SentinelIQ

# SentinelIQ Inspired by spy and detective movies, **SentinelIQ** is an AI-powered forensics analysis platform for immersive crime scene investigation. Users can upload evidence files, and the tool performs a comprehensive analysis using advanced machine learning techniques. It combines a modern React frontend with a Python FastAPI backend to provide real-time forensic analysis across multiple evidence types. It is a Claude Desktop MCP server that can help to analyze crime scenes and get forensic reports from evidences. ## Key Features 1. **Crime Scene Analysis** - Uses Ollama llava for detailed visual analysis 2. **Fingerprint Analysis** - Predicts blood group from fingerprint images using TensorFlow 3. **Audio Transcription** - Transcribes audio evidence using Faster Whisper 4. **Suspect Identification** - Matches suspects against a global database 5. **Comprehensive Reporting** - Generates integrated forensic reports combining all analyses ## Technology Stack ### Frontend - React 19.2.4 - TypeScript 5.9.3 - Vite 8.0.1 (build tool) - TailwindCSS 4.2.2 - Framer Motion 12.38.0 (animations) - Lucide React 0.577.0 (icons) - ESLint 9.39.4 ### Backend - FastAPI (web framework) - Uvicorn (ASGI server) - TensorFlow (neural networks) - PyTorch & Torchvision (deep learning) - Faster Whisper (audio transcription) - Transformers (LLM models) - Scikit-learn & SciPy (ML utilities) - Pillow (image processing) ## Project Structure ``` ai-forensics-react/ ├── src/ # React frontend source │ ├── main.tsx # Entry point │ ├── App.tsx # Main app component with state management │ ├── App.css │ ├── index.css │ ├── components/ │ │ ├── HeroSection.tsx # Upload interface │ │ ├── AnalyzingSection.tsx # Live analysis progress │ │ └── ReportSection.tsx # Results display │ └── assets/ ├── backend/ # Python backend │ ├── app.py # FastAPI server (port 5500) │ ├── image-a.py # Crime scene & report analysis │ ├── fingerprint.py # Blood group prediction │ ├── audio_agent.py # Audio transcription │ ├── suspect-identifying.py # Suspect matching │ ├── test_user_code.py │ ├── requirements.txt # Python dependencies │ ├── fingerprint_bloodgroup_classifier_attention.h5 # Trained model │ └── venv/ # Virtual environment (to be created) ├── public/ # Static assets ├── package.json # Frontend dependencies ├── vite.config.ts # Vite configuration ├── tsconfig.json # TypeScript configuration └── README.md ``` ## Setup & Installation ### Prerequisites - Node.js 16+ and npm - Python 3.8+ - Git ### Frontend Setup 1. **Navigate to the project root:** ```bash cd ai-forensics-react ``` 2. **Install dependencies:** ```bash npm install ``` ### Backend Setup 1. **Navigate to backend directory:** ```bash cd backend ``` 2. **Create a Python virtual environment:** ```bash python -m venv venv ``` 3. **Activate the virtual environment:** **Windows (PowerShell):** ```bash .\\venv\\Scripts\\Activate.ps1 ``` **Windows (CMD):** ```bash .\\venv\\Scripts\\activate.bat ``` **macOS/Linux:** ```bash source venv/bin/activate ``` 4. **Install Python dependencies:** ```bash pip install -r requirements.txt ``` ## Running the Project ### Development Mode **Terminal 1 - Start the FastAPI backend:** ```bash cd backend python app.py ``` (Backend runs on `http://localhost:5500`) **Terminal 2 - Start the React frontend:** ```bash npm run dev ``` (Frontend runs on `http://localhost:5173`) 3. **Access the application:** - Open [http://localhost:5173](http://localhost:5173) in your browser ### Available npm Scripts ```bash npm run dev # Start Vite development server npm run build # Build for production (TypeScript + Vite) npm run lint # Run ESLint npm run preview # Preview production build ``` ### Claude Desktop Integration (MCP Server) This project integrates with Claude Desktop via an MCP (Model Context Protocol) server to perform advanced AI-powered forensic analysis. **Prerequisites:** - Claude Desktop application must be installed. **Configuration:** 1. Open the Claude Desktop application's configuration file (e.g., `settings.json`). 2. Locate the section for MCP server configurations. 3. Add or modify the entry to point to the project's root directory. This allows Claude to access the project's context for analysis. **Example JSON configuration:** ```json { "mcp.server.paths": [ "C:\\binary-frontend\\Binary2\\ai-forensics-react" ] } ``` *Note: The exact JSON key might differ. Please refer to the Claude Desktop documentation for the correct key.* 4. Save the configuration file and restart Claude Desktop. Once configured, you can leverage Claude's capabilities to interact with and analyze the project's data.