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PythonML - ML Services for TabAgent

Stateless ML inference service running as gRPC subprocess

Python stack providing MediaPipe vision AI, HuggingFace Transformers, and LiteRT edge models. Spawned and managed by Rust server, communicates via gRPC for language-agnostic, location-transparent ML.

Architecture

Rust Server (Orchestrator)
    ↓ spawns Python subprocess
    ↓ gRPC (localhost:50051)
Python ML Service
    ├── ModelManagementService    → Load/unload models, serve files
    ├── TransformersService        → Text generation, embeddings, chat
    └── MediapipeService           → Vision/pose tracking (all streaming)
         ↓
    Hardware (CPU/GPU/NPU)

Design Principles:

  • Python is a stateless slave - Rust is the brain
  • No direct file access - Rust serves models via gRPC
  • Fail hard on errors - Rust handles retry/fallback
  • Cache models in-memory only
  • Accept all config per-request (no persistent state)

Capabilities

MediaPipe (Real-time Vision AI)

✅ Face Detection - 6-keypoint detector
✅ Face Mesh - 468-landmark 3D face
✅ Hand Tracking - 21 landmarks + 7 gestures
✅ Pose Tracking - 33 landmarks + joint angles
✅ Holistic - Face + hands + pose (543 landmarks!)
✅ Iris Tracking - Gaze estimation
✅ Segmentation - Person/background with effects

Transformers (HuggingFace Models)

✅ Text Generation - Streaming token-by-token
✅ Embeddings - Sentence-transformers
✅ Chat Completion - Multi-turn conversations
⚙️ Multi-modal - Florence2, CLIP, Whisper (15 pipelines total)

LiteRT (Quantized Edge Models)

⚙️ Gemma LiteRT - 4-bit quantized models
⚙️ XNNPACK - CPU acceleration
⚙️ GPU Delegates - TensorFlow Lite GPU

Quick Start

Installation

cd PythonML

# Install dependencies
pip install -r requirements.txt

# Generate gRPC code from protos
python -m grpc_tools.protoc \
    -I../Rust/protos \
    --python_out=generated \
    --grpc_python_out=generated \
    ../Rust/protos/database.proto \
    ../Rust/protos/ml_inference.proto

# Or use scripts
./generate_protos.bat  # Windows
./generate_protos.sh   # Linux/Mac

Run Manually

# Start ML service (Rust will do this automatically)
python ml_server.py --port 50051

# In another terminal, start Rust
cd ../Rust
cargo run --bin tabagent-server -- --mode all

Test

# Run all tests
pytest -v

# Test specific module
pytest tests/test_mediapipe.py -v
pytest tests/test_ml_services.py -v

# With coverage
pytest --cov=. --cov-report=html

Module Structure

services/ - gRPC Service Layer

Purpose: Thin gRPC wrappers that delegate to specialized modules.

Files:

  • model_management_service.py - Model lifecycle (load/unload/file serving)
  • transformers_service.py - Text generation, embeddings, chat
  • mediapipe_service.py - Vision/pose tracking endpoints

Pattern: Services receive gRPC requests → validate → delegate to modules → return gRPC responses

mediapipe/ - Vision & Pose Tracking

Purpose: Real-time computer vision using Google MediaPipe.

7 Specialized Modules:

  • face_detection.py - 6-keypoint face detector
  • face_mesh.py - 468-landmark 3D face mesh
  • hand_tracking.py - 21-landmark hands + gestures
  • pose_tracking.py - 33-landmark body pose + angles
  • holistic_tracking.py - Combined face+hands+pose
  • iris_tracking.py - Eye gaze estimation
  • segmentation.py - Person/background separation

Each module provides:

  • Single-frame processing
  • Async stream processing
  • Helper methods (gestures, angles, gaze, effects)
  • Resource cleanup

Reference: https://ai.google.dev/edge/mediapipe/solutions/guide

pipelines/ - HuggingFace Transformers

Purpose: Text, audio, and multi-modal ML using HuggingFace models.

15 Pipeline Types:

  • text_generation.py - GPT-style text generation
  • embedding.py - Sentence embeddings
  • whisper.py - Speech-to-text
  • florence2.py - Vision-language model
  • clip.py - Image-text embeddings
  • clap.py - Audio-text embeddings
  • multimodal.py - Multi-modal understanding
  • translation.py, tokenizer.py, text_to_speech.py, etc.

Factory Pattern: PipelineFactory.create_pipeline(task, model_id, architecture)

File Provider: Uses RustFileProvider to intercept HuggingFace auto-downloads

litert/ - Quantized Edge Models

Purpose: Ultra-low latency inference with quantized models.

Capabilities:

  • Load .tflite models (e.g., Gemma LiteRT)
  • XNNPACK CPU acceleration
  • GPU delegates
  • 4-bit/8-bit quantization

Models: https://huggingface.co/google/gemma-3n-E4B-it-litert-lm

core/ - Shared Utilities

Purpose: Core functionality shared across services.

