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\n\n![Gesture Control System](logo/logo.jpg)\n\n# Gesture Control System\n\n**Real-time hand gesture recognition for controlling Blender's 3D viewport**\n\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![MediaPipe](https://img.shields.io/badge/MediaPipe-0.10+-green.svg)](https://google.github.io/mediapipe/)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](https://github.com)\n\n
\n\n---\n\n## Overview\n\nA production-ready gesture recognition system that enables intuitive control of Blender's 3D viewport through hand gestures. Built with MediaPipe for robust hand tracking and featuring platform-specific optimizations for macOS, Linux, and Windows.\n\n### Core Features\n\n- 🎯 **4 Core Gestures** - Pinch, V-gesture, Open Palm, Closed Fist\n- 🔄 **Viewport Control** - Rotate and pan Blender's 3D viewport naturally\n- 🎬 **Animation Control** - Play/stop timeline with hand gestures\n- 🖥️ **Cross-Platform** - Optimized for macOS (main-thread), Linux/Windows (threaded)\n- ⚡ **Low Latency** - Real-time processing with smoothing filters\n- 🎛️ **Configurable** - YAML-based configuration for sensitivity and mappings\n\n---\n\n## Quick Start\n\n### Prerequisites\n\n```bash\n# Python 3.8 or higher\npython --version\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Running the System\n\n**1. Start the gesture engine:**\n\n```bash\npython main_orchestrator.py --config config/blender_mode.yaml --debug\n```\n\n**2. In Blender:**\n- Install the addon from `blender_addon/gesture_control_addon.py`\n- Enable \"Gesture Control Center\" in Preferences → Add-ons\n- Open the sidebar (N key) → Gesture tab\n- Click **\"Connect Only\"** to link with the running engine\n\n**3. Control Blender with gestures!**\n\n---\n\n## Gestures\n\nThe system recognizes 4 core hand gestures for Blender control:\n\n| Gesture | Description | Action |\n|---------|-------------|--------|\n| 🤏 **Pinch** | Thumb + index finger touching | **Rotate viewport** - Move hand while pinching to orbit camera |\n| ✌️ **V-Gesture** | Index + middle fingers extended | **Pan viewport** - Move hand to pan camera position |\n| 🖐️ **Open Palm** | All fingers extended | **Play animation** - Start timeline playback |\n| ✊ **Closed Fist** | All fingers closed | **Stop animation** - Pause timeline |\n\n### Gesture Details\n\n**Pinch (Rotation Mode)**\n- Pinch thumb and index finger together\n- Move your hand to rotate the viewport\n- Automatic orbit around scene center\n- Release to exit rotation mode\n\n**V-Gesture (Navigation Mode)**\n- Extend index and middle fingers (peace sign)\n- Keep ring and pinky fingers closed\n- Move your hand to pan the viewport\n- Release to exit navigation mode\n\n**Animation Control**\n- Open palm to start playback\n- Closed fist to stop\n- Works independently of viewport modes\n\n---\n\n## Architecture\n\n```\n┌─────────────┐\n│ Camera │\n└──────┬──────┘\n │\n ▼\n┌─────────────────┐\n│ MediaPipe │\n│ Hand Tracking │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Gesture │\n│ Detector │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Filters & │\n│ Validators │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Event Bus │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Gesture │\n│ Handlers │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Blender Output │\n│ (Socket) │\n└──────┬──────────┘\n │\n ▼\n┌─────────────────┐\n│ Blender Addon │\n│ (Viewport) │\n└─────────────────┘\n```\n\n### Key Components\n\n**Gesture Detection** (`gestures/`)\n- MediaPipe-based hand landmark tracking\n- Confidence validation and quality checks\n- Smoothing filters for stable detection\n\n**Event System** (`core/`)\n- Central EventBus for message routing\n- Type-safe event handling\n- Modular handler registration\n\n**Handlers** (`handlers/`)\n- `blender_viewport_handler.py` - Viewport rotation and panning\n- `blender_animation_handler.py` - Timeline control\n- Configurable sensitivity and behavior\n\n**Blender Integration** (`blender_addon/`)\n- Socket-based communication (port 8888)\n- Real-time viewport manipulation\n- Simplified rotation and pan functions\n\n---\n\n## Configuration\n\nEdit `config/blender_mode.yaml` to customize behavior:\n\n```yaml\n# Gesture detection settings\ninputs:\n gesture:\n enabled: true\n camera_index: 0\n show_preview: true\n min_confidence: 0.6\n filter_window: 3\n\n# Blender output\noutputs:\n blender:\n enabled: true\n host: localhost\n port: 8888\n```\n\n### Sensitivity Tuning\n\nIn the Blender addon panel, adjust:\n- **Rotation Sensitivity** - Controls viewport rotation speed (default: 0.5)\n- **Pan Sensitivity** - Controls viewport panning speed (default: 0.1)\n\n---\n\n## Project Structure\n\n```\nlauzhack2025/\n├── config/ # YAML configuration files\n├── core/ # Event system and orchestration\n│ ├── event_system.py # EventBus implementation\n│ ├── gesture_handler.py # Handler base classes\n│ └── launcher.py # Application launcher\n├── gestures/ # Gesture recognition\n│ ├── detector.py # Main detection engine\n│ ├── filters.py # Smoothing filters\n│ ├── validators.py # Quality validation\n│ └── library/\n│ └── navigation.py # Core gesture definitions\n├── handlers/ # Gesture handlers\n│ ├── blender_viewport_handler.py\n│ └── blender_animation_handler.py\n├── inputs/ # Input modules\n│ └── gesture_input_production.py\n├── outputs/ # Output modules\n│ └── blender_output.py # Blender socket communication\n├── blender_addon/ # Blender addon\n│ └── gesture_control_addon.py\n├── main_orchestrator.py # Main entry point\n└── requirements.txt # Python dependencies\n```\n\n---\n\n## Platform Support\n\n### macOS\n- **Main-thread camera mode** - Required for camera permissions\n- Camera window displays in foreground\n- Launched via Terminal.app for proper access\n\n### Linux / Windows\n- **Threaded camera mode** - Background processing\n- Standard OpenCV camera access\n- Preview window optional\n\nThe system automatically detects your platform and uses the appropriate mode.\n\n---\n\n## Troubleshooting\n\n**Camera doesn't open?**\n- Check camera permissions in System Preferences (macOS)\n- Try a different camera: `--camera-index 1`\n- Ensure no other app is using the camera\n\n**Gestures not detected?**\n- Improve lighting conditions\n- Position hand clearly in frame\n- Adjust `min_confidence` in config (lower = more sensitive)\n- Check debug output with `--debug` flag\n\n**Blender not responding?**\n- Verify addon is installed and enabled\n- Check port 8888 is available: `lsof -i :8888`\n- Ensure \"Connect Only\" button was clicked in Blender\n- Check Blender's system console for errors\n\n**Viewport movement too fast/slow?**\n- Adjust sensitivity in Blender addon panel\n- Modify `sensitivity` in handler config\n- Fine-tune in real-time without restarting\n\n---\n\n## Development\n\n### Testing\n\n```bash\n# Run all tests\npython -m pytest tests/ -v\n\n# Test specific components\npython -m pytest tests/test_gestures.py\npython -m pytest tests/test_handler_system.py\n```\n\n### Adding Custom Gestures\n\n1. Define gesture in `gestures/library/navigation.py`:\n```python\n@register(\"navigation\")\nclass MyGesture(Gesture):\n @property\n def name(self) -> str:\n return \"MY_GESTURE\"\n \n def detect(self, landmarks, context):\n # Detection logic\n return GestureResult(name=self.name, confidence=0.9)\n```\n\n2. Add handler in `handlers/`\n3. Configure mapping in YAML\n\n---\n\n## Technical Details\n\n**Gesture Detection Pipeline:**\n1. MediaPipe extracts hand landmarks (21 points per hand)\n2. Landmarks filtered through smoothing window (reduces jitter)\n3. Quality validator checks landmark visibility and confidence\n4. Gesture detector matches against registered patterns\n5. Confidence validator ensures stable detection\n6. Event published to EventBus\n7. Handlers process and route to outputs\n\n**Movement Tracking:**\n- Pinch: Tracks midpoint of thumb/index, calculates deltas\n- V-Gesture: Tracks midpoint of index/middle, applies smoothing\n- Sensitivity multipliers: Rotation (20x), Navigation (150x)\n- Deadzone filtering to ignore micro-movements\n\n---\n\n## Requirements\n\n```\nmediapipe>=0.10.0\nopencv-python>=4.8.0\nPyYAML>=6.0\nnumpy>=1.24.0\n```\n\n---\n\n## License\n\nMIT License - See LICENSE file for details.\n\n---\n\n
\n\n**Built for LauzHack 2025**\n\nMade with ❤️ by the gesture control team\n\n
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\n \"ALIANTE\n

\n\n**A**utomated **L**andscape **I**mage **A**nalysis with **N**eural **T**echnology and **E**cosystems\n\nA distributed system for automated biodiversity analysis of drone-captured images using Vision Language Models (VLMs) and image segmentation. The platform integrates drone simulation, image storage, AI-powered analysis, and a web-based interface for ecosystem assessment.\n\n## Overview\n\nALIANTE is a microservices-based platform that enables automated analysis of aerial imagery for biodiversity assessment. The system combines:\n\n- **Drone Simulation**: ArduPilot SITL (Software In The Loop) for realistic drone flight simulation\n- **Image Processing**: Automated image storage and retrieval with support for segmentation metadata\n- **AI Analysis**: OpenAI-powered vision models for biodiversity assessment with SAM3 integration\n- **Clustering**: Post-processing service for grouping similar image segments\n- **Web Interface**: React-based frontend for uploading images and viewing analysis results\n- **MCP Integration**: Model Context Protocol server for exposing services to AI agents\n\n## Architecture\n\nThe system is composed of several microservices orchestrated via Docker Compose:\n\n```\n┌─────────────────┐\n│ Web Frontend │ (React + Vite)\n│ (Port 3000) │\n└────────┬────────┘\n │\n ▼\n┌─────────────────┐\n│ Tree Analyzer │ (Node.js + Express)\n│ Service │\n│ (Port 4000) │◄─── OpenAI API\n└────────┬────────┘\n │\n ├──► Image Storage Service (Port 4100)\n │\n └──► Clustering Service (Port 5051)\n │\n └──► SAM3 Segmentation (External MCP)\n │\n┌────────┴────────┐\n│ Drone Simulator│ (ArduPilot SITL)\n│ + Servient │\n│ (Port 19080) │\n└─────────────────┘\n │\n └──► Zion TDD (Port 28081)\n```\n\n### Core Services\n\n#### 1. **Drone Simulator** (`drone/`)\n- ArduPilot SITL environment for drone flight simulation\n- MAVLink communication via mavlink2rest\n- Configurable home position, vehicle type, and simulation parameters\n\n#### 2. **Drone Node Servient** (`servients/drone-node/`)\n- Node-WoT (Web of Things) servient exposing drone as a Thing\n- RESTful API for drone control (arm, takeoff, goto, land, etc.)