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---
license: apache-2.0
language:
- en
metrics:
- code_eval
library_name: adapter-transformers
pipeline_tag: text-generation
tags:
- code
---
# 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
1. **Clone the Repository:**
```bash
git clone https://github.com/your-repo/mixture_of_agents.git
cd mixture_of_agents
```
2. **Install Required Libraries:**
```bash
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:
```python
from google.colab import drive
drive.mount('/content/drive')
```
2. **Install Necessary Packages:**
- Install the required packages in the Colab environment:
```python
!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:
```python
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:
```python
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:
```bash
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:
```json
{
"task_type": "front_end",
"task_data": {
"task": "Create a responsive website layout"
}
}
```
**Using `curl`:**
```bash
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. |