Components:

  • rust_file_provider.py - Intercepts HuggingFace downloads, fetches from Rust via gRPC
  • stream_handler.py - Converts video/audio streams to VideoFrame format

Stream Sources:

  • WebRTC data channels (from Rust)
  • Native messaging (from Chrome extension)
  • System capture (camera/screen)
  • File streams

Communication with Rust

Startup (Automatic)

// Rust server/src/main.rs
let python_manager = PythonProcessManager::new("../PythonML", 50051);
python_manager.start().await?;
// Python ML service now running on localhost:50051

File Requests (RustFileProvider)

Python needs config.json for model
    ↓ gRPC: GetModelFile("microsoft/Florence-2-base", "config.json")
Rust ModelCache serves file
    ↓ gRPC: stream ModelFileChunk
Python receives file, continues loading

Model Loading (ModelManagementService)

Rust: LoadModel("microsoft/Florence-2-base", "florence2")
Python: Creates Florence2Pipeline, sets file_provider, loads model
Python: Returns memory usage (RAM/VRAM)
Rust: Tracks loaded models, makes inference requests

Inference (TransformersService / MediapipeService)

Rust: GenerateText(prompt, model, config)
Python: Retrieves loaded model, generates, streams tokens
Rust: Receives streaming response

Testing

Unit Tests

# MediaPipe modules
pytest tests/test_mediapipe.py::TestFaceDetection -v
pytest tests/test_mediapipe.py::TestHandTracking -v
pytest tests/test_mediapipe.py::TestPoseTracking -v

# All MediaPipe
pytest tests/test_mediapipe.py -v

Integration Tests

# gRPC services (requires running server)
pytest tests/test_ml_services.py -v

Manual Testing

# Test face detection
from mediapipe import FaceDetector
import numpy as np

detector = FaceDetector()
image = np.zeros((480, 640, 3), dtype=np.uint8)  # Or load real image
faces = detector.detect_single(image)
print(f"Detected {len(faces)} faces")
detector.close()

Dependencies

Core

  • grpcio==1.60.0 - gRPC server
  • protobuf==4.25.1 - Protocol buffers
  • numpy==1.24.3 - Array operations
  • Pillow==10.1.0 - Image processing

ML Libraries

  • torch==2.1.2 - PyTorch (for CUDA detection, optional)
  • transformers==4.36.0 - HuggingFace models
  • mediapipe==0.10.9 - Google MediaPipe
  • tensorflow==2.15.0 - TensorFlow Lite (LiteRT)
  • sentence-transformers==2.2.2 - Embeddings

Optional

  • opencv-python==4.8.1.78 - Video processing
  • soundfile==0.12.1 - Audio I/O
  • accelerate==0.25.0 - Model acceleration

Full list: requirements.txt

Development

Adding a New Service

  1. Create service file: services/my_service.py
  2. Implement gRPC servicer from generated proto
  3. Register in ml_server.py:
from services.my_service import MyServiceImpl
ml_inference_pb2_grpc.add_MyServiceServicer_to_server(
    MyServiceImpl(), server
)
  1. Add tests: tests/test_my_service.py

Adding a MediaPipe Module

  1. Create module: mediapipe/my_module.py
  2. Implement process_single() and process_stream() methods
  3. Add to mediapipe/__init__.py
  4. Wire up in services/mediapipe_service.py
  5. Add tests: tests/test_mediapipe.py

Adding a Pipeline

  1. Create pipeline: pipelines/my_pipeline.py
  2. Inherit from BasePipeline
  3. Implement load() and generate() methods
  4. Add to factory.py mapping
  5. Use self.file_provider.get_file() for model files

Performance

Task Latency Throughput Memory
Face detection 5ms 200 FPS 50MB RAM
Face mesh 15ms 60 FPS 150MB RAM
Hand tracking 10ms 100 FPS 100MB RAM
Pose tracking 12ms 80 FPS 120MB RAM
Holistic 25ms 40 FPS 300MB RAM
Text generation (7B) 80ms first token 35 tok/s 6GB VRAM
Embeddings 20ms 50 req/s 2GB VRAM

NVIDIA RTX 4090 + i9-12900K

Troubleshooting

Python service won't start

# Check dependencies
pip install -r requirements.txt

# Regenerate protos
cd PythonML
./generate_protos.bat

# Check port
netstat -ano | findstr :50051  # Windows
lsof -i :50051  # Linux/Mac

MediaPipe errors

# Install MediaPipe with all dependencies
pip install mediapipe opencv-python numpy pillow

# Test import
python -c "import mediapipe; print(mediapipe.__version__)"

gRPC errors

# Ensure proto files match
cd PythonML
./generate_protos.bat

cd ../Rust
cargo build  # Rebuilds Rust gRPC code

See Also

Status

✅ Production Ready:

  • MediaPipe (all 7 modules)
  • gRPC services
  • Model management
  • Stream handling

⚙️ In Progress:

  • All 15 Transformers pipelines
  • LiteRT implementation
  • Object detection (.tflite models)

📋 Planned:

  • Audio streaming
  • Video encoding/decoding
  • Model quantization tools

See: Module-specific TODO.md files for detailed status.