Extending WiMarka
=================

This guide covers extending WiMarka with new features, languages, and custom evaluation logic.

Adding New Languages
--------------------

To add support for a new Philippine language:

Step 1: Update Language Codes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Add language code to ``config.py``:

.. code-block:: python

   SUPPORTED_LANGUAGES = ['EN', 'CEB', 'ILO', 'TGT', 'HIL']  # Add HIL

Step 2: Update Helper Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Modify ``utils/helper.py`` to recognize the new language:

.. code-block:: python

   LANGUAGE_NAMES = {
       'EN': 'English',
       'CEB': 'Cebuano',
       'ILO': 'Ilocano',
       'TGT': 'Tagalog',
       'HIL': 'Hiligaynon'  # Add new language
   }

Step 3: Add Language-Specific Models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If using language-specific models, update ``model.py``:

.. code-block:: python

   LANGUAGE_MODELS = {
       'CEB': 'model-id-for-cebuano',
       'ILO': 'model-id-for-ilocano',
       'TGT': 'model-id-for-tagalog',
       'HIL': 'model-id-for-hiligaynon'  # Add new model
   }

Step 4: Test
~~~~~~~~~~~~

Create test files and validate:

.. code-block:: bash

   wimarka --src_file_path test_en.txt \\
           --src_lang EN \\
           --tgt_file_path test_hil.txt \\
           --tgt_lang HIL

Adding Custom Tasks
-------------------

To add a new evaluation task:

Step 1: Create Task Module
~~~~~~~~~~~~~~~~~~~~~~~~~~~

Create ``wimarka/tasks/sentiment_analysis.py``:

.. code-block:: python

   def analyze_sentiment(src_line: str, tgt_line: str) -> str:
       """Analyze sentiment preservation in translation."""
       # Implementation
       return sentiment_report

Step 2: Integrate into Pipeline
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Modify ``main.py``:

.. code-block:: python

   from wimarka.tasks import sentiment_analysis
   
   # In wmk_eval function
   sentiment = sentiment_analysis.analyze_sentiment(src_line, tgt_line)
   results['sentiment'].append(sentiment)

Step 3: Update Results Structure
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Add new field to results dictionary:

.. code-block:: python

   results = {
       'source': [],
       'target': [],
       # ... existing fields ...
       'sentiment': []  # New field
   }

Custom Scoring Algorithms
--------------------------

To implement custom scoring:

.. code-block:: python

   def custom_scoring(src: str, tgt: str, errors: List[str]) -> Tuple[float, float, float]:
       """Custom scoring logic."""
       # Your algorithm
       fluency = calculate_custom_fluency(src, tgt)
       adequacy = calculate_custom_adequacy(src, tgt, errors)
       overall = (fluency + adequacy) / 2
       
       return fluency, adequacy, overall

   # Replace in pipeline
   from wimarka.tasks import scoring
   scoring.scoring = custom_scoring

Creating Plugins
----------------

For distributable extensions:

.. code-block:: python

   # wimarka_plugin_myfeature/__init__.py
   
   def register_plugin():
       """Register plugin with WiMarka."""
       from wimarka import main
       main.register_task('myfeature', my_task_function)

Alternative Interfaces
----------------------

Web API Example
~~~~~~~~~~~~~~~

.. code-block:: python

   from flask import Flask, request, jsonify
   from wimarka.main import wmk_eval, results
   
   app = Flask(__name__)
   
   @app.route('/evaluate', methods=['POST'])
   def evaluate():
       data = request.json
       # Process and return results
       return jsonify(results)

GUI Example
~~~~~~~~~~~

.. code-block:: python

   import tkinter as tk
   from wimarka.main import wmk_eval
   
   # Create GUI application
   # ...

Best Practices
--------------

* **Modular Design**: Keep extensions independent
* **Documentation**: Document all custom features
* **Testing**: Test extensions thoroughly
* **Compatibility**: Ensure backwards compatibility

See Also
--------

* :doc:`architecture` - System architecture
* :doc:`development` - Development guide
* :doc:`api_reference` - API documentation