+ - MODEL_CACHE_DIR=/models
+ - HF_HOME=/models/huggingface
+ - YOLO_HOME=/models/ultralytics
+ - EASYOCR_MODULE_PATH=/models/easyocr
+ volumes:
+ - ../test-videos:/media:ro
+ - thumbnails_data:/data/thumbnails
+ depends_on:
+ postgres:
+ condition: service_healthy
+ valkey:
+ condition: service_healthy
+ networks:
+ - eioku
+
+ nginx:
+ image: nginx:alpine
+ ports:
+ - "9080:80"
+ volumes:
+ - ./nginx.conf:/etc/nginx/nginx.conf:ro
+ depends_on:
+ - backend
+ - frontend
+ networks:
+ - eioku
+
+networks:
+ eioku:
+ driver: bridge
+
+volumes:
+ postgres_data:
+ valkey_data:
+ thumbnails_data:
diff --git a/docker/docker-compose.cuda.yml b/docker/docker-compose.cuda.yml
new file mode 100644
index 0000000..cce3948
--- /dev/null
+++ b/docker/docker-compose.cuda.yml
@@ -0,0 +1,100 @@
+services:
+ postgres:
+ image: postgres:16-alpine
+ environment:
+ POSTGRES_USER: eioku
+ POSTGRES_PASSWORD: eioku_demo
+ POSTGRES_DB: eioku
+ volumes:
+ - postgres_data:/var/lib/postgresql/data
+ healthcheck:
+ test: ["CMD-SHELL", "pg_isready -U eioku"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ networks:
+ - eioku
+
+ valkey:
+ image: valkey/valkey:8-alpine
+ command: valkey-server --appendonly yes
+ volumes:
+ - valkey_data:/data
+ healthcheck:
+ test: ["CMD", "valkey-cli", "ping"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ networks:
+ - eioku
+
+ backend:
+ image: codihuston/eioku-backend:0.0.0
+ environment:
+ - DATABASE_URL=postgresql://eioku:eioku_demo@postgres:5432/eioku
+ - REDIS_URL=redis://valkey:6379
+ volumes:
+ - ../test-videos:/media:ro
+ - ../config:/app/config:ro
+ - thumbnails_data:/thumbnails
+ depends_on:
+ postgres:
+ condition: service_healthy
+ valkey:
+ condition: service_healthy
+ networks:
+ - eioku
+
+ frontend:
+ image: codihuston/eioku-frontend:0.0.0
+ networks:
+ - eioku
+
+ ml-service:
+ image: codihuston/eioku-ml-service:0.0.0
+ environment:
+ - DATABASE_URL=postgresql://eioku:eioku_demo@postgres:5432/eioku
+ - REDIS_URL=redis://valkey:6379
+ - NVIDIA_VISIBLE_DEVICES=all
+ - MODEL_CACHE_DIR=/models
+ - HF_HOME=/models/huggingface
+ - YOLO_HOME=/models/ultralytics
+ - EASYOCR_MODULE_PATH=/models/easyocr
+ volumes:
+ - ../test-videos:/media:ro
+ - thumbnails_data:/data/thumbnails
+ depends_on:
+ postgres:
+ condition: service_healthy
+ valkey:
+ condition: service_healthy
+ networks:
+ - eioku
+ deploy:
+ resources:
+ reservations:
+ devices:
+ - driver: nvidia
+ count: all
+ capabilities: [gpu]
+
+ nginx:
+ image: nginx:alpine
+ ports:
+ - "9080:80"
+ volumes:
+ - ./nginx.conf:/etc/nginx/nginx.conf:ro
+ depends_on:
+ - backend
+ - frontend
+ networks:
+ - eioku
+
+networks:
+ eioku:
+ driver: bridge
+
+volumes:
+ postgres_data:
+ valkey_data:
+ thumbnails_data:
diff --git a/docker/nginx.conf b/docker/nginx.conf
new file mode 100644
index 0000000..7b61cfb
--- /dev/null
+++ b/docker/nginx.conf
@@ -0,0 +1,46 @@
+events {
+ worker_connections 1024;
+}
+
+http {
+ include /etc/nginx/mime.types;
+ default_type application/octet-stream;
+
+ upstream backend {
+ server backend:8000;
+ }
+
+ upstream frontend {
+ server frontend:80;
+ }
+
+ server {
+ listen 80;
+
+ # API routes
+ location /api/ {
+ proxy_pass http://backend/;
+ proxy_set_header Host $host;
+ proxy_set_header X-Real-IP $remote_addr;
+ proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+ proxy_set_header Range $http_range;
+ proxy_set_header If-Range $http_if_range;
+ proxy_read_timeout 300s;
+ }
+
+ # Health check
+ location /health {
+ proxy_pass http://backend/health;
+ proxy_set_header Host $host;
+ proxy_set_header X-Real-IP $remote_addr;
+ }
+
+ # Frontend (proxy to frontend container)
+ location / {
+ proxy_pass http://frontend/;
+ proxy_set_header Host $host;
+ proxy_set_header X-Real-IP $remote_addr;
+ proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+ }
+ }
+}
diff --git a/docs/changes.md b/docs/changes.md
new file mode 100644
index 0000000..9749f94
--- /dev/null
+++ b/docs/changes.md
@@ -0,0 +1,749 @@
+# Eioku Architecture Evolution
+
+This document chronicles the architectural journey of the Eioku video intelligence platform, including C4 diagrams, key trade-offs, and lessons learned.
