DocuPy

Overview

DocuPy is an all-in-one tool designed to enhance productivity and streamline Python code workflows. With a user-friendly interface built using Gradio, DocuPy provides powerful utilities for documenting, testing, and converting Python code into C++. By leveraging state-of-the-art AI models like GPT, Claude, Gemini, and CodeQwen, this application offers developers advanced capabilities to:

1. Generate Docstrings and Comments: Automatically add clear, concise, and meaningful documentation and inline comments to your Python code.
2. Create Comprehensive Unit Tests: Quickly generate robust unit tests to ensure your code behaves as expected, helping maintain quality and reliability.
3. Convert Python Code to C++: Seamlessly translate Python code into C++ while providing options to execute and review the outputs directly within the app.

DocuPy's intuitive tab-based design ensures that each feature is accessible and straightforward to use. Developers can focus on coding while the app takes care of essential but time-consuming tasks like documentation, testing, and code translation. Whether you’re a beginner or an experienced developer, DocuPy is tailored to make your coding experience efficient and enjoyable.

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Features

1. Document Code with Docstrings and Comments
2. Generate Comprehensive Unit Tests
3. Convert Python Code to C++
4. Running Code Functions

⚠️ Important Warning

The functions that dynamically execute Python or C++ code provided as input are extremely powerful but also highly dangerous if the input code is not trusted.

Potential Risks:

• 🚫 Deleting files or directories: The code could remove important files or directories.
• 🕵️ Stealing sensitive data: Access to environment variables, credentials, or other private information.
• 💻 Running arbitrary commands: Malicious commands can be executed, compromising the system.

Recommendation: Only use this functionality in controlled environments and with trusted input.

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Setup

Clone the Repository

git clone https://github.com/emads22/DocuPy.git
cd DocuPy

Install Dependencies

• Navigate to the project directory and activate the provided Conda environment to ensure all dependencies are installed:

  conda env create -f .\docupy_env.yml
  conda activate docupy

• If you're running the Jupyter notebook docupy.ipynb inside the assets/study folder on Google Colab, make sure to install the required libraries by executing the following command:

  !pip install -q openai anthropic python-dotenv gradio huggingface_hub transformers

  You can access the notebook directly on Google Colab using the following link: docupy.ipynb

Set Up API Keys

DocuPy uses various AI models that require API keys. Follow these steps to configure them:

• Closed-Tier Models (GPT, Claude, Gemini): Obtain API keys for each model from their respective providers and add them to a .env file in the project directory.

  Example .env structure:

  OPENAI_API_KEY=your_openai_api_key
  ANTHROPIC_API_KEY=your_anthropic_api_key
  GOOGLE_API_KEY=your_google_api_key

• CodeQwen (Open-Source): Obtain a Hugging Face token by logging in to your Hugging Face account and generating an access token. Use this token to access a dedicated endpoint for the CodeQwen model.

  HF_TOKEN=your_huggingface_token
  CODE_QWEN_URL=your_hugging_face_open_source_model_dedicated_endpoint

Make sure to include these values in your .env file to ensure the application can access the required APIs.

Special Instructions for Colab Users

When running DocuPy on Colab, avoid storing API keys in a .env file. Instead, add them as Colab secrets for security.

Ensure all required environment variables are set before launching the app.

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Usage

Running the App

1. Navigate to the project directory.
2. Start the application by running:

   python app.py

3. The app will launch in your default web browser. If it does not open automatically, follow the URL provided in the terminal.

Using the Features

1. Document Code with Docstrings and Comments

• Navigate to the "Docstrings & Comments" tab.
• Paste your Python code into the Python Code textbox.
• Select a model (e.g., GPT, Claude, Gemini, CodeQwen) to process the code.
• Click the Add Docstrings & Comments button to generate the documented code.
• Review and copy the enhanced code from the output textbox.

2. Generate Comprehensive Unit Tests

• Open the "Unit Tests" tab.
• Paste your Python code into the Python Code textbox.
• Choose a model to generate the unit tests.
• Click the Generate Unit Tests button to view the generated test cases in the output textbox.

3. Convert Python Code to C++

• Go to the "Python to C++" tab.
• Paste your Python code into the Python Code textbox.
• Select a model to perform the conversion.
• Click the Convert to C++ button to generate the equivalent C++ code.
• Optionally, execute the generated Python or C++ code using the respective Run buttons and review the outputs in the result areas.

4. Additional Notes

• Ensure your API keys are set up before using closed-tier models.
• For best performance, choose the model that aligns with your requirements.
• For security, avoid running untrusted code using the execution features.

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Contributing

Contributions are welcome! Here are some ways you can contribute to the project:

• Report bugs and issues.
• Suggest new features or improvements.
• Submit pull requests with bug fixes or enhancements.

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Author

• Emad
  

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License

This project is licensed under the MIT License, which grants permission for free use, modification, distribution, and sublicense of the code, provided that the copyright notice (attributed to emads22) and permission notice are included in all copies or substantial portions of the software. This license is permissive and allows users to utilize the code for both commercial and non-commercial purposes.

Please see the LICENSE file for more details.