etsi-ai/etsi-failprint

AI Summary: Implement NLP model failure analysis in the `failprint` MLOps tool. This involves creating a text preprocessing pipeline for embedding generation, implementing embedding-based clustering for similar failure patterns, adding support for transformer model outputs, integrating with popular NLP libraries (transformers, spaCy, sentence-transformers), adding text-specific drift detection, and creating NLP-focused failure segmentation (by text length, complexity, domain, etc.). The goal is to extend the tool's capabilities to handle unstructured text data and provide root cause analysis for NLP model failures.

AI Summary: Implement a command-line interface (CLI) for the `failprint` Python package using the `argparse` module. The CLI should allow users to specify input data files (features, true labels, predicted labels), output format, and optionally clustering, via command-line arguments. The CLI should then call the existing `analyze()` function and either print the report to the terminal or write it to a file.

AI Summary: Resolve a license conflict in the failprint project by choosing a single license (either BSD-2-Clause or MIT) and updating the `pyproject.toml`, `README.md`, and `LICENSE` files to reflect the chosen license consistently.

AI Summary: Improve the `failprint` tool's segmentation logic to handle continuous numerical features more effectively. The current implementation creates a segment for each unique value, leading to excessive noise. The task involves implementing automatic binning (using `pd.cut` or `pd.qcut`) for numerical columns with high cardinality, allowing the analysis to identify problematic ranges instead of individual values.

AI Summary: Add a License section to the failprint project's README file, specifying the license type (which is already present in a separate license file).

AI Summary: Enhance the documentation for the `etsi-failprint` project by creating a comprehensive getting started guide (docs/getting_started.md), updating the README with links to examples, and adding code examples and explanations. The goal is to make the project more accessible to beginners, including clear instructions for setup, running tests, interpreting reports, and troubleshooting common errors.

AI Summary: Integrate a Streamlit dashboard into the failprint MLOps tool to visualize failure patterns from ML model predictions. This involves creating an interactive UI allowing users to upload data, explore misclassified clusters, view feature distributions, and analyze class imbalance and drift using charts and plots (e.g., Plotly or Matplotlib). The existing `analyze()` function's logic should be reused.