+
+---
+
+## Table of Contents
+
+1. [Executive Summary](#executive-summary)
+2. [Architecture Phases](#architecture-phases)
+3. [C4 Diagrams](#c4-diagrams)
+4. [Key Trade-offs](#key-trade-offs)
+5. [Data Flow Evolution](#data-flow-evolution)
+6. [Lessons Learned](#lessons-learned)
+
+---
+
+## Executive Summary
+
+Eioku evolved from a monolithic video processing application to a distributed, GPU-aware microservices architecture over ~109 commits. The transformation addressed:
+
+- **Scalability**: Separating GPU-bound ML inference from API coordination
+- **Flexibility**: Unified artifact storage supporting multiple ML model versions
+- **Discoverability**: Cross-video search and navigation across entire video libraries
+- **Maintainability**: Domain-driven design with clear service boundaries
+
+---
+
+## Architecture Phases
+
+### Phase 0: Initial Monolith (Pre-Refactor)
+
+**Problem Statement**: Single backend service handling everything—API requests, ML inference, database operations, and video processing.
+
+```mermaid
+graph TB
+ subgraph Monolith["Monolithic Backend"]
+ API["FastAPI Server"]
+ ML["ML Inference
(YOLO, Whisper)"]
+ VP["Video Processing"]
+ DB_OPS["Database Operations"]
+ end
+
+ API --> ML
+ API --> VP
+ API --> DB_OPS
+
+ ML --> GPU["GPU (local)"]
+ DB_OPS --> SQLite["SQLite DB"]
+
+ style Monolith fill:#f9f,stroke:#333,stroke-width:2px
+```
+
+**Issues**:
+- ML inference blocked API requests
+- GPU memory contention
+- Difficult to scale horizontally
+- New ML models required new database tables
+- No unified artifact versioning
+- Cannot write the SQLite concurrently (multiple workers)
+- Individual database schemas resulted in sprawl in code to support each
+ data model (supporting different versions of ML models, or adding new types
+ of ML model responses becomes a migration nightmare)
+
+---
+
+### Phase 1: Artifact Envelope Architecture (PR #01 - 23 commits)
+
+**Goal**: Unified storage model for all ML outputs with schema versioning.
+
+```mermaid
+graph TB
+ subgraph Producers["ML Producers"]
+ Whisper["Whisper"]
+ YOLO["YOLO"]
+ Places["Places365"]
+ OCR["EasyOCR"]
+ end
+
+ Producers --> SR["Schema Registry
(Validates payloads)"]
+ SR --> AR["Artifact Repository
(CRUD + Selection)"]
+
+ AR --> Artifacts[(artifacts
JSONB payload)]
+ AR --> Projections[(projections
FTS, labels, clusters)]
+ AR --> Selections[(selections
policies)]
+
+ Artifacts -.->|sync| Projections
+
+ style SR fill:#bbf,stroke:#333
+ style AR fill:#bfb,stroke:#333
+```
+
+**Database Schema**:
+
+```mermaid
+erDiagram
+ ARTIFACTS {
+ uuid artifact_id PK
+ uuid asset_id FK
+ varchar artifact_type
+ int schema_version
+ int span_start_ms
+ int span_end_ms
+ jsonb payload_json
+ varchar producer
+ varchar model_profile
+ varchar config_hash
+ uuid run_id FK
+ timestamp created_at
+ }
+
+ OBJECT_LABELS {
+ uuid artifact_id PK
+ uuid asset_id FK
+ varchar label
+ real confidence
+ int start_ms
+ int end_ms
+ }
+
+ TRANSCRIPT_FTS {
+ uuid artifact_id PK
+ uuid asset_id FK
+ text text
+ tsvector text_tsv
+ int start_ms
+ int end_ms
+ }
+
+ VIDEOS ||--o{ ARTIFACTS : contains
+ ARTIFACTS ||--o| OBJECT_LABELS : projects
+ ARTIFACTS ||--o| TRANSCRIPT_FTS : projects
+```
+
+**Key Decisions**:
+
+| Decision | Options Considered | Chosen | Rationale |
+|----------|-------------------|--------|-----------|
+| Storage format | Separate tables per type vs JSONB | JSONB | Eliminates "new artifact tax", flexible schema evolution |
+| Projections | Views vs materialized tables | Materialized tables with triggers | Better query performance, FTS support |
+| Selection | Always latest vs configurable | Configurable policies | Supports pinning, profiles, experimentation |
+
+---
+
+### Phase 2: ML Service Separation (PR #02 - 27 commits)
+
+**Goal**: Extract GPU-bound ML inference into a dedicated service.
+
+```mermaid
+graph TB
+ subgraph API["API Service (Port 8000)"]
+ REST["REST Endpoints"]
+ TaskMgmt["Task Creation"]
+ JobEnqueue["Job Enqueueing"]
+ end
+
+ subgraph ML["ML Service (Port 8001)"]
+ Worker["arq Worker"]
+ ModelMgr["Model Manager"]
+ GPUSem["GPU Semaphore"]
+ end
+
+ REST --> TaskMgmt
+ TaskMgmt --> JobEnqueue
+ JobEnqueue -->|enqueue| Redis[(Redis
ml_jobs queue)]
+
+ Redis -->|consume| Worker
+ Worker --> ModelMgr
+ ModelMgr --> GPUSem
+ GPUSem --> GPU["GPU (CUDA)"]
+
+ Worker -->|write artifacts| Postgres[(PostgreSQL)]
+
+ API -->|read/write| Postgres
+
+ style API fill:#e1f5fe,stroke:#333
+ style ML fill:#fff3e0,stroke:#333
+```
+
+**Model Manager Design**:
+
+```mermaid
+classDiagram
+ class ModelManager {
+ +models: Dict
+ +gpu_semaphore: Semaphore
+ +infer(task_type, video_path, config)
+ +load_model(task_type)
+ +unload_model(task_type)
+ }
+
+ class ModelInfo {
+ +service: BaseModel
+ +status: str
+ +model_name: str
+ }
+
+ ModelManager --> ModelInfo : manages
+
+ class YOLO {
+ +detect(video_path)
+ }
+ class Whisper {
+ +transcribe(video_path)
+ }
+ class Places365 {
+ +classify(video_path)
+ }
+
+ ModelInfo --> YOLO
+ ModelInfo --> Whisper
+ ModelInfo --> Places365
+```
+
+---
+
+### Phase 3: Worker-ML Service Complete Separation (PR #03 - 34 commits)
+
+**Goal**: Full service isolation with reconciliation and task management.
