Ddroidlabs-mixture-of-agents

small agentic model designed as a coding assistant

Mixture of Agents Model (MAM) - Full-Stack Development Team

Overview

The Mixture of Agents Model (MAM) is an AI-driven full-stack development team that integrates specialized agents for front-end development, back-end development, database management, DevOps, and project management. This unified model leverages a pretrained transformer and fine-tuned datasets to handle a variety of software development tasks efficiently.

Folder Structure

mixture_of_agents/
├── app.py
├── colab_notebook.ipynb
├── dataset/
│   └── code_finetune_dataset.json
├── agents/
│   ├── front_end_agent.py
│   ├── back_end_agent.py
│   ├── database_agent.py
│   ├── devops_agent.py
│   └── project_management_agent.py
├── integration/
│   └── integration_layer.py
└── model/
    ├── load_pretrained_model.py
    └── fine_tune_model.py

Setup Instructions

Prerequisites

Python 3.7 or higher
Flask
Google Colab account (for running the notebook)
Libraries: transformers, datasets, numpy, pandas

Installation

Clone the Repository:

git clone https://github.com/your-repo/mixture_of_agents.git
cd mixture_of_agents

Install Required Libraries:
```
pip install -r requirements.txt
```
Upload to Google Drive:
- Upload the mixture_of_agents folder to your Google Drive.
Open Colab Notebook:
- Open colab_notebook.ipynb in Google Colab.

Running the Model

Mount Google Drive:
- Mount your Google Drive in Colab by running the first cell of the notebook:
```
from google.colab import drive
drive.mount('/content/drive')
```
Install Necessary Packages:
- Install the required packages in the Colab environment:
```
!pip install transformers datasets
```

Load and Fine-Tune the Model:

Follow the steps in the Colab notebook to load the pretrained model and fine-tune it using the provided dataset:

from model.load_pretrained_model import load_model_and_tokenizer
model, tokenizer = load_model_and_tokenizer()

from model.fine_tune_model import fine_tune_model
fine_tune_model(model, tokenizer, '/content/drive/MyDrive/mixture_of_agents/dataset/code_finetune_dataset.json')

Initialize and Use the Agents:

Initialize the agents and use the integration layer to process tasks:

from agents.front_end_agent import FrontEndAgent
from agents.back_end_agent import BackEndAgent
from agents.database_agent import DatabaseAgent
from agents.devops_agent import DevOpsAgent
from agents.project_management_agent import ProjectManagementAgent
from integration.integration_layer import IntegrationLayer

front_end_agent = FrontEndAgent(model, tokenizer)
back_end_agent = BackEndAgent(model, tokenizer)
database_agent = DatabaseAgent(model, tokenizer)
devops_agent = DevOpsAgent(model, tokenizer)
project_management_agent = ProjectManagementAgent(model, tokenizer)
integration_layer = IntegrationLayer(front_end_agent, back_end_agent, database_agent, devops_agent, project_management_agent)

task_data = {'task': 'Create a responsive website layout'}
result = integration_layer.process_task('front_end', task_data)
print(result)

Running the Web Application

Ensure All Agent Files and Integration Layer Are Available:
- Make sure the agents and integration directories with their respective Python files (front_end_agent.py, back_end_agent.py, database_agent.py, devops_agent.py, project_management_agent.py, and integration_layer.py) are in the same directory as app.py.
Run the Application:
- Execute the app.py script to start the Flask web server:
```
python app.py
```
Using the API:
- Open your web browser and navigate to http://127.0.0.1:5000/ to see the welcome message.
- Use a tool like curl or Postman to send a POST request to the /process endpoint with JSON payload to process tasks.

Example POST Request

You can use the following example JSON payload to test the /process endpoint:

{
  "task_type": "front_end",
  "task_data": {
    "task": "Create a responsive website layout"
  }
}

Using curl:

curl -X POST http://127.0.0.1:5000/process -H "Content-Type: application/json" -d '{"task_type": "front_end", "task_data": {"task": "Create a responsive website layout"}}'

Agent Descriptions

Front-End Agent

File: agents/front_end_agent.py
Responsibilities: UI/UX design, HTML, CSS, JavaScript frameworks (React, Vue).

Back-End Agent

File: agents/back_end_agent.py
Responsibilities: Server-side logic, API development, frameworks like Node.js, Django.

Database Agent

File: agents/database_agent.py
Responsibilities: Database design, query optimization, data migration.

DevOps Agent

File: agents/devops_agent.py
Responsibilities: CI/CD pipelines, server management, deployment automation.

Project Management Agent

File: agents/project_management_agent.py
Responsibilities: Requirement gathering, task management, progress tracking.

Integration Layer

File: integration/integration_layer.py
Responsibilities: Ensures seamless communication and coordination between agents.

Fine-Tuning Dataset

Dataset File

File: dataset/code_finetune_dataset.json
Description: Contains examples of various coding tasks to fine-tune the model for development-related tasks.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure your code follows the project's style guidelines and includes appropriate tests.

License

This project is licensed under the apache-2.0 License.

Contact

For any questions or issues, please open an issue on GitHub or contact the repository maintainer.