Akshatrao00/-Neurobreak-Ai

AI Summary: Enhance the self-evolving AI project by implementing real-time logging and monitoring. This involves integrating a logging framework (e.g., Python's logging module or Loguru), storing logs persistently, and creating a web dashboard (using Flask, FastAPI, or Streamlit) to display key metrics like iteration number, best score, score history, code snippets, and errors. The dashboard should ideally allow for starting/stopping the AI loop. Comprehensive documentation is also required.

AI Summary: Improve the AI's code evaluation by integrating Python static analysis tools (like pylint, flake8, or mypy) to assess code quality and correctness. The AI should then use these analysis results to generate a more meaningful performance score, replacing the current random scoring system. Unit tests should be added to verify the new evaluation logic, and the updated evaluation strategy should be documented.

AI Summary: Integrate OpenAI's GPT-4 API into the NeuroBreak AI project to replace the mock code modification logic with real AI-generated Python code. This involves modifying the `engine/modifier.py` module, implementing error handling, secure configuration options (likely using environment variables), and updating documentation. The goal is to make the AI truly self-evolving.