+
+```mermaid
+graph TB
+ Client["External Clients"] --> API
+
+ subgraph API["API Service"]
+ VideoDisc["Video Discovery"]
+ TaskOrch["Task Orchestration"]
+ ArtifactQ["Artifact Queries"]
+ Reconciler["Reconciler (cron)"]
+ end
+
+ subgraph Storage["Data Layer"]
+ Postgres[(PostgreSQL
Source of Truth)]
+ Redis[(Redis/Valkey
Job Queues)]
+ end
+
+ subgraph MLService["ML Service"]
+ ARQWorker["arq Worker"]
+ Models["Model Manager"]
+ GPU["GPU (CUDA)"]
+ end
+
+ API <-->|read/write| Postgres
+ API -->|enqueue jobs| Redis
+
+ Redis -->|ml_jobs| ARQWorker
+ ARQWorker --> Models
+ Models --> GPU
+ ARQWorker -->|write artifacts| Postgres
+
+ Reconciler -->|sync state| Postgres
+ Reconciler -->|check jobs| Redis
+
+ style API fill:#e3f2fd,stroke:#333
+ style MLService fill:#fff8e1,stroke:#333
+ style Storage fill:#f3e5f5,stroke:#333
+```
+
+**Reconciliation Flow**:
+
+```mermaid
+flowchart TB
+ Start["Reconciler
(every 5 min)"] --> CheckPending
+
+ subgraph Pending["Check PENDING Tasks"]
+ CheckPending{Job in Redis?}
+ CheckPending -->|No| ReEnqueue1["Re-enqueue job"]
+ CheckPending -->|Yes| Skip1["Do nothing"]
+ end
+
+ subgraph Running["Check RUNNING Tasks"]
+ CheckRunning{Job status?}
+ CheckRunning -->|Not Found| Reset["Reset to PENDING
Re-enqueue"]
+ CheckRunning -->|Complete| SyncComplete["Sync to PostgreSQL"]
+ CheckRunning -->|Failed| SyncFailed["Mark FAILED"]
+ CheckRunning -->|In Progress| Skip2["Do nothing"]
+ end
+
+ subgraph LongRunning["Check Long-Running"]
+ CheckLong{Running > threshold?}
+ CheckLong -->|Yes| Alert["Send alert"]
+ CheckLong -->|No| Skip3["Do nothing"]
+ end
+
+ Pending --> Running
+ Running --> LongRunning
+```
+
+---
+
+### Phase 4: Frontend & Metadata Extraction (PR #04 - 25 commits)
+
+**Goal**: User-facing components and video metadata pipeline.
+
+```mermaid
+graph TB
+ subgraph Frontend["React Frontend"]
+ Gallery["VideoGallery"]
+ Player["VideoPlayer"]
+
+ subgraph Overlays["Overlays"]
+ ObjOverlay["ObjectDetectionOverlay"]
+ OCROverlay["OCROverlay"]
+ FaceOverlay["FaceOverlay"]
+ end
+
+ subgraph Viewers["Viewers"]
+ Transcript["TranscriptViewer"]
+ Metadata["MetadataViewer"]
+ Scene["SceneViewer"]
+ Task["TaskStatusViewer"]
+ end
+ end
+
+ Gallery --> Player
+ Player --> Overlays
+ Player --> Viewers
+
+ Player -->|API calls| Backend["API Service"]
+
+ style Frontend fill:#e8f5e9,stroke:#333
+```
+
+**Metadata Extraction Pipeline**:
+
+```mermaid
+flowchart LR
+ Video["Video File"] --> FFProbe["ffprobe"]
+ Video --> EXIF["EXIF Extraction"]
+
+ FFProbe --> Duration["duration
codec
resolution"]
+ EXIF --> FileDate["file_created_at
GPS coords"]
+
+ FileDate --> Geocode["Reverse Geocoding"]
+ Geocode --> Location["country
city
place name"]
+
+ Duration --> Artifact["MetadataV1
Artifact"]
+ FileDate --> Artifact
+ Location --> Artifact
+
+ Artifact --> Projection["video_locations
projection"]
+```
+
+---
+
+### Phase 5: Global Jump Navigation (Post-PR4)
+
+**Goal**: Cross-video search and navigation across entire video library.
