# How to Run: Tamil MCQ Generator

This guide explains the directory structure of the project and guides you through running the MCQ generator locally to test the model.

## 📂 Directory Structure

```text
Quiz_MCQ_model/
├── data/                             # Raw corpus and text data files
├── models/                           # Trained machine learning models (.bin fasttext model, Hugging Face models)
├── nlp/                              # Core NLP tools
│   ├── ner_tagger.py                 # Hugging Face Named Entity Recognition
│   └── pos_tagger.py                 # Stanza POS Tagging
├── preprocessing/                    # Text sanitization pipeline
│   ├── cleaner.py                    # Cleans texts of unwanted characters
│   ├── data_loader.py                # Loads zips and txt corpus files
│   ├── process_pipeline.py           # Orchestrates the validation and tokenization
│   └── tokenizer.py                  # Sentence and word tokenizers
├── question_generation/              # The MCQ Logic
│   ├── candidate_selector.py         # Selects the best word to be the answer
│   ├── domain_vocab.py               # Pre-defined vocabularies by subject
│   ├── option_generator.py           # Uses FastText to generate wrong answers (distractors)
│   ├── question_builder.py           # Masks the answer to build the question
│   └── test_model.py                 # The MAIN script to run the pipeline
├── prepare_corpus.py                 # Script to prepare the 'data/' folder
├── tamil_mcq_clustering.py           # Script to cluster words offline (HDBSCAN)
├── train_fasttext.py                 # Script to train your own FastText embeddings
├── requirements.txt                  # Python dependencies
├── README.md                         # Project overview
└── HOW_TO_RUN.md                     # This file
```

---

## 💻 Running the Model

### Step 1: Install Dependencies
Open a terminal in the root directory and install the required Python packages:

```bash
pip install -r requirements.txt
```

### Step 2: Preparing the Offline Data (Optional)
*Note: If `models/tamil_fasttext.bin` and the cluster JSON files are already built and present, you can skip this step.*

If you need to train the model from scratch:
1. Run `python prepare_corpus.py` to compile the corpus.
2. Run `python train_fasttext.py` to create the word embeddings.
3. Run `python tamil_mcq_clustering.py` to generate the word clusters.

### Step 3: Checking the Model (The Main Entry Point)
To observe the MCQ Generation happening live on a sample Tamil text passage, you run the `test_model.py` script located in the `question_generation` folder.

Run the following command from the root directory:

```bash
python question_generation/test_model.py
```

### What to Expect:
When you run `test_model.py`, the code will pass a sample paragraph (e.g., about the Solar System) through the entire pipeline.
The terminal output will display:
- The original sentence.
- The formatted MCQ sentence with the blank "_____".
- 4 generated options (including the correct answer and 3 smart distractors).
- The correct answer extracted below.