lemniscate-sha-256 / metatron

Metatron - Advanced Multi-Platform Debugging Tool Metatron is an advanced, AI-powered debugging tool designed to analyze and debug code across various programming languages, including Python, JavaScript, Java, C++, AI frameworks (TensorFlow, PyTorch), and blockchain (Solidity).

Please, I am not a coder, just a passionate hobbyist, consider it will take me some time to do this all by myself, thanks a lot!

🧠 Metatron - Advanced Multi-Platform Debugging Tool Metatron is an advanced, AI-powered debugging tool designed to analyze and debug code across various programming languages, including Python, JavaScript, Java, C++, AI frameworks (TensorFlow, PyTorch), and blockchain (Solidity). Metatron features a beautiful, responsive user interface and supports multi-platform deployment on Windows, Linux, and the web.

🚀 Features !!!

🌐 Multi-Language Support: Supports various programming languages and frameworks.

🧠 AI-Powered Analysis: Utilizes advanced AI models for accurate error detection and debugging.

🔮 Quantum Computing Support: Visualize and simulate quantum circuits, with quantum-specific error detection and debugging.

🤝 Real-Time Collaboration: Collaborate on debugging sessions in real-time.

🔒 Secure and Private: Robust protection against web injection, buffer overflow, and other vulnerabilities.

✨ Beautiful and Responsive UI: Intuitive and modern UI design.

📖 Table of Contents

• Getting Started
• Prerequisites
• Installation
• Running the Application
• Directory Structure
• API Endpoints
• Quantum Computing Features
• Contributing
• License
• Contact

🛠 Getting Started

Follow these steps to set up and run Metatron on your local machine.

📋 Prerequisites

Node.js and npm Python 3.6 or later MySQL database Tor (for secure communication)

📦 Installation

1. Clone the repository git clone https://github.com/your-username/metatron.git cd metatron

2. Install dependencies

Electron and React dependencies: npm install

Python Flask dependencies: pip install flask flask_sqlalchemy flask_login werkzeug transformers requests[socks] flask-wtf fpdf stem openai qiskit pymysql matplotlib flask-cors

5. Configure the database Update the database configuration in app.py: app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://username:password@hostname/database'

6. Configure Tor Install Tor and configure it with a password:

sh Copy code sudo apt-get install tor sudo nano /etc/tor/torrc Add the following lines to the Tor configuration file:

plaintext Copy code ControlPort 9051 HashedControlPassword 16:YOUR_HASHED_PASSWORD CookieAuthentication 1 Restart Tor: sudo service tor restart

▶️ Running the Application

1. Start the Flask backend python app.py

2. Start the React frontend npm start

3. Package the Electron app (optional)

To build the Electron app for Windows: npm run package-win

To build the Electron app for Linux: npm run package-linux

📁 Directory Structure !!!

metatron/ ├── app.py # Flask backend

├── utils.py # Utility functions

├── main.js # Electron main process

├── preload.js # Electron preload script

├── package.json # Node.js dependencies and scripts

├── requirements.txt # Python dependencies

├── static/ # Static files (CSS, JS)

│ └── styles.css

├── templates/ # HTML templates

│   ├── index.html

│   ├── login.html

│   ├── register.html

│   ├── result.html

│   ├── debug.html

│   ├── sessions.html

│   ├── visualize_quantum.html

│   └── simulate_quantum.html

├── src/ # React frontend source

│ ├── App.js

│ └── App.css

└── README.md # Project documentation

🔗 API Endpoints

POST /analyze: Analyze code and return errors and suggestions.

POST /debug: Debug code and provide step-by-step debugging information.

POST /visualize_quantum: Visualize quantum circuits.

POST /simulate_quantum: Simulate quantum circuits.

POST /compare_versions: Compare two versions of code.

POST /refactor_code: Refactor code using AI suggestions.

⚛️ Quantum Computing Features !!!

Metatron includes advanced features specifically designed for quantum computing:

+++ Quantum Circuit Visualization: Provide visual representations of quantum circuits to help users understand the structure and flow of their quantum programs.

+++ Quantum Gate Error Detection: Implement advanced error detection for quantum gates and operations.

+++ Quantum Performance Metrics: Offer detailed performance metrics for quantum computations, such as gate fidelity and qubit coherence times.

+++ Integration with Quantum Cloud Services: Integrate with quantum cloud services like IBM Quantum Experience and Google Quantum AI to run quantum circuits on actual quantum processors.

+++ Quantum Algorithm Libraries: Provide a library of pre-built quantum algorithms for common tasks like Grover's search, Shor's algorithm, and quantum teleportation.

+++ Quantum Debugger: Develop a quantum-specific debugger that can simulate quantum circuits and highlight potential issues in the quantum code.

+++ Quantum Resource Estimation: Estimate the quantum resources required for a given computation, including the number of qubits and gates.

+++ Quantum Compiler Integration: Integrate with quantum compilers to optimize quantum circuits for specific quantum hardware.

+++ Quantum Hybrid Algorithms: Support hybrid quantum-classical algorithms that combine quantum circuits with classical computation.

+++ Quantum Error Mitigation: Implement techniques for quantum error mitigation to improve the accuracy of quantum computations.

🤝 Contributing !!!

We welcome contributions! Please follow these steps to contribute:

• Fork the repository.

• Create a new branch (git checkout -b feature-branch).

• Make your changes and commit them (git commit -m 'Add new feature').

• Push to the branch (git push origin feature-branch).

• Open a pull request.

📜 License !!!

This project is licensed under the MIT License. See the LICENSE file for details.

📞 Contact For questions or suggestions, please open an issue or contact us at [email protected].

metatron's Issues

"huggingface_model" is not defined

The error "huggingface_model" is not defined means that I have not defined the variable or function named huggingface_model in the code.

To fix this, i will need to:

Import the necessary library: Import the Hugging Face library or a specific model from that library. Research and install the relevant library if it's not already in the environment.

Initialize the model: Once imported, I'll need to initialize the specific Hugging Face model I want to use for code refactoring.

+++ So what is code refactoring ?

Code refactoring is the process of improving the internal structure, readability, and maintainability of a software codebase without altering its external behavior or functionality. This practice aims to enhance code quality and reduce technical debt by reorganizing, simplifying, or optimizing the code, making it more efficient, modular, and easier to understand for developers.

+++ To simplify things: I think I will just use CodeT5.

Introduced by Wang et al. in [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
CodeT5 is a Transformer-based model for code understanding and generation based on the T5 architecture.

(https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwj0ppGGnYGHAxXrUUEAHSf-B1MQFnoECBcQAQ&url=https%3A%2F%2Fpaperswithcode.com%2Fmethod%2Fcodet5&usg=AOvVaw3qvNBPha5mZnOK0jaKUddv&opi=89978449) ~ For Nerds

Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies.

T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text.

A Transformer pipeline describes the flow of data from origin systems to destination systems and defines how to transform the data along the way.

++ Import pipeline: We import the pipeline function from transformers to easily create a pipeline for code generation.

++ Initialize refactor_pipeline: We create a code-generation pipeline using the CodeT5 model (google/codet5-base).

++ Refactor the code: We pass the code and the language to the refactor_pipeline, which returns the refactored code.

Important:
We have to make sure the transformers library is installed. You can install it using pip install transformers.
I will add it to requirements.txt