+
+```mermaid
+flowchart TB
+ Request["GET /jump/global"] --> Controller["GlobalJumpController
Validate params"]
+
+ Controller --> Service["GlobalJumpService"]
+
+ Service --> Router{Route by kind}
+
+ Router -->|object| ObjSearch["_search_objects_global()
object_labels"]
+ Router -->|face| FaceSearch["_search_faces_global()
face_clusters"]
+ Router -->|transcript| TransSearch["_search_transcript_global()
transcript_fts"]
+ Router -->|ocr| OCRSearch["_search_ocr_global()
ocr_fts"]
+ Router -->|scene| SceneSearch["_search_scenes_global()
scene_ranges"]
+ Router -->|location| LocSearch["_search_locations_global()
video_locations"]
+
+ ObjSearch --> Timeline["Global Timeline Ordering"]
+ FaceSearch --> Timeline
+ TransSearch --> Timeline
+ OCRSearch --> Timeline
+ SceneSearch --> Timeline
+ LocSearch --> Timeline
+
+ Timeline --> Response["GlobalJumpResult"]
+```
+
+**Global Timeline Ordering**:
+
+```mermaid
+flowchart LR
+ subgraph Order["ORDER BY"]
+ O1["1. file_created_at"]
+ O2["2. video_id"]
+ O3["3. start_ms"]
+ end
+
+ O1 --> O2 --> O3
+
+ subgraph Example["Example: 'Next DOG'"]
+ V1["Video A
2024-01-01
dog @ 5000ms"]
+ V2["Video A
2024-01-01
dog @ 8000ms"]
+ V3["Video B
2024-01-02
dog @ 1000ms"]
+ end
+
+ V1 -->|next| V2 -->|next| V3
+```
+
+---
+
+## C4 Diagrams
+
+### Level 1: System Context
+
+```mermaid
+C4Context
+ title System Context Diagram for Eioku
+
+ Person(user, "Video Editor", "Browses and searches video library")
+
+ System(eioku, "Eioku Platform", "Video intelligence platform with ML-powered search and navigation")
+
+ System_Ext(files, "Video Files", "Filesystem storage")
+ System_Ext(models, "ML Models", "HuggingFace, Ultralytics")
+ System_Ext(apis, "External APIs", "Geocoding services")
+
+ Rel(user, eioku, "Uses", "HTTPS")
+ Rel(eioku, files, "Reads")
+ Rel(eioku, models, "Downloads")
+ Rel(eioku, apis, "Calls")
+```
+
+### Level 2: Container Diagram
+
+```mermaid
+C4Container
+ title Container Diagram for Eioku
+
+ Person(user, "User")
+
+ Container_Boundary(eioku, "Eioku Platform") {
+ Container(frontend, "Frontend", "React", "Video player, gallery, viewers")
+ Container(api, "API Service", "FastAPI", "REST API, task management, reconciler")
+ Container(ml, "ML Service", "arq worker", "YOLO, Whisper, Places365")
+
+ ContainerDb(postgres, "PostgreSQL", "Database", "Videos, tasks, artifacts, projections")
+ ContainerDb(redis, "Redis/Valkey", "Cache/Queue", "Job queues, result cache")
+ }
+
+ Rel(user, frontend, "Uses", "HTTPS")
+ Rel(frontend, api, "Calls", "REST API")
+ Rel(api, postgres, "Reads/Writes")
+ Rel(api, redis, "Enqueues jobs")
+ Rel(redis, ml, "Delivers jobs")
+ Rel(ml, postgres, "Writes artifacts directly")
+```
+
+### Level 3: Component Diagram - API Service
+
+```mermaid
+C4Component
+ title Component Diagram - API Service
+
+ Container_Boundary(api, "API Service") {
+ Component(video_ctrl, "VideoController", "FastAPI Router", "Video CRUD endpoints")
+ Component(task_ctrl, "TaskController", "FastAPI Router", "Task management endpoints")
+ Component(artifact_ctrl, "ArtifactController", "FastAPI Router", "Artifact query endpoints")
+ Component(jump_ctrl, "GlobalJumpController", "FastAPI Router", "Cross-video search")
+
+ Component(discovery, "VideoDiscoveryService", "Service", "Scans filesystem for videos")
+ Component(producer, "JobProducer", "Service", "Enqueues ML tasks")
+ Component(jump_svc, "GlobalJumpService", "Service", "Global timeline navigation")
+ Component(reconciler, "Reconciler", "Cron Job", "Syncs task state")
+ Component(projection, "ProjectionSyncService", "Service", "Updates projections")
+
+ Component(video_repo, "VideoRepository", "Repository", "Video persistence")
+ Component(artifact_repo, "ArtifactRepository", "Repository", "Artifact persistence")
+ Component(task_repo, "TaskRepository", "Repository", "Task persistence")
+ }
+
+ Rel(video_ctrl, discovery, "Uses")
+ Rel(task_ctrl, producer, "Uses")
+ Rel(jump_ctrl, jump_svc, "Uses")
+
+ Rel(discovery, video_repo, "Uses")
+ Rel(producer, task_repo, "Uses")
+ Rel(jump_svc, artifact_repo, "Uses")
+ Rel(reconciler, task_repo, "Uses")
+```
+
+### Level 3: Component Diagram - ML Service
+
+```mermaid
+C4Component
+ title Component Diagram - ML Service
+
+ Container_Boundary(ml, "ML Service") {
+ Component(worker, "arq Worker", "Worker", "Consumes from ml_jobs queue")
+ Component(handler, "TaskHandler", "Handler", "Routes to specific handlers")
+
+ Component(obj_handler, "ObjectDetectionHandler", "Handler", "YOLO object detection")
+ Component(face_handler, "FaceDetectionHandler", "Handler", "Face detection")
+ Component(trans_handler, "TranscriptionHandler", "Handler", "Whisper transcription")
+ Component(ocr_handler, "OCRHandler", "Handler", "EasyOCR text extraction")
+ Component(place_handler, "PlaceDetectionHandler", "Handler", "Places365 classification")
+ Component(scene_handler, "SceneDetectionHandler", "Handler", "PySceneDetect")
+ Component(meta_handler, "MetadataHandler", "Handler", "ffprobe + EXIF")
+
+ Component(model_mgr, "ModelManager", "Service", "Model lifecycle management")
+ Component(gpu_sem, "GPU Semaphore", "Concurrency", "Limits GPU operations")
+ }
+
+ Rel(worker, handler, "Dispatches to")
+ Rel(handler, obj_handler, "Routes")
+ Rel(handler, face_handler, "Routes")
+ Rel(handler, trans_handler, "Routes")
+ Rel(handler, ocr_handler, "Routes")
+ Rel(handler, place_handler, "Routes")
+ Rel(handler, scene_handler, "Routes")
+ Rel(handler, meta_handler, "Routes")
+
+ Rel(obj_handler, model_mgr, "Uses")
+ Rel(face_handler, model_mgr, "Uses")
+ Rel(model_mgr, gpu_sem, "Acquires")
+```
+
+---
+
+## Key Trade-offs
+
+### 1. JSONB vs Separate Tables for Artifacts
+
+```mermaid
+quadrantChart
+ title Storage Strategy Comparison
+ x-axis Low Flexibility --> High Flexibility
+ y-axis Low Performance --> High Performance
+ quadrant-1 Ideal
+ quadrant-2 Performance Focus
+ quadrant-3 Avoid
+ quadrant-4 Flexibility Focus
+
+ "Separate Tables": [0.3, 0.8]
+ "Pure JSONB": [0.9, 0.4]
+ "JSONB + Projections": [0.75, 0.75]
+```
+
+| Factor | JSONB (Chosen) | Separate Tables |
+|--------|---------------|-----------------|
+| **Flexibility** | High - schema changes don't require migrations | Low - each change needs ALTER TABLE |
+| **Query Performance** | Moderate - GIN indexes help | High - native indexing |
+| **New Artifact Tax** | None - just add schema version | High - new table, migrations, ORM models |
+| **Type Safety** | Runtime validation via Pydantic | Compile-time via ORM |
+
+**Decision**: JSONB with projection tables. Get flexibility of JSONB for canonical storage, performance of native tables for queries.
