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ML Engineer Portfolio & AI Demo Stack

Welcome to my comprehensive portfolio website featuring an interactive machine learning model collection and professional showcase.

🎯 About This Project

This is a full-stack React application that serves as both my personal portfolio and a demonstration platform for various machine learning and AI capabilities. The site showcases my work as a Machine Learning Engineer while providing interactive demos of cutting-edge AI models.

🤖 New Architecture: Server-Hosted LLM

The chat functionality has been re-architected to use a powerful, server-hosted Language Model (LLM) powered by a Python FastAPI backend. This replaces the previous implementation that used Transformers.js for client-side inference.

Key Changes:

  • FastAPI Backend: A new backend service built with FastAPI hosts a Hugging Face text-generation model (e.g., mistralai/Mistral-7B-Instruct-v0.2).
  • GPU-Powered: The backend is designed for deployment on a GPU server for high-performance inference, with Docker support included.
  • Streaming API: The backend exposes a /chat endpoint with streaming support, allowing for real-time, token-by-token responses in the UI.
  • Decoupled Frontend: The React frontend now communicates with the FastAPI backend, making it a lightweight and highly scalable client application.

🛠️ Development Setup

This project now consists of two main parts: the React frontend and the FastAPI backend.

1. Backend Setup (FastAPI + Hugging Face)

The backend runs a Hugging Face model on a Python server. It requires a machine with a GPU for optimal performance.

Prerequisites:

  • Python 3.10+
  • An NVIDIA GPU with CUDA drivers installed.

Instructions:

  1. Navigate to the backend directory:

    cd backend-fastapi

  2. Create and activate a virtual environment:

    python -m venv venv
source venv/bin/activate

  3. Install dependencies:

    pip install -r requirements.txt

  4. Configure the model: Create a .env file in the backend-fastapi directory and specify the Hugging Face model you want to use:

    MODEL_ID="mistralai/Mistral-7B-Instruct-v0.2"

  5. Run the backend server:

    uvicorn main:app --host 0.0.0.0 --port 8000

    The server will download the model on the first run, which may take some time.

2. Frontend Setup (React)

  1. Navigate to the project root directory:

    cd /path/to/MLAI-Stack

  2. Install dependencies:

    npm install

  3. Run the frontend development server:

    npm run dev

    The frontend will be available at http://localhost:5173 and will connect to the backend running on port 8000.

🐳 Docker Deployment (Backend)

The FastAPI backend can be easily deployed as a Docker container, which is the recommended approach for production.

Prerequisites:

  • Docker
  • NVIDIA Container Toolkit (for GPU support)

Instructions:

  1. Navigate to the backend directory:

    cd backend-fastapi

  2. Build the Docker image:

    docker build -t llm-backend .

  3. Run the Docker container with GPU access:

    docker run --gpus all -p 8000:8000 -v ./huggingface:/app/huggingface llm-backend
    • --gpus all provides the container with access to all available GPUs.
    • -v ./huggingface:/app/huggingface mounts a local directory to the container to cache the Hugging Face models, preventing re-downloads on container restarts.

🔗 Live Demo

Visit the live application to explore the interactive ML demos and portfolio content.

📞 Contact

Carlos Gonzalez Rivera

This project demonstrates the intersection of machine learning research, software engineering, and user experience design. Each component is built with production-quality standards and serves as both a functional tool and a learning resource.