\n- Real-time telemetry (position, battery, mode, armed status)\n- Integration with Zion TDD for Thing Description registration\n\n#### 3. **Image Storage Service** (`image-storage-service/`)\n- REST API for image upload and retrieval\n- Multer-based file handling with UUID-based naming\n- Public URL generation for hosted images\n- Health check endpoint\n\n#### 4. **Tree Analyzer Service** (`tree-analyzer-service/`)\n- Biodiversity analysis using OpenAI Vision API\n- SAM3 segmentation integration via MCP\n- Aggregates visual analysis with segmentation metadata\n- Forwards segmentation data to clustering service\n\n#### 5. **Clustering Service** (`postprocessing/`)\n- Flask-based service for image segment clustering\n- Uses SigLIP embeddings and Agglomerative Clustering\n- Processes SAM3 segmentation results\n- Groups similar image crops for pattern analysis\n\n

\n \"Clustering\n
\n High-level clustering summary showing inter-species relationships.\n

\n\n

\n \"Clustered\n \"Clustered\n \"Clustered\n

\n

\n \"Clustered\n \"Clustered\n \"Clustered\n

\n\n#### 6. **Web Frontend** (`tree-analyzer-web/`)\n- React application with Vite\n- Image upload interface\n- Customizable analysis prompts\n- Results visualization with observations and recommendations\n- SAM3 segmentation metadata display\n\n#### 7. **Biodiversity MCP Server** (`mcp/biodiversity-mcp/`)\n- Model Context Protocol server exposing services as tools\n- Tools: `upload_drone_image`, `analyze_biodiversity_image`, `list_hosted_images`\n- Compatible with ChatGPT, Claude, and other MCP clients\n\n#### 8. **Zion TDD** (External)\n- Thing Description Directory for WoT device registration\n- PostgreSQL-backed storage\n- JWT-based authentication\n\n## Features\n\n- **Drone Control**: Full flight control via WoT servient (arm, takeoff, waypoint navigation, landing)\n- **Image Analysis**: AI-powered biodiversity assessment with customizable prompts\n- **Segmentation**: SAM3 integration for object detection and segmentation\n- **Clustering**: Automatic grouping of similar image segments\n- **Web Interface**: User-friendly React frontend for image upload and analysis\n- **MCP Integration**: Expose services to AI agents via Model Context Protocol\n- **Docker Compose**: One-command deployment of all services\n- **Health Monitoring**: Health check endpoints for service status\n\n## Prerequisites\n\n- **Docker** and **Docker Compose** (v2.0+)\n- **Node.js** 20+ (for local development)\n- **Python** 3.9+ (for local development)\n- **OpenAI API Key** (for image analysis)\n- **SAM3 MCP Server** (optional, for segmentation)\n\n## Installation\n\n1. **Clone the repository**:\n ```bash\n git clone \n cd ALIANTE\n ```\n\n2. **Set environment variables**:\n Create a `.env` file in the root directory:\n ```env\n OPENAI_API_KEY=your_openai_api_key_here\n OPENAI_MODEL=gpt-4o-mini\n SAM3_MCP_URL=https://lauz.lab.students.cs.unibo.it/gradio_api/mcp/sse\n IMAGE_PUBLIC_BASE_URL=http://127.0.0.1:4100\n ```\n\n3. **Start all services**:\n ```bash\n docker-compose up -d\n ```\n\n4. **Verify services are running**:\n ```bash\n docker-compose ps\n ```\n\n## Configuration\n\n### Service Ports\n\n| Service | Port | Description |\n|--------|------|-------------|\n| Web Frontend | 3000 | React application |\n| Tree Analyzer | 4000 | Analysis API |\n| Image Storage | 4100 | Image upload/retrieval |\n| Clustering | 5051 | Post-processing service |\n| Drone Servient | 19080 | WoT API |\n| Zion TDD | 28081 | Thing Directory |\n| MAVProxy | 5770 | Drone telemetry |\n\n### Environment Variables\n\n#### Tree Analyzer Service\n- `OPENAI_API_KEY`: Required. Your OpenAI API key\n- `OPENAI_MODEL`: Optional. Model to use (default: `gpt-4o-mini`)\n- `SAM3_MCP_URL`: Optional. SAM3 MCP server endpoint\n- `CLUSTERING_URL`: Optional. Clustering service URL (default: `http://clustering:5051/receive`)\n\n#### Image Storage Service\n- `PORT`: Optional. Service port (default: `4100`)\n- `STORAGE_DIR`: Optional. Storage directory (default: `uploads` for local dev, `/data/uploads` in Docker)\n- `PUBLIC_BASE_URL`: Optional. Public base URL for images\n\n#### Drone Node Servient\n- `DRONE_NAME`: Optional. Drone identifier (default: `drone`)\n- `SYSID`: Optional. MAVLink system ID (default: `1`)\n- `WOT_PORT`: Optional. WoT server port (default: `8080`)\n- `M2R_BASE`: Optional. mavlink2rest base URL\n- `TDD_URL`: Optional. Zion TDD URL (default: `http://zion:3000`)\n\n## Usage\n\n### Web Interface\n\n1. Open your browser to `http://localhost:3000`\n2. Click \"Choose drone snapshot\" to upload an image\n3. Optionally modify the biodiversity analysis prompt\n4. Click \"Upload & Analyze\" to start the analysis\n5. View results including:\n - Focus area\n - Summary\n - Observations list\n - Recommended actions\n - SAM3 segmentation metadata\n\n### API Endpoints\n\n#### Image Storage Service\n\n**Upload Image**:\n```bash\nPOST /api/images\nContent-Type: multipart/form-data\n\nForm data:\n image: \n```\n\n**List Images**:\n```bash\nGET /api/images\n```\n\n**Health Check**:\n```bash\nGET /api/health\n```\n\n#### Tree Analyzer Service\n\n**Analyze Biodiversity**:\n```bash\nPOST /api/biodiversity\nContent-Type: application/json\n\n{\n \"imageUrl\": \"http://localhost:4100/images/1234567890-abc.jpg\",\n \"prompt\": \"Assess biodiversity of the forest canopy and highlight key species.