+
+### 2. Redis Queue vs HTTP for ML Service
+
+```mermaid
+flowchart LR
+ subgraph HTTP["HTTP Approach"]
+ A1["API"] -->|POST /infer| A2["ML Service"]
+ A2 -->|wait 30-120s| A1
+ A1 -->|timeout risk| X1["Connection Error"]
+ end
+
+ subgraph Queue["Queue Approach (Chosen)"]
+ B1["API"] -->|enqueue| B2["Redis"]
+ B2 -->|consume| B3["ML Service"]
+ B3 -->|write to| B4["PostgreSQL"]
+ end
+
+ style Queue fill:#e8f5e9
+ style HTTP fill:#ffebee
+```
+
+| Factor | Redis Queue (Chosen) | Direct HTTP |
+|--------|---------------------|-------------|
+| **Timeout Handling** | Natural - no HTTP timeout | Complex - needs long timeouts |
+| **Retry Logic** | Built into arq | Manual implementation |
+| **Backpressure** | Automatic queuing | Connection exhaustion |
+
+### 3. Projection Tables vs Materialized Views
+
+```mermaid
+flowchart TB
+ subgraph Projections["Projection Tables (Chosen)"]
+ P1["INSERT artifact"] --> P2["Trigger fires"]
+ P2 --> P3["INSERT into projection"]
+ P3 --> P4["Immediate consistency"]
+ end
+
+ subgraph MatViews["Materialized Views"]
+ M1["INSERT artifact"] --> M2["View stale"]
+ M2 --> M3["REFRESH MATERIALIZED VIEW"]
+ M3 --> M4["Eventually consistent"]
+ end
+
+ style Projections fill:#e8f5e9
+```
+
+| Factor | Projection Tables (Chosen) | Materialized Views |
+|--------|---------------------------|-------------------|
+| **Update Control** | Explicit via triggers | REFRESH command |
+| **Partial Updates** | Supported | Full refresh only |
+| **Staleness** | Real-time sync | Potentially stale |
+
+---
+
+## Data Flow Evolution
+
+### Before: Synchronous Processing
+
+```mermaid
+sequenceDiagram
+ participant Client
+ participant API
+ participant ML as ML Inference
+ participant DB as Database
+
+ Client->>API: POST /process
+ API->>ML: Run inference
+ Note over ML: 30-120 seconds blocking
+ ML->>API: Results
+ API->>DB: Save artifacts
+ DB->>API: OK
+ API->>Client: Response
+
+ Note over Client,DB: Total: 30-120 seconds waiting
+```
+
+### After: Async Queue-Based Processing
+
+```mermaid
+sequenceDiagram
+ participant Client
+ participant API
+ participant Redis
+ participant ML as ML Service
+ participant DB as PostgreSQL
+
+ Client->>API: POST /tasks
+ API->>DB: Create task (PENDING)
+ API->>Redis: Enqueue job ID
+ API->>Client: 202 Accepted (immediate)
+
+ Note over Client: Can poll for status
+
+ Redis->>ML: Deliver job
+ ML->>ML: Run inference
+ ML->>DB: Save artifacts directly
+ ML->>DB: Mark task COMPLETED
+
+ Client->>API: GET /tasks/{id}
+ API->>DB: Query task status
+ API->>Client: Status: COMPLETED
+```
+
+---
+
+## Lessons Learned
+
+### 1. Schema Versioning is Critical
+
+```mermaid
+flowchart LR
+ subgraph Before["Before"]
+ B1["artifact_v1"] --> B2["Breaking change"]
+ B2 --> B3["Migration nightmare"]
+ end
+
+ subgraph After["After"]
+ A1["artifact v1"] --> A2["Add v2 schema"]
+ A2 --> A3["Both coexist"]
+ A3 --> A4["Graceful migration"]
+ end
+
+ style Before fill:#ffebee
+ style After fill:#e8f5e9
+```
+
+### 2. PostgreSQL as Source of Truth
+
+```mermaid
+flowchart TB
+ subgraph Problem["Problem: Redis Crash"]
+ R1["Redis loses jobs"] --> R2["Tasks stuck RUNNING"]
+ R2 --> R3["Manual intervention"]
+ end
+
+ subgraph Solution["Solution: Reconciler"]
+ S1["PostgreSQL = truth"] --> S2["Reconciler checks Redis"]
+ S2 --> S3["Re-enqueue missing jobs"]
+ S3 --> S4["Automatic recovery"]
+ end
+
+ style Problem fill:#ffebee
+ style Solution fill:#e8f5e9
+```
+
+### 3. GPU Memory Management
+
+```mermaid
+flowchart TB
+ subgraph Problem["Problem"]
+ P1["Model A loads"] --> P2["Model B loads"]
+ P2 --> P3["OOM Error"]
+ end
+
+ subgraph Solution["Solution"]
+ S1["GPU Semaphore
(concurrency=2)"]
+ S2["Request 1"] --> S1
+ S3["Request 2"] --> S1
+ S4["Request 3"] -->|wait| S1
+ S1 --> S5["Controlled access"]
+ end
+
+ style Problem fill:#ffebee
+ style Solution fill:#e8f5e9
+```
+
+---
+
+## Future Considerations
+
+### Planned Enhancements
+
+```mermaid
+timeline
+ title Eioku Roadmap
+
+ Phase 6 : Embedding Search
+ : pgvector for similarity
+ : CLIP embeddings
+ : Face similarity
+
+ Phase 7 : Multi-Filter Queries
+ : AND/OR logic
+ : Combined searches
+
+ Phase 8 : Geo-Spatial
+ : PostGIS integration
+ : Location-based search
+
+ Phase 9 : Performance
+ : Materialized views
+ : Query caching
+ : Table partitioning
+```
+
+### Technical Debt
+
+1. Remove `backend/out.json` from git history (18MB debug artifact)
+2. Consolidate duplicate database models between services
+3. Add comprehensive integration tests for Reconciler
+4. Document API versioning strategy
+
+---
+
+*Generated: 2026-01-29*
+*Commits Analyzed: 109 (PR branches)*
+*Design Docs: .kiro/specs/*
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 50f2d17..11768ac 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -3,6 +3,7 @@ import VideoGallery from './components/VideoGallery';
import VideoPlayer from './components/VideoPlayer';
import SearchPage, { SearchNavigationState } from './pages/SearchPage';
import GalleryPage from './pages/GalleryPage';
+import { API_URL } from './config';
/**
* State passed from search page to player page.
