Update README.md

README.md

@@ -1,103 +1,185 @@
----
-license: apache-2.0
-datasets:
-- CM/codexglue_code2text_python
-- CM/codexglue_code2text_javascript
-language:
-- en
-metrics:
-- code_eval
-library_name: adapter-transformers
-tags:
-- code
----
+The error you are encountering is due to the code block within the folder structure section. The Markdown parser might be getting confused by the indentation or the format. Let's format the `README.md` properly to avoid YAML parsing errors.
+
+Here is the corrected `README.md`:
+
+```markdown
+# 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
+
+```plaintext
+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"
+  }
+}
+```
 
-# Model Card for dnnsdunca/ddroidlabs-GPT-2
+**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"}}'
+```
 
-<!-- Provide a quick summary of what the model is/does. -->
-This model is based on GPT-2 and has been fine-tuned to generate text based on specific prompts. It is intended for use in generating creative writing, story generation, or any application requiring coherent and contextually relevant text output.
+## Agent Descriptions
 
-This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+### 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.
 
-## Model Details
+### Database Agent
+- **File:** `agents/database_agent.py`
+- **Responsibilities:** Database design, query optimization, data migration.
 
-### Model Description
+### DevOps Agent
+- **File:** `agents/devops_agent.py`
+- **Responsibilities:** CI/CD pipelines, server management, deployment automation.
 
-<!-- Provide a longer summary of what this model is. -->
-The `dnnsdunca/ddroidlabs-GPT-2` model is a fine-tuned version of the GPT-2 model designed for generating high-quality text. It can be used in various applications requiring natural language generation.
+### 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
 
-- **Developed by:** [Your Name or Team]
-- **Funded by [optional]:** [Funding Source]
-- **Shared by [optional]:** [Your Name or Team]
-- **Model type:** GPT-2 (Generative Pre-trained Transformer 2)
-- **Language(s) (NLP):** English
-- **License:** [License Information]
-- **Finetuned from model [optional]:** [Base model used for fine-tuning]
+### Dataset File
+- **File:** `dataset/code_finetune_dataset.json`
+- **Description:** Contains examples of various coding tasks to fine-tune the model for development-related tasks.
 
-### Model Sources [optional]
+## Contributing
 
-<!-- Provide the basic links for the model. -->
-- **Repository:** https://huggingface.co/Dnnsdunca/ddroidlabs-GPT-2-usage
-- **Paper [optional]:** [Link to any relevant paper]
-- **Demo [optional]:** [Link to any demo]
+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.
 
-## Uses
+## License
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This project is licensed under the MIT License.
 
-### Direct Use
+## Contact
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-This model can be used directly for generating text based on given prompts. Examples include story generation, creative writing, and dialogue generation.
+For any questions or issues, please open an issue on GitHub or contact the repository maintainer.
+```
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-This model can be fine-tuned further for specific tasks such as generating technical documentation, personalized content, or any other application requiring specific text generation.
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-The model should not be used for generating harmful or malicious content, including but not limited to fake news, hate speech, or any form of content intended to deceive or harm individuals.
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-The model inherits biases from the data it was trained on. Users should be aware of potential biases in the generated text and use the model responsibly.
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Check if CUDA is available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Define Model and Tokenizer
-model_name = "dnnsdunca/ddroidlabs-GPT-2"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
-
-# Function to generate text based on a given prompt
-def generate_text(prompt, max_length=100):
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    
-    with torch.no_grad():
-        outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1)
-    
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated_text
-
-# Test the System
-if __name__ == "__main__":
-    prompt = "Once upon a time"
-    generated_text = generate_text(prompt)
-    print("Generated Text:\n", generated_text)
+This updated `README.md` should be free of YAML parsing errors and provides a comprehensive guide for setting up and running the Mixture of Agents Model.