🔄 Pseudo2Code – Transformer-based Pseudocode to C++ Converter

A fully custom Transformer-based Sequence-to-Sequence model built from scratch in PyTorch to convert human-written pseudocode into executable C++ code. Trained on the SPoC dataset from Stanford.

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🖼️ Demo

Try it live on Hugging Face Spaces:
👉 https://huggingface.co/spaces/asadsandhu/Pseudo2Code

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🧠 Model Architecture

• Developed using the Transformer architecture from scratch in PyTorch
• No pre-trained models (pure from-scratch implementation)
• Token-level sequence generation using greedy decoding
• Custom vocabulary construction for both pseudocode and C++ output

Input:   Pseudocode lines (line-by-line)
Model:   Transformer (Encoder-Decoder)
Output:  C++ code line for each pseudocode line

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📊 Dataset

We used the SPoC dataset from Stanford:

• ✅ Clean pseudocode–C++ line pairs
• ✅ Token-level annotations for syntax handling
• ✅ Multiple test splits (generalization to problems/workers)
• ✅ Custom preprocessing and vocabulary building implemented

📎 Licensed under CC BY 4.0

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📁 Directory Structure

.
├── app.py                # Gradio web app for inference
├── train.py              # Transformer training code
├── model.pth             # Trained model weights
├── spoc/                 # Dataset directory
│   └── train/
│       ├── spoc-train.tsv
│       └── split/spoc-train-eval.tsv
├── assets/
│   └── demo.png          # App screenshot
└── README.md             # You're here

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🛠️ How to Run Locally

⚙️ 1. Clone Repo & Install Requirements

git clone https://github.com/asadsandhu/Pseudo2Code.git
cd Pseudo2Code
pip install -r requirements.txt

Or manually install:

pip install torch gradio tqdm

🚀 2. Launch the App

Make sure model.pth is present (or train using train.py):

python app.py

The app will open in your browser.

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🧪 Training the Model

You can retrain the model using the train.py script:

python train.py

By default, it downloads data from the public repo and trains for 10 epochs. Outputs a model.pth file with learned weights and vocab.

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🔧 Key Hyperparameters

Parameter	Value
Model Type	Transformer
Max Length	128
Embedding Dim	256
FFN Dim	512
Heads	4
Encoder Layers	2
Decoder Layers	2
Batch Size	64
Epochs	10
Optimizer	Adam
Learning Rate	1e-4

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🧩 Example Input

n , nn, ans = integers with ans =0
Read n
for i=2 to n-1 execute
set nn to n
while nn is not equal to 0, set ans to ans + nn%i, and also set nn= nn/i
}
set o to gcd(ans, n-2)
print out ans/o "/" (n-2)/o

⏩ Output C++

int main() {
int n , nn , ans = 0 ;
cin > > n ;
for ( int i = 2 ; i < = n - 1 ; i + + ) {
nn = n ;
while ( nn = = 0 ) ans + = nn % i , nn / = i ;
}
o = gcd ( ans , n - 2 ) ;
cout < < ans / 2 / o ( n - 2 ) / o < < endl ;
return 0;
}

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📦 Deployment

This app is deployed live on:

• Hugging Face Spaces: Pseudo2Code
• GitHub: github.com/asadsandhu/Pseudo2Code

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🙌 Acknowledgements

• 📘 SPoC Dataset by Stanford University Kulal, S., Pasupat, P., & Liang, P. (2020). SPoC: Search-based Pseudocode to Code

• 🧠 Transformer Paper: "Attention is All You Need"

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🧑‍💻 Author

Asad Ali GitHub: asadsandhu Hugging Face: asadsandhu LinkedIn: asadxali

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📄 License

This project is licensed under the MIT License. Feel free to use, modify, and share with credit.