@@ -82,6 +83,7 @@ function App() {
if (view === 'search') {
return (
@@ -104,6 +107,7 @@ function App() {
return (
-
+
);
}
diff --git a/frontend/src/components/TaskStatusViewer.tsx b/frontend/src/components/TaskStatusViewer.tsx
index 5e941ff..3ebf542 100644
--- a/frontend/src/components/TaskStatusViewer.tsx
+++ b/frontend/src/components/TaskStatusViewer.tsx
@@ -178,10 +178,11 @@ export default function TaskStatusViewer({ videoId, apiUrl = 'http://localhost:8
};
/**
- * Check if a task can be rerun (completed or failed, not pending/running).
+ * Check if a task can be retried (failed or cancelled only).
+ * Completed tasks cannot be retried - this matches the backend API.
*/
- const canRerun = (status: string) => {
- return status === 'completed' || status === 'failed';
+ const canRetry = (status: string) => {
+ return status === 'failed' || status === 'cancelled';
};
/**
@@ -269,7 +270,7 @@ export default function TaskStatusViewer({ videoId, apiUrl = 'http://localhost:8
{duration && ({duration})}
- {canRerun(task.status) && (
+ {canRetry(task.status) && (
diff --git a/frontend/src/vite-env.d.ts b/frontend/src/vite-env.d.ts
new file mode 100644
index 0000000..c57d674
--- /dev/null
+++ b/frontend/src/vite-env.d.ts
@@ -0,0 +1,9 @@
+///
+
+interface ImportMetaEnv {
+ readonly VITE_API_URL?: string;
+}
+
+interface ImportMeta {
+ readonly env: ImportMetaEnv;
+}
diff --git a/ml-service/poetry.cpu.lock b/ml-service/poetry.cpu.lock
new file mode 100644
index 0000000..f175d44
--- /dev/null
+++ b/ml-service/poetry.cpu.lock
@@ -0,0 +1,3930 @@
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+ Downloading poetry-1.7.1-py3-none-any.whl (235 kB)
+ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 236.0/236.0 kB 5.0 MB/s eta 0:00:00
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+Collecting build<2.0.0,>=1.0.3
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+Collecting packaging>=20.5
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+Collecting msgpack>=0.5.2
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+Collecting filelock>=3.8.0
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+Collecting rapidfuzz<4.0.0,>=3.0.0
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+Collecting urllib3>=1.25
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+Collecting jaraco.classes
+ Downloading jaraco.classes-3.4.0-py3-none-any.whl (6.8 kB)
+Collecting SecretStorage>=3.2
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+Collecting jeepney>=0.4.2
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+Collecting importlib-metadata>=4.11.4
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+Collecting ptyprocess>=0.5
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+Collecting idna<4,>=2.5
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+Collecting zipp>=3.20
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+Collecting cryptography>=2.0
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+Installing collected packages: trove-classifiers, ptyprocess, fastjsonschema, distlib, zipp, urllib3, typing-extensions, tomlkit, tomli, shellingham, rapidfuzz, pyproject-hooks, pycparser, poetry-core, platformdirs, pkginfo, pexpect, packaging, msgpack, more-itertools, jeepney, installer, idna, filelock, crashtest, charset_normalizer, certifi, virtualenv, requests, jaraco.classes, importlib-metadata, dulwich, cleo, cffi, build, requests-toolbelt, cryptography, cachecontrol, SecretStorage, keyring, poetry-plugin-export, poetry
+Successfully installed SecretStorage-3.5.0 build-1.4.0 cachecontrol-0.13.1 certifi-2026.1.4 cffi-2.0.0 charset_normalizer-3.4.4 cleo-2.1.0 crashtest-0.4.1 cryptography-46.0.4 distlib-0.4.0 dulwich-0.21.7 fastjsonschema-2.21.2 filelock-3.20.3 idna-3.11 importlib-metadata-8.7.1 installer-0.7.0 jaraco.classes-3.4.0 jeepney-0.9.0 keyring-24.3.1 more-itertools-10.8.0 msgpack-1.1.2 packaging-26.0 pexpect-4.9.0 pkginfo-1.12.1.2 platformdirs-3.11.0 poetry-1.7.1 poetry-core-1.8.1 poetry-plugin-export-1.6.0 ptyprocess-0.7.0 pycparser-3.0 pyproject-hooks-1.2.0 rapidfuzz-3.14.3 requests-2.32.5 requests-toolbelt-1.0.0 shellingham-1.5.4 tomli-2.4.0 tomlkit-0.14.0 trove-classifiers-2026.1.14.14 typing-extensions-4.15.0 urllib3-2.6.3 virtualenv-20.36.1 zipp-3.23.0
+WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
+
+[notice] A new release of pip is available: 23.0.1 -> 25.3
+[notice] To update, run: pip install --upgrade pip
+Creating virtualenv eioku-ml-service-9TtSrW0h-py3.10 in /root/.cache/pypoetry/virtualenvs
+The --no-wheel and --wheel options are deprecated. They have no effect for Python > 3.8 as wheel is no longer bundled in virtualenv.
