AgenticDeveloper Wiki

Contents

• Introduction
• Project Structure
• Setup Instructions
• Usage
• Contribution Guidelines
• Future Work
• References

Introduction

Project Structure

• agent.py: Defines the PrimeAgent and SecondaryAgent classes.
• app.py: Main entry point for running the Gradio interface.
• network.py: Contains functions to create neural network models for text processing and the gating network.
• requirements.txt: Lists the dependencies required for the project.
• train.py: Implements the training and evaluation pipeline.
• interface.py: Sets up the Gradio interface for the chat window and code display.
• README.md: Project documentation and setup instructions.

Setup Instructions

1. Clone the repository:
   sh

   1git clone https://github.com/Dnnsdesigns/AgenticDeveloper.git 2cd AgenticDeveloper
2. Create a virtual environment (optional but recommended):
   sh

   1python -m venv venv 2source venv/bin/activate # On Windows use `venv\Scripts\activate`
3. Install the required dependencies:
   pip install -r requirements.txt
4. Run the application:
   python app.py

Usage

1. Chat Window: Where you can interact with the assistant to get code suggestions, debugging help, and more.
2. Code Display Window: Displays the code snippets generated by the assistant.

Contribution Guidelines

1. Fork the repository: Create a fork of the repository on your GitHub account.
2. Clone your fork: Clone the forked repository to your local machine.
   sh

   1git clone https://github.com/YOUR_USERNAME/AgenticDeveloper.git 2cd AgenticDeveloper
3. Create a new branch: Create a new branch for your feature or bugfix.
   git checkout -b feature/your-feature-name
4. Make your changes: Implement your feature or bugfix.
5. Commit your changes: Commit your changes with a descriptive commit message.
   git commit -m "Add feature/your-feature-name"
6. Push your changes: Push your changes to your forked repository.
   git push origin feature/your-feature-name
7. Create a Pull Request: Create a pull request from your forked repository to the main repository.

Future Work

• Expand to Multimodal Inputs: Add support for image and audio inputs to provide more comprehensive assistance.
• Improve NLP Capabilities: Enhance the natural language processing capabilities of the assistant.
• Add More Specialized Agents: Introduce more secondary agents with specialized skills.
• Optimize Performance: Improve the performance and efficiency of the model.

References

• Hugging Face Datasets
• Gradio Documentation
• TensorFlow Documentation