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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/
β”œβ”€β”€ app.py
β”œβ”€β”€ colab_notebook.ipynb
β”œβ”€β”€ dataset/
β”‚   └── code_finetune_dataset.json
β”œβ”€β”€ agents/
β”‚   β”œβ”€β”€ front_end_agent.py
β”‚   β”œβ”€β”€ back_end_agent.py
β”‚   β”œβ”€β”€ database_agent.py
β”‚   β”œβ”€β”€ devops_agent.py
β”‚   └── project_management_agent.py
β”œβ”€β”€ integration/
β”‚   └── integration_layer.py
└── model/
    β”œβ”€β”€ 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

  1. Clone the Repository:

    git clone https://github.com/your-repo/mixture_of_agents.git
    cd mixture_of_agents
    
  2. Install Required Libraries:

    pip install -r requirements.txt
    
  3. Upload to Google Drive:

    • Upload the mixture_of_agents folder to your Google Drive.
  4. Open Colab Notebook:

    • Open colab_notebook.ipynb in Google Colab.

Running the Model

  1. 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')
      
  2. Install Necessary Packages:

    • Install the required packages in the Colab environment:
      !pip install transformers datasets
      
  3. 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')
      
  4. 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

  1. 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.
  2. Run the Application:

    • Execute the app.py script to start the Flask web server:
      python app.py
      
  3. 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.

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