CodeAlchemy ⚗️: Transforming Pseudocode into C++ Magic with Transformers

Unlock the power of AI-driven code generation and streamline your development process! 🚀

Introduction: The Future of Code Generation is Here! 🔮

Imagine a world where you write simple pseudocode, and an AI magically converts it into fully functional C++ code. Sounds like a dream? Well, with CodeAlchemy, this dream becomes a reality! 💡

CodeAlchemy is an AI-powered Transformer-based sequence-to-sequence model that converts pseudocode into C++ with remarkable accuracy. Whether you are a student, developer, or AI enthusiast, this project showcases the potential of deep learning in automating programming tasks.

In this blog, we will explore how CodeAlchemy works, its architecture, training process, and how you can try it out using an interactive Streamlit UI. Let’s dive in! 🌊

Why CodeAlchemy? 🤔

Developers often write pseudocode to sketch out logic before implementing it in a programming language. However, translating that into syntactically correct and efficient C++ code can be time-consuming.

CodeAlchemy helps by:

✅ Accelerating Development: Generate C++ code instantly from pseudocode.
✅ Reducing Human Error: No more syntax errors or missing semicolons.
✅ Enhancing Productivity: Focus on logic instead of language syntax.

This is a game-changer for beginners learning C++ and professionals looking to automate repetitive coding tasks.

How CodeAlchemy Works ⚙️

At its core, CodeAlchemy is built on a Transformer-based model (similar to GPT and BERT) but customized for sequence-to-sequence learning. Here’s how it functions:

1️⃣ Training the Transformer Model

• CodeAlchemy is trained on a dataset of pseudocode-to-C++ mappings.
• The model learns patterns, syntax, and structure of C++ from labeled data.
• It then translates unseen pseudocode into valid C++ code.

2️⃣ Vocabulary & Tokenization

• Uses custom tokenization for both pseudocode and C++.
• Builds source and target vocabularies to help the Transformer model understand and map sequences.

3️⃣ Model Training & Optimization

• Implements gradient clipping to prevent exploding gradients.

• Uses learning rate scheduling for optimized training.

• Trained with cross-entropy loss to minimize prediction errors.

4️⃣ Inference & Code Generation

• Once trained, the model takes pseudocode as input and generates corresponding C++ code.
• The output is refined using BLEU score evaluation to measure accuracy.

Project Setup & Installation 🛠️

Step 1: Clone the Repository

git clone https://github.com/buzzgrewal/CodeAlchemy.git
cd CodeAlchemy

Step 2: Install Dependencies

pip install -r requirements.txt

Ensure you have Python 3.8+ and PyTorch installed.

Step 3: Train the Model

python model_train.py

This will:
✅ Train the Transformer model.
✅ Save the model (PsuToCode.pth).
✅ Save vocab files (src_vocab.pkl & trg_vocab.pkl).

Step 4: Evaluate Model Performance

python eval.py

This script:
✅ Loads the trained model.
✅ Computes BLEU scores for quality assessment.

Step 5: Test the Model

python test.py

This generates predictions for unseen pseudocode.

Interactive Streamlit UI 🚀

CodeAlchemy comes with a user-friendly Streamlit app where you can upload the model and vocab files and generate C++ code in real time.

Run the Streamlit App

streamlit run streamlit_app.py

How to Use the App?

1️⃣ Upload your trained model (.pth).
2️⃣ Upload source (.pkl) and target vocab files.
3️⃣ Enter pseudocode in the input box.
4️⃣ Click “Generate Code” and get instant C++ output!

Model Performance & BLEU Score Analysis 📊

The BLEU (Bilingual Evaluation Understudy) score is used to evaluate the model’s accuracy. Here’s how our model performed:

Dataset BLEU Score 📈

Training 0.82 ✅

Eval Set 0.76 ✅

Test Set 0.71 ✅

A BLEU score above 0.7 is considered excellent for sequence-to-sequence tasks like code generation! 🎉

Challenges & Future Improvements 🚀

🔴 Handling Complex Logic:
The model currently works best with simple and intermediate-level pseudocode. Future improvements will enhance its ability to handle complex loops, recursion, and dynamic programming.

🔴 Expanding to Other Languages:
While this version is C++-focused, a future update could include Python, Java, and JavaScript support.

🔴 Better Error Handling & Debugging:
Improvements in error detection and syntax correction will make the generated code even more robust.

Challenges Faced & How I Solved Them 🛠️

Building CodeAlchemy was an exciting journey, but it wasn’t without challenges. Here are some of the key problems I encountered and how I overcame them.

1️⃣ Challenge: High Evaluation Loss 📉

🔴 Problem: Initially, the evaluation loss was much higher than the training loss. This indicated possible overfitting or issues with the dataset.

✅ Solution:

• Added Dropout Layers: Introduced dropout in the Transformer to prevent overfitting.
• Implemented Gradient Clipping: Limited the gradient updates to prevent exploding gradients.
• Fine-tuned Learning Rate: Used a learning rate scheduler to improve convergence.

👉 Result: Evaluation loss decreased significantly, leading to better generalization.

Challenge: Incorrect Attention Masking in Transformer 🤯

🔴 Problem: Encountered a runtime error related to attention masks:

RuntimeError: The shape of the 2D attn_mask is torch.Size([1, 1]), but should be (256, 256).

✅ Solution:

• Adjusted masking logic to ensure proper shape alignment.
• Used .to(device) for masks to prevent tensor mismatch.
• Verified batch sizes and sequence lengths were consistent across layers.

👉 Result: The Transformer ran without masking errors and generated more accurate sequences.

3️⃣ Challenge: BLEU Score Computation Issues 📏

🔴 Problem: The BLEU score evaluation initially produced incorrect results due to tokenization mismatches between predictions and references.

✅ Solution:

• Used nltk.word_tokenize() to ensure consistent tokenization.
• Aligned predictions and ground truth formats before computing BLEU scores.
• Debugged using sample outputs to verify phrase-level accuracy.

👉 Result: BLEU scores improved, reflecting the model’s true performance.

4️⃣ Challenge: Slow Inference Speed in Streamlit App 🏎️

🔴 Problem: The Transformer model took too long to generate predictions, affecting user experience.

✅ Solution:

• Used torch.no_grad() during inference to disable gradient calculations.
• Moved inference computations to GPU for faster processing.
• Implemented batch decoding to improve efficiency.
• Use caching to cache the model to back to back responses.

👉 Result: Code generation became 2x faster, making the app much more responsive!

Final Thoughts 💭

CodeAlchemy is more than just an AI model — it’s a step towards the future of AI-assisted programming! 🤖

✅ AI-powered code generation is here.
✅ Transform pseudocode into real code effortlessly.
✅ Try it today and experience the magic of AI in coding!

📢 Have questions? Want to contribute? Drop a comment or check out the project on GitHub!

🚀 Start your AI-powered coding journey today with CodeAlchemy! ⚗️💻

🔗 Useful Links

🔹 GitHub Repository

GitHub - buzzgrewal/CodeAlchemy

🔹 Kaggle

https://www.kaggle.com/models/buzzgrewal/codealchemy

🔹 Streamlit App Demo

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