\"\n}\n```\n\n**Health Check**:\n```bash\nGET /api/health\n```\n\n#### Drone Servient (WoT)\n\nThe drone is exposed as a WoT Thing. The Thing title is `${DRONE_NAME}-thing` (default: `drone1-thing` when `DRONE_NAME=drone1`).\n\nAccess the Thing Description and interact with the Thing at:\n```\nhttp://localhost:19080/drone1-thing\n```\n\n**Example Actions**:\n- `POST /drone1-thing/actions/arm`\n- `POST /drone1-thing/actions/takeoff` with `{\"alt\": 10}`\n- `POST /drone1-thing/actions/goto` with `{\"lat\": 44.494, \"lon\": 11.342, \"alt\": 20}`\n- `POST /drone1-thing/actions/land`\n\n**Properties**:\n- `GET /drone1-thing/properties/armed`\n- `GET /drone1-thing/properties/position`\n- `GET /drone1-thing/properties/battery`\n\nNote: The exact path may vary depending on Node-WoT configuration. Check the servient logs for the actual Thing URL.\n\n### MCP Server\n\nThe MCP server exposes three tools:\n\n1. **`upload_drone_image`**: Upload a base64-encoded image\n2. **`analyze_biodiversity_image`**: Analyze an image with a custom prompt\n3. **`list_hosted_images`**: List all stored images\n\nConnect to the MCP server at `http://localhost:3030/mcp` from compatible clients (ChatGPT, Claude, VS Code MCP, etc.).\n\n## Development\n\n### Local Development\n\n#### Backend Services\n\n```bash\n# Image Storage Service\ncd image-storage-service\nnpm install\nnpm start\n\n# Tree Analyzer Service\ncd tree-analyzer-service\nnpm install\nnpm start\n\n# Clustering Service\ncd postprocessing\npip install -r requirements.txt\npython clustering.py\n```\n\n#### Frontend\n\n```bash\ncd tree-analyzer-web\nnpm install\nnpm run dev\n```\n\n#### Drone Servient\n\n```bash\ncd servients/drone-node\nnpm install\nnode index.js\n```\n\nNote: The servient requires mavlink2rest and the drone simulator to be running.\n\n### Building Docker Images\n\n```bash\n# Build all services\ndocker-compose build\n\n# Build specific service\ndocker-compose build tree-analyzer\n```\n\n### Testing\n\nHealth check endpoints are available for services:\n- Image Storage: `http://localhost:4100/api/health`\n- Tree Analyzer: `http://localhost:4000/api/health`\n- Clustering: Endpoint `/receive` available at `http://localhost:5051/receive` (POST only)\n\n## Project Structure\n\n```\nALIANTE/\n├── drone/ # ArduPilot SITL Docker setup\n│ ├── Dockerfile\n│ └── entrypoint.sh\n├── servients/\n│ └── drone-node/ # Node-WoT servient for drone control\n│ ├── index.js\n│ └── package.json\n├── image-storage-service/ # Image upload/retrieval service\n│ ├── src/\n│ │ └── server.js\n│ ├── Dockerfile\n│ └── package.json\n├── tree-analyzer-service/ # Biodiversity analysis service\n│ ├── src/\n│ │ └── server.js\n│ ├── Dockerfile\n│ └── package.json\n├── tree-analyzer-web/ # React frontend\n│ ├── src/\n│ │ └── App.jsx\n│ ├── Dockerfile\n│ └── package.json\n├── postprocessing/ # Clustering service\n│ ├── clustering.py\n│ ├── Dockerfile\n│ └── requirements.txt\n├── mcp/\n│ └── biodiversity-mcp/ # MCP server\n│ ├── index.js\n│ └── package.json\n├── scripts/ # Utility scripts\n│ ├── drone_tunnels.sh\n│ └── mp_multi.sh\n├── docker-compose.yml # Service orchestration\n└── README.md\n```\n\n## Troubleshooting\n\n### Services not starting\n\n1. 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"Akbermet-Toktobekova/Lauz_hack_2025", + "owner": "Akbermet-Toktobekova", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": "lhp_645d339ca1bf924d", + "pull_requests_closed": 0, + "pull_requests_merged": 3, + "pull_requests_open": 0, + "pull_requests_total": 3, + "pushed_at": "2025-11-23T10:52:29Z", + "readme_length": 11021, + "readme_text": "# Fraud Detection & AML Risk Assessment System\n\n## 🎯 Project Goal\n\nThis project implements an **AI-powered fraud detection and Anti-Money Laundering (AML) risk assessment system** for financial institutions. The system uses **LLaMA 20B** (GPT-OSS-20B) to analyze customer profiles, transaction patterns, and provide explainable risk assessments compliant with **Swiss FINMA regulations**.