Emotica AI

Emotica AI is a compassionate and therapeutic virtual assistant designed to provide empathetic and supportive conversations. It integrates a local LLaMA model for text generation, a vision model for image captioning, a RAG system for information retrieval, and emotion detection to tailor its responses.

Features

• Local AI: Runs entirely on your local machine.
• Multi-modal: Understands both text and images.
• RAG (Retrieval-Augmented Generation): Can answer questions based on documents you provide.
• Emotion-Aware: Detects user emotions from images to provide more empathetic responses.
• Web Interface: Easy-to-use chat interface accessible from your browser.
• Memory: Remembers key information shared by the user for a personalized experience.

Requirements

• Python: Version 3.10, 3.11, or 3.12.
• Git: For cloning the repository.
• C++ Compiler: Required for llama-cpp-python.
  • Windows: Install Visual Studio with the "Desktop development with C++" workload.
  • macOS: Install Xcode Command Line Tools (xcode-select --install).
  • Linux: Install build-essential (sudo apt-get install build-essential).
• CUDA Toolkit (Optional, for NVIDIA GPUs): For GPU acceleration. Make sure to install a version compatible with your driver and PyTorch.

Deployment Guide

1. Clone the Repository

git clone https://github.com/KienPC1234/Emotica-AI.git
cd Emotica-AI

2. Create a Virtual Environment

It's highly recommended to use a virtual environment to manage dependencies.

• Windows:

  python -m venv venv
  .\venv\Scripts\activate

• macOS / Linux:

  python3 -m venv venv
  source venv/bin/activate

3. Install Dependencies

The application will attempt to install required packages automatically when you run it. However, you can also install them manually.

There are two requirements files:

• requirements.txt: For CPU-based inference.
• requirements_cuda.txt: For NVIDIA GPU-based inference (includes CUDA-enabled PyTorch and llama-cpp-python).

For CPU:

pip install -r requirements.txt

For NVIDIA GPU (CUDA):

pip install -r requirements_cuda.txt

Note: The automatic installer in main.py uses requirements.txt by default. You may need to edit main.py if you want it to use the CUDA requirements file.

4. Configure Models

1. Download your desired GGUF-format LLaMA models.

2. Place them in the models/ directory.

3. Edit models/cfg.json to configure your models. A sample file is created on first run if it doesn't exist. Make sure the path for each model is correct.

   {
     "models": [
       {
         "name": "Llama-3-8B-Instruct-Demo",
         "path": "models/Meta-Llama-3-8B-Instruct.Q4_K_M.gguf",
         "format": "llama-3",
         "description": "Model LLaMA 3 8B",
         "max_context": 8192,
         "temperature": 0.7,
         "top_p": 0.9
       }
     ],
     "valid_formats": ["llama-3", "mistral-instruct", "chatml"]
   }

5. Run the Application

Simply run the main.py script from the project root directory.

python main.py

The script will:

1. Check if your Python version is compatible (3.10-3.12).
2. Check for and install any missing dependencies from requirements.txt.
3. Start the LLaMA API server and the Web server.
4. Automatically open the web interface in your default browser.

The application will be available at http://localhost:8000 (or the port configured in ports.toml).

6. Shutting Down

Press Ctrl+C in the terminal where main.py is running to gracefully shut down all server processes.