Metadata-Version: 2.4
Name: imap-foml-study-library
Version: 0.1.0
Summary: Offline IMAP and FOML study library with searchable PDFs and Jupyter notebook solutions
Author: Kalesh contributors
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Utilities
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pypdf>=4.0
Provides-Extra: dev
Requires-Dist: pytest>=7; extra == "dev"
Requires-Dist: build>=1; extra == "dev"
Requires-Dist: twine>=5; extra == "dev"
Dynamic: license-file

# IMAP & FOML Study Library

An offline, terminal-based study library for IMAP exam PDFs and FOML solved-paper/practical Jupyter notebooks. It searches bundled PDFs locally and lets you read or export notebook code from a numbered menu.

## Install from PyPI

```bash
python -m pip install --upgrade imap-foml-study-library
python -m kalesh
```

You can also launch the console command `imap-foml-study-library` after installation. The legacy `python -m kalesh` command remains supported.

## Main menu

- **1. Browse documents**: read the bundled IMAP PDFs in the terminal.
- **2. Search all documents**: search the text of all bundled PDFs offline.
- **3. Library statistics**: see the available document counts.
- **4. Rebuild search index** and **5. Health check**.
- **8. FOML Paper**: browse the three solved-paper notebooks (Exam, Set B, Set C).
- **9. FOML Codes**: browse the six practical notebooks (LDA, TensorFlow diabetes, PCA/face detection, PCA/TensorFlow, KNN, K-Means).

Inside a notebook, use `A` to display all cells, `E` to export code to a `.py` file, or `B` to browse one cell at a time. Use `S` to search within the selected notebook group.

## Bundled materials

The distribution includes the existing IMAP/backend/Docker PDFs, six FOML practical notebooks, and three FOML solved-paper notebooks. Some notebooks reference external datasets or libraries; these are not automatically installed or bundled, so executing the original practicals may require additional dependencies/data files.

## Offline use

After installation, the bundled content works without an internet connection. Internet access is needed to install or update the package from PyPI.

## Import from Python

The internal module remains `kalesh` for compatibility:

```python
import kalesh
print(kalesh.search("Docker", limit=5))
```
