🤖 AI Minecraft Assistant 🕹️

👋 Introduction:

• This is Humphrey, a AI Minecraft Assistant project aims to create a helpful tool that provides assistance and answers questions related to the game Minecraft. It leverages Meta's Llama 3 LLM model to understand user queries and provide relevant information and guidance.
• This project is not affiliated with or endorsed by Mojang Studios, the creators of Minecraft.

📦 Tech Stack:

• Meta's Llama3 Language Model (LLM) for natural language understanding and generation.
• Python for backend scripting.
• HTML/CSS/JavaScript for frontend development.
• A text2text-generation model from Finnish-NLP on HuggingFace

👨‍💻 Features:

• Listening and recognizing speech, responding only when "Humphrey" is spoken (similar to how "Alexa" works)
• Answering questions related to gameplay mechanics, crafting recipes, item descriptions, etc.
• Providing tips, tricks, and strategies for gameplay improvement.
• Offering assistance with troubleshooting common issues and errors.

💭 Process:

• The project begins with defining the scope and objectives of the AI Minecraft Assistant.
• Development starts with setting up the backend infrastructure, including installing and configuring the language model.
• Backend development continues with implementing speech recognition.
• Frontend development is initiated to create a user-friendly interface that is continuously updated with AJAX.
• Testing and debugging are performed to ensure the assistant functions correctly and provides accurate responses.
• Documentation and user guides are created to assist users in utilizing the AI Minecraft Assistant effectively.

📚 What We Learn:

• Understanding and leveraging the capabilities of language models for natural language processing tasks.
• Integrating external APIs or SDKs with existing software (e.g., HuggingFace NLP, Ollama API requests).
• Frontend development skills for creating user interfaces.
• Project management and coordination in software development.
• Problem-solving and troubleshooting skills, especially when dealing with complex systems and integration challenges.

✨ Improvements:

• Fine tuning the LLM instead of prompt engineering
• Adding support for additional languages to cater to a broader audience.
• Gathering user feedback and iteratively improving the assistant based on user needs and preferences.

🚦 Running the Project: To run the project:

• Clone the repository from GitHub.
• Install and run Ollama on your local machine.
• Install dependencies as specified in the project's requirements.txt.
• Run the program with python3 app.py while using not Python 3.12 (I tested with 3.11 and 3.9)
• Speak a question beginning with the word "Humphrey", similar to how an "Alexa" works

📸 Video of the Website: