COptimize 🚀🔥⚡

COptimize is a web-based tool that analyzes C code and suggests the best compiler optimization flag based on extracted features. The project leverages machine learning to predict the most suitable optimization setting, with a focus on reducing execution time by recommending flags such as -O0, -O1, -O2, -O3, and -Ofast for performance enhancement. 🎯📊💡

Live Website 🌍🔗✨

The application is hosted at: COptimize

Features ⚙️📌🚀

• Upload C code as a file or paste it into a text box.
• Extracts static code features such as Lines of Code (LOC), loop counts, and conditionals.
• Uses a trained machine learning model to predict the best compiler optimization flag.
• Provides optimization recommendations instantly.

Tech Stack 🖥️🔬💻

• Backend: Flask
• Machine Learning: scikit-learn (Random Forest Classifier)
• Frontend: HTML, CSS, JavaScript (via templates)
• Deployment: Render.com

Installation 🛠️📦⚡

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Swoyamjeetcodes/coptimize.git
   cd coptimize

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask application:

   python app.py

5. Open the application in your browser at:

   http://localhost:5000
   

Project Structure 📂📝📁

├── app.py             # Flask application
├── model.py           # Machine learning model for optimization
├── features.csv       # Training dataset
├── requirements.txt   # Dependencies
├── templates/         # HTML templates
├── static/            # Static assets (CSS, JS)
└── uploads/           # Directory for uploaded C files

Usage 🎯🖥️🚀

1. Visit the hosted application or run it locally.
2. Upload a C file or enter code manually.
3. Click submit to analyze the code.
4. The application will predict and display the best optimization flag.

Dependencies 📌📜💡

The required dependencies are listed in requirements.txt:

• Flask
• scikit-learn
• pycparser
• pandas
• numpy
• gunicorn

License 📜🔓✅

This project is licensed under the MIT License. 🎉⚖️📖