+Updating dependencies
+Resolving dependencies...
+The lock file might not be compatible with the current version of Poetry.
+Upgrade Poetry to ensure the lock file is read properly or, alternatively, regenerate the lock file with the `poetry lock` command.
+
+Writing lock file
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "alembic"
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diff --git a/ml-service/poetry.lock b/ml-service/poetry.lock
index e7bc8ac..603c3ff 100644
--- a/ml-service/poetry.lock
+++ b/ml-service/poetry.lock
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optional = false
python-versions = ">=3.10"
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optional = false
python-versions = ">=3.7"
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python-versions = ">=3.8"
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cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
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description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
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python-versions = ">=3.9.0"
groups = ["main"]
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description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.8"
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python-versions = ">=3.9"
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- {file = "scipy-1.17.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5fb10d17e649e1446410895639f3385fd2bf4c3c7dfc9bea937bddcbc3d7b9ba"},
- {file = "scipy-1.17.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8547e7c57f932e7354a2319fab613981cde910631979f74c9b542bb167a8b9db"},
- {file = "scipy-1.17.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:33af70d040e8af9d5e7a38b5ed3b772adddd281e3062ff23fec49e49681c38cf"},
- {file = "scipy-1.17.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f9eb55bb97d00f8b7ab95cb64f873eb0bf54d9446264d9f3609130381233483f"},
- {file = "scipy-1.17.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1ff269abf702f6c7e67a4b7aad981d42871a11b9dd83c58d2d2ea624efbd1088"},
- {file = "scipy-1.17.0-cp314-cp314t-win_arm64.whl", hash = "sha256:031121914e295d9791319a1875444d55079885bbae5bdc9c5e0f2ee5f09d34ff"},
- {file = "scipy-1.17.0.tar.gz", hash = "sha256:2591060c8e648d8b96439e111ac41fd8342fdeff1876be2e19dea3fe8930454e"},
-]
-
-[package.dependencies]
-numpy = ">=1.26.4,<2.7"
-
-[package.extras]
-dev = ["click (<8.3.0)", "cython-lint (>=0.12.2)", "mypy (==1.10.0)", "pycodestyle", "ruff (>=0.12.0)", "spin", "types-psutil", "typing_extensions"]
-doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)", "tabulate"]
-test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
-
[[package]]
name = "setuptools"
version = "80.10.2"
@@ -3384,7 +3048,7 @@ version = "1.3.1"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main"]
files = [
{file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
{file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -3531,7 +3195,6 @@ description = "Read and write TIFF files"
optional = false
python-versions = ">=3.10"
groups = ["main"]
-markers = "python_version == \"3.10\""
files = [
{file = "tifffile-2025.5.10-py3-none-any.whl", hash = "sha256:e37147123c0542d67bc37ba5cdd67e12ea6fbe6e86c52bee037a9eb6a064e5ad"},
{file = "tifffile-2025.5.10.tar.gz", hash = "sha256:018335d34283aa3fd8c263bae5c3c2b661ebc45548fde31504016fcae7bf1103"},
@@ -3548,30 +3211,6 @@ test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "l
xml = ["defusedxml", "lxml"]
zarr = ["fsspec", "zarr (<3)"]
-[[package]]
-name = "tifffile"
-version = "2026.1.14"
-description = "Read and write TIFF files"
-optional = false
-python-versions = ">=3.11"
-groups = ["main"]
-markers = "python_version >= \"3.11\""
-files = [
- {file = "tifffile-2026.1.14-py3-none-any.whl", hash = "sha256:29cf4adb43562a4624fc959018ab1b44e0342015d3db4581b983fe40e05f5924"},
- {file = "tifffile-2026.1.14.tar.gz", hash = "sha256:a423c583e1eecd9ca255642d47f463efa8d7f2365a0e110eb0167570493e0c8c"},
-]
-
-[package.dependencies]
-numpy = "*"
-
-[package.extras]
-all = ["defusedxml", "fsspec", "imagecodecs (>=2025.11.11)", "kerchunk", "lxml", "matplotlib", "zarr (>=3.1.3)"]
-codecs = ["imagecodecs (>=2025.11.11)"]
-plot = ["matplotlib"]
-test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "kerchunk", "lfdfiles", "lxml", "ndtiff", "oiffile", "psdtags", "pytest", "requests", "roifile", "xarray", "zarr (>=3.1.3)"]
-xml = ["defusedxml", "lxml"]
-zarr = ["fsspec", "kerchunk", "zarr (>=3.1.3)"]
-
[[package]]
name = "tokenizers"
version = "0.22.2"
@@ -3863,14 +3502,14 @@ typing-extensions = ">=4.12.0"
[[package]]
name = "ultralytics"
-version = "8.4.8"
+version = "8.4.9"
description = "Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