\n\n### Key Objectives\n\n- **Automated Risk Assessment**: Analyze customer profiles and transactions to assign risk scores (0-100)\n- **Explainable AI**: Provide detailed rationales and feature contributions for risk decisions\n- **Compliance-Focused**: Ensure assessments align with Swiss banking regulations (FINMA)\n- **Unified Customer Profile (UCP)**: Create a single source of truth for all customer data\n- **Conversational Q&A**: Enable compliance officers to query customer data using natural language\n\n---\n\n## 🏗️ Architecture Overview\n\n```\n┌─────────────────────────���───────────────┐\n│ Backend API (Flask) │\n│ - RESTful endpoints │\n│ - /api/assess-risk │\n│ - /api/assess-risk-enhanced │\n│ - /api/qa │\n└──────────────┬──────────────────────────┘\n │\n┌──────────────▼──────────────────────────┐\n│ AI Service Level (Core Logic) │\n│ - FraudAgent (Basic) │\n│ - EnhancedFraudAgent (UCP-based) │\n│ - RAGAgent (Q&A) │\n└──────────────┬──────────────────────────┘\n │\n┌──────────────▼──────────────────────────┐\n│ Data Layer + LLM │\n│ - ProfileAgent (Data extraction) │\n│ - UCPBuilder (Feature engineering) │\n│ - LlamaClient (LLM API calls) │\n└─────────────────────────────────────────┘\n```\n\n---\n\n## 📦 Components\n\n### 1. **ProfileAgent** (`ai_service_level/profile_agent.py`)\n- **Purpose**: Extracts and formats customer data from CSV files\n- **Functionality**:\n - Loads customer data from multiple CSV files\n - Joins data across tables (partner → partner_role → business_rel → account → transactions)\n - Formats into structured text profiles\n - Extracts: Identity, Onboarding Notes, Recent Transactions\n\n### 2. **FraudAgent** (`ai_service_level/fraud_agent.py`)\n- **Purpose**: Basic fraud risk assessment using LLM\n- **Flow**:\n 1. Gets profile text from ProfileAgent\n 2. Creates prompt for LLM risk assessment\n 3. Calls LLaMA server via LlamaClient\n 4. Parses response to extract risk score (0-100) and rationale\n- **Output**: `{risk_score, rationale, partner_id}`\n\n### 3. **UCPBuilder** (`ai_service_level/ucp.py`)\n- **Purpose**: Builds Unified Customer Profile (UCP) - single source of truth\n- **Creates**:\n - Canonical Identity (entity resolution)\n - Static Profile (demographics)\n - Account Data\n - **Financial Aggregates** (feature engineering):\n - `total_spending_30d`, `total_spending_90d`\n - `velocity_tx_per_hour` (transaction frequency)\n - `max_tx_amount`, `avg_tx_value_90d`\n - `tx_count_30d`, `tx_count_90d`\n\n### 4. **EnhancedFraudAgent** (`ai_service_level/enhanced_fraud_agent.py`)\n- **Purpose**: Advanced fraud detection with explainable AI\n- **Features**:\n - Uses UCP for comprehensive analysis\n - Analyzes risk indicators (velocity spikes, large transactions)\n - Provides feature contributions (XAI)\n - Stores risk metadata back in UCP\n- **Output**: Includes `feature_contributions`, `ucp`, `compliance_notes`\n\n### 5. **RAGAgent** (`ai_service_level/rag_agent.py`)\n- **Purpose**: Conversational Q&A about customers\n- **Functionality**:\n - Answers questions using UCP context\n - Extracts citations from customer data\n - Provides factual answers based on profile data\n\n### 6. **LlamaClient** (`ai_service_level/llama_client.py`)\n- **Purpose**: HTTP client for llama-server API\n- **Functionality**: Wraps OpenAI-compatible API calls to LLaMA 20B model\n\n---\n\n## 🚀 How to Run\n\n### Prerequisites\n\n1. **Python 3.x** with required packages:\n ```bash\n pip install flask requests pandas\n ```\n\n2. **LLaMA Server** running on port 8080\n - Model: GPT-OSS-20B (or compatible)\n - Server: llama-server from llama.cpp\n\n### Step 1: Start LLaMA Server\n\n```bash\ncd /workspace/gpt-oss-20b/llama.cpp\n\n# Build server (if not already built)\ncmake -B build\ncmake --build build --config Release -t llama-server\n\n# Start server\n./build/bin/llama-server \\\n -m /path/to/your/model.gguf \\\n --host 0.0.0.0 \\\n --port 8080\n```\n\n### Step 2: Start Backend API\n\n```bash\ncd /workspace\n\n# Set environment variables (optional)\nexport LLAMA_SERVER_URL=\"http://127.0.0.1:8080\"\nexport DATA_DIR=\"data\"\nexport PORT=5001\n\n# Start Flask backend\npython backend/app.py\n```\n\nThe API will be available at `http://localhost:5001`\n\n### Step 3: Test the System\n\n```bash\n# Run test script\npython test_fraud_detection.py\n\n# Or test API directly\ncurl -X POST http://localhost:5001/api/assess-risk \\\n -H \"Content-Type: application/json\" \\\n -d '{\"partner_id\": \"96a660ff-08e0-49c1-be6d-bb22a84e742e\"}'\n```\n\n---\n\n## 📡 API Endpoints\n\n### Health Check\n```http\nGET /health\n```\nReturns system health and LLaMA server status.\n\n### Basic Risk Assessment\n```http\nPOST /api/assess-risk\nContent-Type: application/json\n\n{\n \"partner_id\": \"96a660ff-08e0-49c1-be6d-bb22a84e742e\"\n}\n```\n\n**Response:**\n```json\n{\n \"partner_id\": \"...\",\n \"risk_score\": 45,\n \"rationale\": \"Explanation of risk assessment...\",\n \"raw_response\": \"Full LLM response...\",\n \"status\": \"success\"\n}\n```\n\n### Enhanced Risk Assessment\n```http\nPOST /api/assess-risk-enhanced\nContent-Type: application/json\n\n{\n \"partner_id\": \"96a660ff-08e0-49c1-be6d-bb22a84e742e\"\n}\n```\n\n**Response:**\n```json\n{\n \"partner_id\": \"...\",\n \"risk_score\": 45,\n \"rationale\": \"Detailed explanation...\",\n \"feature_contributions\": {\n \"transaction_velocity\": {\n \"value\": 15.2,\n \"impact\": \"high\",\n \"reason\": \"High transaction frequency...