# Python Docstring Generator

Generates docstrings for Python code snippets using a sequence-to-sequence model (e.g. T5 or CodeT5). Useful for code summarization and documentation.

## Task

Given a Python function or code block (without a docstring), the model produces a short natural-language description suitable as a docstring.

## Model

- Uses **Hugging Face Transformers** with a small T5 or CodeT5 checkpoint (e.g. `t5-small`, or `Salesforce/codet5-small` for code).
- Inference script loads the model and tokenizer and runs generation with configurable length and sampling.

## Dataset

- Training (optional): datasets like **CodeXGlue** code-to-text, or **DocstringGeneration**-style data from Hugging Face Datasets.
- For inference only, no dataset is required; use pre-trained weights.

## Usage

```bash
pip install -r requirements.txt
python inference.py --input "def add(a, b): return a + b"
```

For a quick demo in the browser, run the Gradio app:

```bash
python app.py
```

## Example

Input:
```python
def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n - 1)
```

Output (example): `"Compute the factorial of n recursively."`

## Files

- `inference.py` — loads T5 (or CodeT5), runs generation; can take a file path or inline code.
- `app.py` — Gradio UI for pasting code and getting a docstring.

## Limitations / future work

- Quality depends on the base model and any fine-tuning; out-of-domain code may get generic descriptions.
- Could be extended to multi-line docstrings or different styles (Google, NumPy, Sphinx).

## Author

**Alireza Aminzadeh**  
- Email: [alireza.aminzadeh@hotmail.com](mailto:alireza.aminzadeh@hotmail.com)  
- Hugging Face: [syeedalireza](https://huggingface.co/syeedalireza)  
- LinkedIn: [alirezaaminzadeh](https://www.linkedin.com/in/alirezaaminzadeh)