- {file = "ultralytics-8.4.8-py3-none-any.whl", hash = "sha256:be0af706dbe0fe87939966a1a34e4b03e194d070bb28b6673a81fd317e6114b9"},
- {file = "ultralytics-8.4.8.tar.gz", hash = "sha256:9fa754c1eb61f204b0257d4f24dfb9ea94511cefd4da7b21ee97ee6081b1c46d"},
+ {file = "ultralytics-8.4.9-py3-none-any.whl", hash = "sha256:994bb7007da842374489eb7282814d5ee626904f186d570e76d90ae15f13b0fb"},
+ {file = "ultralytics-8.4.9.tar.gz", hash = "sha256:7fedde080f415acbe42afb1db569a887b829e79e0ba5f104a2be2f75bb2e8833"},
]
[package.dependencies]
@@ -3884,8 +3523,8 @@ pyyaml = ">=5.3.1"
requests = ">=2.23.0"
scipy = ">=1.4.1"
torch = [
- {version = ">=1.8.0,<2.4.0 || >2.4.0,<2.10", markers = "sys_platform == \"win32\""},
- {version = ">=1.8.0,<2.10", markers = "sys_platform != \"win32\""},
+ {version = ">=1.8.0,<2.4.0 || >2.4.0", markers = "sys_platform == \"win32\""},
+ {version = ">=1.8.0", markers = "sys_platform != \"win32\""},
]
torchvision = ">=0.9.0"
ultralytics-thop = ">=2.0.18"
@@ -4370,4 +4009,4 @@ files = [
[metadata]
lock-version = "2.1"
python-versions = "^3.10"
-content-hash = "140fbff4cdc73ea6c1de7ca84e48a10ac01df87ca083ee0e63fc91e65302ee61"
+content-hash = "4a4a582f65b3e9a69fb3623b825f7836c35a09e850e18aaab10e793c72e8f321"
diff --git a/ml-service/pyproject.cpu.toml b/ml-service/pyproject.cpu.toml
deleted file mode 100644
index 6ecfe7b..0000000
--- a/ml-service/pyproject.cpu.toml
+++ /dev/null
@@ -1,35 +0,0 @@
-[tool.poetry]
-name = "eioku-ml-service"
-version = "0.1.0"
-description = "Eioku ML Service - ML inference job consumer (CPU)"
-authors = ["Eioku Team "]
-packages = [{include = "src"}]
-
-[tool.poetry.dependencies]
-python = "^3.10"
-fastapi = "^0.104.1"
-uvicorn = {extras = ["standard"], version = "^0.24.0"}
-pydantic = "^2.0"
-ultralytics = "^8.0.0"
-faster-whisper = "^1.2.1"
-easyocr = "^1.7.0"
-opencv-python = "^4.8.0"
-numpy = "^1.24"
-pillow = "^10.0"
-# PyTorch CPU-only (no CUDA)
-torch = "^2.1"
-torchvision = "^0.16"
-arq = "^0.26"
-sqlalchemy = "^2.0"
-alembic = "^1.12.0"
-psycopg2-binary = "^2.9.9"
-python-json-logger = "^2.0.7"
-xxhash = "^3.4.1"
-redis = "^5.0.0"
-psutil = "^5.9.0"
-pyexiftool = "^0.5.5"
-geopy = "^2.4.0"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
diff --git a/ml-service/pyproject.toml b/ml-service/pyproject.toml
index 88119fc..fdbb304 100644
--- a/ml-service/pyproject.toml
+++ b/ml-service/pyproject.toml
@@ -1,9 +1,8 @@
[tool.poetry]
name = "eioku-ml-service"
version = "0.1.0"
-description = "Eioku ML Service - ML inference job consumer"
+description = "Eioku ML Service - ML inference job consumer (CPU)"
authors = ["Eioku Team "]
-readme = "README.md"
packages = [{include = "src"}]
[tool.poetry.dependencies]
@@ -11,47 +10,30 @@ python = "^3.10"
fastapi = "^0.104.1"
uvicorn = {extras = ["standard"], version = "^0.24.0"}
pydantic = "^2.0"
-# ML inference dependencies (same as backend)
ultralytics = "^8.0.0"
faster-whisper = "^1.2.1"
easyocr = "^1.7.0"
opencv-python = "^4.8.0"
numpy = "^1.24"
pillow = "^10.0"
-torch = {version = "^2.1", markers = "platform_system != 'Darwin'", extras = ["cu121"]}
-torchvision = {version = "^0.16", markers = "platform_system != 'Darwin'"}
-# Job queue
+# PyTorch CPU-only (no CUDA)
+torch = "^2.1"
+torchvision = "^0.16"
arq = "^0.26"
-# Database
sqlalchemy = "^2.0"
alembic = "^1.12.0"
psycopg2-binary = "^2.9.9"
python-json-logger = "^2.0.7"
-# Hashing
xxhash = "^3.4.1"
-# Redis
redis = "^5.0.0"
-# System monitoring
psutil = "^5.9.0"
-nvidia-ml-py = "^12.535.108"
-# Metadata extraction
pyexiftool = "^0.5.5"
geopy = "^2.4.0"
[tool.poetry.group.dev.dependencies]
-ruff = "^0.1.6"
-pytest = "^7.4.3"
-pytest-asyncio = "^0.21.1"
-httpx = "^0.25.2"
+pytest = "^7.4.0"
+ruff = "^0.1.15"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
-
-[tool.ruff]
-line-length = 88
-target-version = "py310"
-
-[tool.ruff.lint]
-select = ["E", "W", "F", "I", "N", "UP"]
-ignore = []
diff --git a/ml-service/src/workers/task_handler.py b/ml-service/src/workers/task_handler.py
index 508ec43..ef89413 100644
--- a/ml-service/src/workers/task_handler.py
+++ b/ml-service/src/workers/task_handler.py
@@ -83,7 +83,7 @@ async def process_ml_task(
model_cache_dir = os.getenv("MODEL_CACHE_DIR", "/models")
os.environ["HF_HOME"] = os.path.join(model_cache_dir, "huggingface")
os.environ["YOLO_HOME"] = os.path.join(model_cache_dir, "ultralytics")
- os.environ["EASYOCR_HOME"] = os.path.join(model_cache_dir, "easyocr")
+ os.environ["EASYOCR_MODULE_PATH"] = os.path.join(model_cache_dir, "easyocr")
model_manager = ModelManager(cache_dir=model_cache_dir)
logger.info(f"✅ Model manager initialized for task {task_id}")
diff --git a/test-videos/.gitkeep b/test-videos/.gitkeep
new file mode 100644
index 0000000..e69de29