\"\n }\n },\n \"ucp\": {...},\n \"model_version\": \"llama-20b-v1.0\",\n \"timestamp\": \"2024-01-01T12:00:00\"\n}\n```\n\n### Conversational Q&A\n```http\nPOST /api/qa\nContent-Type: application/json\n\n{\n \"partner_id\": \"96a660ff-08e0-49c1-be6d-bb22a84e742e\",\n \"question\": \"What is the total spending for this customer in the last 30 days?\"\n}\n```\n\n**Response:**\n```json\n{\n \"partner_id\": \"...\",\n \"question\": \"...\",\n \"answer\": \"Based on the UCP data...\",\n \"citations\": [...],\n \"ucp_snapshot\": {...},\n \"source\": \"Unified Customer Profile (UCP)\"\n}\n```\n\n### Get Profile\n```http\nPOST /api/profile\nContent-Type: application/json\n\n{\n \"partner_id\": \"96a660ff-08e0-49c1-be6d-bb22a84e742e\"\n}\n```\n\n---\n\n## 📁 Project Structure\n\n```\n/workspace/\n├── backend/\n│ └── app.py # Flask API server\n├── ai_service_level/\n│ ├── __init__.py # Package exports\n│ ├── profile_agent.py # Data extraction\n│ ├── fraud_agent.py # Basic fraud detection\n│ ├── enhanced_fraud_agent.py # Enhanced fraud detection\n│ ├── rag_agent.py # Q&A agent\n│ ├── llama_client.py # LLaMA API client\n│ └── ucp.py # Unified Customer Profile\n├── data/ # CSV data files\n│ ├── partner.csv\n│ ├── transactions.csv\n│ ├── client_onboarding_notes.csv\n│ ├── partner_role.csv\n│ ├── business_rel.csv\n│ ├── br_to_account.csv\n│ ├── account.csv\n│ └── partner_country.csv\n├── frontend/ # Frontend (if applicable)\n├── gpt-oss-20b/ # LLaMA model and server\n├── test_fraud_detection.py # Test script\n└── README.md # This file\n```\n\n---\n\n## 🔧 Configuration\n\n### Environment Variables\n\n- `LLAMA_SERVER_URL`: LLaMA server URL (default: `http://127.0.0.1:8080`)\n- `DATA_DIR`: Path to data directory (default: `data`)\n- `PORT`: Backend API port (default: `5001`)\n\n### Data Files Required\n\nThe system requires the following CSV files in the `data/` directory:\n- `partner.csv` - Customer identity information\n- `transactions.csv` - Transaction history\n- `client_onboarding_notes.csv` - KYC onboarding notes\n- `partner_role.csv` - Partner-entity relationships\n- `business_rel.csv` - Business relationships\n- `br_to_account.csv` - Business relationship to account mapping\n- `account.csv` - Account information\n- `partner_country.csv` - Partner country data\n\n---\n\n## 🧪 Testing\n\nRun the test script to verify the entire pipeline:\n\n```bash\npython test_fraud_detection.py\n```\n\nThis will test:\n1. ✅ Profile Agent (data extraction)\n2. ✅ LLaMA Connection\n3. ✅ Fraud Agent (full pipeline)\n4. ✅ Backend API (if running)\n\n---\n\n## 📝 What We Did\n\n### Session Summary\n\n1. **Project Analysis**\n - Analyzed the fraud detection system architecture\n - Documented all components and their interactions\n - Identified data flow from CSV files → Profile Agent → LLM → Risk Assessment\n\n2. **Code Review**\n - Reviewed `ai_service_level` components\n - Understood UCP (Unified Customer Profile) system\n - Analyzed Enhanced Fraud Agent with explainable AI features\n\n3. **File Cleanup Analysis**\n - Identified unused/duplicate files:\n - `backend/app1.py` (old backup)\n - `ai_service_level/__init__1.py` (old version)\n - `ai_service_level/hello.py` (test script)\n - `ai_service_level/notebooks/model.ipynb` (empty)\n - Provided cleanup recommendations\n\n4. **Git Setup Instructions**\n - Provided instructions for git workflow\n - Explained how to exclude model folder from git\n - Documented recovery process for deleted files\n\n5. **Documentation**\n - Created comprehensive README.md\n - Documented API endpoints\n - Provided setup and running instructions\n\n---\n\n## 🔒 Security & Compliance\n\n- **FINMA Compliance**: System designed for Swiss banking regulations\n- **Data Privacy**: Customer data processed locally\n- **Explainable AI**: All risk assessments include detailed rationales\n- **Audit Trail**: Risk metadata stored in UCP for compliance tracking\n\n---\n\n## 🚧 Future Enhancements\n\n- [ ] OCR Processor implementation for document processing\n- [ ] Multimodal support (image analysis)\n- [ ] Real-time transaction monitoring\n- [ ] Advanced anomaly detection\n- [ ] Integration with external KYC services\n- [ ] Dashboard UI for compliance officers\n\n---\n\n## 📚 Technical Stack\n\n- **Backend**: Flask (Python)\n- **LLM**: LLaMA 20B (GPT-OSS-20B) via llama.cpp\n- **Data Processing**: Pandas\n- **API**: RESTful JSON API\n- **Data Storage**: CSV files (can be migrated to database)\n\n---\n\n## 👥 Usage Example\n\n```python\nfrom ai_service_level.fraud_agent import FraudAgent\n\n# Initialize agent\nagent = FraudAgent(data_dir=\"data\", llama_url=\"http://127.0.0.1:8080\")\n\n# Assess risk for a customer\nresult = agent.assess_risk(\"96a660ff-08e0-49c1-be6d-bb22a84e742e\")\n\nprint(f\"Risk Score: {result['risk_score']}/100\")\nprint(f\"Rationale: {result['rationale']}\")\n```\n\n---\n\n## 📄 License\n\n[Add your license information here]\n\n---\n\n## 🤝 Contributing\n\n[Add contribution guidelines if applicable]\n\n---\n\n## 📧 Contact\n\n[Add contact information if needed]\n\n---\n\n**Last Updated**: 2024", + "readme_title": "Fraud Detection & AML Risk Assessment System", + "releases_count": 0, + "repo": "Lauz_hack_2025", + "repo_name": "Lauz_hack_2025", + "stars": 1, + "topics": [], + "updated_at": 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The repository consists of a backend server that gives out tickets and does validation, a client facing frontend application for generating anonymized tickets that prevent leaks of user information (only ever storing the UUID as recognizable information) and an Android application for reading NFC tags and validating tickets.