gonz4lex/runelog

AI Summary: Implement a `delete_experiment` method in the Runelog Python library to remove a specified experiment and its associated files. The method should handle cases where the experiment doesn't exist, raising a custom exception. Unit tests are required to ensure proper functionality and error handling.

AI Summary: Refactor the Runelog Python library to replace generic exceptions with custom exceptions, specifically `NoActiveRunException` in `_get_run_path()` and `ModelNotFoundException` in `load_registered_model()`, and potentially others as needed throughout the codebase. This improves error handling and clarity.

AI Summary: Complete the documentation for a lightweight machine learning experiment tracking library, including a comprehensive README with installation instructions and usage examples, at least two example scripts demonstrating different use cases, a LICENSE file, and a CHANGELOG.md file.

AI Summary: Create a Dockerfile to package the existing Streamlit UI application for Runelog. The Dockerfile should build successfully and the resulting image should run successfully, mounting the `.mlruns` and `.registry` directories as volumes to persist experiment and model data.

AI Summary: Refactor the Runelog Python library to improve error handling by creating custom exception classes (e.g., `ModelNotFound`, `ExperimentNotFound`) for more informative error messages. This involves brainstorming appropriate exception types and implementing them throughout the codebase.