\n\nHackathon website:https://2025.lauzhack.com/projects\nTeam name: InnoNation", + "readme_title": "2025 Lauzhack SBB challenge", + "releases_count": 0, + "repo": "Lauzhack2025-SBB", + "repo_name": "Lauzhack2025-SBB", + "stars": 0, + "topics": [], + "updated_at": "2026-02-18T22:05:44Z", + "url": "https://github.com/Aleksandar204/Lauzhack2025-SBB", + "watchers": 0 + }, + "https://github.com/Barlassen/AZKT-System-": { + "commit_count_default_branch": 9, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 5, + "html_url": "https://github.com/nerdynomadd", + "login": "nerdynomadd" + }, + { + "contributions": 3, + "html_url": "https://github.com/srsingh04", + "login": "srsingh04" + }, + { + "contributions": 1, + "html_url": "https://github.com/Barlassen", + "login": "Barlassen" + } + ], + "created_at": "2025-11-22T12:59:07Z", + "default_branch": "main", + "description": "Anonymouse Zero Knowledge Ticket System For SBB CFF", + "dirs_total_count": 19, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 462, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 667, + "type": "file" + }, + { + "path": "README.md", + "size": 2791, + "type": "file" + }, + { + "path": "backend", + "size": 0, + "type": "dir" + }, + { + "path": "docs", + "size": 0, + "type": "dir" + }, + { + "path": "frontend", + "size": 0, + "type": "dir" + }, + { + "path": "package-lock.json", + "size": 523517, + "type": "file" + }, + { + "path": "package.json", + "size": 781, + "type": "file" + }, + { + "path": "pnpm-lock.yaml", + "size": 114, + "type": "file" + }, + { + "path": "tickets", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 90, + "first_commit_date_default_branch": "2025-11-22T20:50:25Z", + "forks": 0, + "has_ci": false, + "has_contributing": true, + "has_docker": false, + "has_docs": true, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 65748 + }, + { + "language": "CSS", + "size": 16446 + }, + { + "language": "TypeScript", + "size": 12234 + }, + { + "language": "Noir", + "size": 1428 + }, + { + "language": "Dockerfile", + "size": 117 + } + ], + "last_commit_date_default_branch": "2025-11-23T10:28:11Z", + "last_commit_oid_default_branch": "3a3cad494ef23560243e9795d6986db6edea2edb", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "Barlassen/AZKT-System-", + "owner": "Barlassen", + "parent_repo": null, + "parent_url": null, + "primary_language": "JavaScript", + "project_foreign_key": "lhp_7920af26a2c785af", + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2025-11-23T10:58:13Z", + "readme_length": 2712, + "readme_text": "# 🚂 AZKT - Anonymous Zero-Knowledge Ticket System\n\n**A privacy-preserving ticketing system for SBB CFF FFS**\n\n## 🎯 Core Principles\n\n- **Anonymity**: No personal data collected\n- **ZK Proof**: Prove ticket validity without revealing identity \n- **Ephemeral Wallet**: Single-use key pairs, impossible to track users\n- **Copy-Safe**: Tickets cannot be copied and reused\n- **Multi-Check**: Tickets can be verified multiple times\n- **Easy Access**: Low entry barrier for non-technical users\n- **Print@Home**: Tickets can be printed on paper\n- **Secure**: Against misuse and fraud\n- **Low Cost**: Due to minimal data\n\n## 🚀 Quick Start\n\n### Option 1: Demo Mode (Simplified Proofs)\n\nWorks immediately, no setup required:\n\n```bash\n# Install dependencies\nnpm install\n\n# Start backend\ncd backend && npm run dev\n\n# Start frontend (new terminal)\ncd frontend && npm run dev\n\n# Open http://localhost:3000\n```\n\n### Option 2: Production Mode (Real ZK Proofs)\n\nFor %100 working real ZK proofs:\n\n1. **Install Rust:** https://rustup.rs/\n2. **Run setup:**\n ```powershell\n cd contracts\n .\\setup-circom.ps1\n ```\n3. **Start services:**\n ```bash\n cd backend && npm run dev\n cd frontend && npm run dev\n ```\n\nSee [QUICK_PRODUCTION_SETUP.md](QUICK_PRODUCTION_SETUP.md) for details.\n\n## 📁 Project Structure\n\n```\nAZKT-System/\n├── frontend/ # Next.js frontend\n├── backend/ # Node.js/Express backend\n├── contracts/ # Circom circuits & ZK proofs\n├── verification/ # Offline verification agent\n└── docs/ # Documentation\n```\n\n## 🔐 ZK Proof System\n\n- **Circuit**: `contracts/circuits/TicketOwnership.circom`\n- **Based on**: Hydra-S3 proving scheme\n- **Format**: Hydra-S3 standard (pathElements + pathIndices)\n- **Proof System**: Groth16 (via snarkjs)\n\n## 📚 Documentation\n\n- [START_HERE.md](START_HERE.md) - Quick start guide\n- [PRODUCTION_SETUP.md](PRODUCTION_SETUP.md) - Full production setup\n- [QUICK_PRODUCTION_SETUP.md](QUICK_PRODUCTION_SETUP.md) - Fast setup guide\n- [docs/GETTING_ZK_WORKING.md](docs/GETTING_ZK_WORKING.md) - ZK proof setup\n- [docs/CURRENT_STATUS.md](docs/CURRENT_STATUS.md) - Implementation status\n\n## 🎯 Features\n\n✅ Ephemeral wallet generation \n✅ Merkle tree integration \n✅ ZK proof generation (real or simplified) \n✅ QR code generation (Print@Home ready) \n✅ Copy protection \n✅ Nullifier system (single-use guarantee) \n✅ Offline verification \n✅ SBB ticket format compliance \n\n## 🛠️ Technology Stack\n\n- **Frontend**: Next.js 14, React\n- **Backend**: Node.js, Express\n- **ZK Proofs**: Circom, snarkjs\n- **Cryptography**: Poseidon hash, Merkle trees\n- **QR Codes**: qrcode library\n\n## 📝 License\n\nMIT\n\n## 🤝 Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md)", + "readme_title": "🚂 AZKT - Anonymous Zero-Knowledge Ticket System", + "releases_count": 0, + "repo": "AZKT-System-", + "repo_name": "AZKT-System-", + "stars": 0, + "topics": [], + "updated_at": "2025-11-23T10:28:19Z", + "url": "https://github.com/Barlassen/AZKT-System-", + "watchers": 0 + }, + "https://github.com/Chaitanya-nawale/LauzHack2025": { + "commit_count_default_branch": 1, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 1, + "html_url": "https://github.com/Chaitanya-nawale", + "login": "Chaitanya-nawale" + } + ], + "created_at": "2025-11-22T11:34:35Z", + "default_branch": "main", + "description": null, + "dirs_total_count": 0, + "files_root_entries": [ + { + "path": "README.md", + "size": 509, + "type": "file" + }, + { + "path": "agent.py", + "size": 2476, + "type": "file" + }, + { + "path": "app.py", + "size": 12882, + "type": "file" + }, + { + "path": "tools.py", + "size": 24215, + "type": "file" + } + ], + "files_total_count": 4, + "first_commit_date_default_branch": "2025-11-25T19:22:30Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 39573 + } + ], + "last_commit_date_default_branch": "2025-11-25T19:22:30Z", + "last_commit_oid_default_branch": "f3a9f1547092532f6b911b89c5908bcb3d8902d5", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "Chaitanya-nawale/LauzHack2025", + "owner": "Chaitanya-nawale", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": "lhp_592bcf5cc3d9ed9f", + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2025-11-25T19:22:31Z", + "readme_length": 509, + "readme_text": "# Autonomous Web Agent\n\nThis project is an autonomous agent that can interact with web pages.\n\n## Prerequisites\n\nYou need to have the language model server running. 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AI-Powered Tenant Protection Platform\n\nA comprehensive tenant protection platform leveraging multi-agent AI systems, computer vision, and Swiss legal expertise to help tenants manage lease agreements, document property conditions, and defend against unfair damage claims.\n\n## 🌐 Live Demo\n\n**Deployed on Vercel**: https://leasecare.chlc.top/\n\n---\n\n## 🚀 Features\n\n### Core Capabilities\n- **🤖 Multi-Agent AI Pipeline**: 3-stage defense analysis system with specialized AI agents\n- **📄 Smart Document Analysis**: NLP-powered lease agreement parsing with risk assessment\n- **🎯 Auto Asset Detection**: ML-based classification (property, vehicles, equipment)\n- **📸 Guided Photo Documentation**: Timestamped evidence collection with metadata\n- **💬 Live Lease Chat**: Context-aware AI assistant with clickable law citations\n- **🔍 Checkout Comparison**: Computer vision damage detection with before/after analysis\n- **⚖️ Defense Report Generation**: Automated legal defense with Swiss CO compliance\n- **🇨🇭 Swiss Law Integration**: Real-time legal explanations via OpenJustice API\n\n---\n\n## 🏗️ Architecture Overview\n\n### System Architecture\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ Frontend Layer │\n│ Vue 3 + TypeScript + Tailwind CSS + Vite │\n└─────────────────────────────────────────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────────────────────────┐\n│ State Management │\n│ Pinia Store (Lease Data, User Context, Evidence) │\n└─────────────────────────────────────────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────────────────────────┐\n│ Service Layer │\n│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │\n│ │ Together AI │ │ OpenJustice │ │ Firebase │ │\n│ │ Service │ │ Service │ │ Service │ │\n│ └──────────────┘ └──────────────┘ └──────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────────────────────────┐\n│ External Services │\n│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │\n│ │ Together AI │ │ OpenJustice │ │ Firebase │ │\n│ │ (Llama-4) │ │ API │ │ (Storage) │ │\n│ └──────────────┘ └──────────────┘ └──────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n```\n\n### Data Flow\n\n```\nUser Upload → Document Parser → AI Analysis → Review Stage\n ↓\n Guided Intake → Photo Storage\n ↓\n Live Chat ← Context Retrieval\n ↓\n Checkout → Damage Detection\n ↓\n Defense Pipeline → Report Generation\n```\n\n## 🛠️ Technology Stack\n\n### Frontend Framework\n- **Vue 3.5+** - Composition API with `