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.gitignore

@@ -1,162 +1,162 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Agents.py

@@ -1,17 +1,17 @@
-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)
+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)

Clone repo

@@ -1,2 +1,2 @@
-git clone https://github.com/your-repo/mixture_of_agents.git
-cd mixture_of_agents
+git clone https://github.com/your-repo/mixture_of_agents.git
+cd mixture_of_agents

LICENSE

@@ -1,201 +1,201 @@
-                                 Apache License
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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
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+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
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+      and conversions to other media types.
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+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
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+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
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+      on behalf of whom a Contribution has been received by Licensor and
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+      this License, each Contributor hereby grants to You a perpetual,
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+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
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+      this License, each Contributor hereby grants to You a perpetual,
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+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
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+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
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+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
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+          Derivative Works a copy of this License; and
+
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+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
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+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
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+          documentation, if provided along with the Derivative Works; or,
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+          wherever such third-party notices normally appear. The contents
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+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
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+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,191 +1,180 @@
----
-license: apache-2.0
-language:
-- en
-metrics:
-- code_eval
-library_name: adapter-transformers
-pipeline_tag: text-generation
-tags:
-- code
----
-# Ddroidlabs-Codex-Mini
-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.
+# 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.

Required Libraries.py

@@ -1 +1 @@
-pip install transformers datasets numpy pandas
+pip install transformers datasets numpy pandas

app.py

@@ -1,45 +1,45 @@
-from flask import Flask, request, jsonify
-from transformers import AutoModelForCausalLM, AutoTokenizer
-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
-
-app = Flask(__name__)
-
-# Load the model and tokenizer
-model_name = "gpt-3"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Initialize agents
-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)
-
-@app.route('/')
-def home():
-    return "Welcome to the Mixture of Agents Model API!"
-
-@app.route('/process', methods=['POST'])
-def process_task():
-    data = request.json
-    task_type = data.get('task_type')
-    task_data = data.get('task_data')
-    
-    if not task_type or not task_data:
-        return jsonify({"error": "task_type and task_data are required"}), 400
-
-    try:
-        result = integration_layer.process_task(task_type, task_data)
-        return jsonify({"result": result})
-    except ValueError as e:
-        return jsonify({"error": str(e)}), 400
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, request, jsonify
+from transformers import AutoModelForCausalLM, AutoTokenizer
+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
+
+app = Flask(__name__)
+
+# Load the model and tokenizer
+model_name = "gpt-3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Initialize agents
+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)
+
+@app.route('/')
+def home():
+    return "Welcome to the Mixture of Agents Model API!"
+
+@app.route('/process', methods=['POST'])
+def process_task():
+    data = request.json
+    task_type = data.get('task_type')
+    task_data = data.get('task_data')
+    
+    if not task_type or not task_data:
+        return jsonify({"error": "task_type and task_data are required"}), 400
+
+    try:
+        result = integration_layer.process_task(task_type, task_data)
+        return jsonify({"result": result})
+    except ValueError as e:
+        return jsonify({"error": str(e)}), 400
+
+if __name__ == '__main__':
+    app.run(debug=True)

back-end-agent.py

@@ -0,0 +1,10 @@
+# back_end_agent.py
+class BackEndAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

database-agent.py

@@ -0,0 +1,10 @@
+# database_agent.py
+class DatabaseAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

devops-agent.py

@@ -0,0 +1,10 @@
+# devops_agent.py
+class DevOpsAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

fine-tune-model.py

@@ -0,0 +1,30 @@
+# fine_tune_model.py
+from datasets import load_dataset
+from transformers import Trainer, TrainingArguments
+
+def fine_tune_model(model, tokenizer, dataset_path):
+    dataset = load_dataset('json', data_files=dataset_path)
+    
+    def preprocess_function(examples):
+        return tokenizer(examples['input'], truncation=True, padding=True)
+
+    tokenized_datasets = dataset.map(preprocess_function, batched=True)
+
+    training_args = TrainingArguments(
+        output_dir="./results",
+        evaluation_strategy="epoch",
+        learning_rate=2e-5,
+        per_device_train_batch_size=8,
+        per_device_eval_batch_size=8,
+        num_train_epochs=3,
+        weight_decay=0.01,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets['train'],
+        eval_dataset=tokenized_datasets['validation']
+    )
+
+    trainer.train()

front-end-agent.py

@@ -0,0 +1,10 @@
+# front_end_agent.py
+class FrontEndAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

integration-layer.py

@@ -0,0 +1,16 @@
+# integration_layer.py
+class IntegrationLayer:
+    def __init__(self, front_end_agent, back_end_agent, database_agent, devops_agent, project_management_agent):
+        self.agents = {
+            'front_end': front_end_agent,
+            'back_end': back_end_agent,
+            'database': database_agent,
+            'devops': devops_agent,
+            'project_management': project_management_agent
+        }
+
+    def process_task(self, task_type, task_data):
+        if task_type in self.agents:
+            return self.agents[task_type].process(task_data)
+        else:
+            raise ValueError("Unknown task type")

load-pretrained-model.py

@@ -0,0 +1,8 @@
+# load_pretrained_model.py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def load_model_and_tokenizer():
+    model_name = "gpt-3"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer

project structure.md

@@ -1,15 +1,19 @@
-mixture_of_agents/
-├── 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
+mixture_of_agents/
+├── README.md
+├── setup.sh
+├── requirements.txt
+├── 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

project-mgr-agent.py

@@ -0,0 +1,10 @@
+# project_management_agent.py
+class ProjectManagementAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

setup.sh

@@ -0,0 +1,230 @@
+#!/bin/bash
+
+# Variables
+REPO_URL="https://github.com/your-repo/mixture_of_agents.git"
+PROJECT_DIR="mixture_of_agents"
+PYTHON_VERSION="python3"
+VENV_DIR="venv"
+REQUIREMENTS_FILE="requirements.txt"
+
+# Clone the repository
+git clone $REPO_URL
+cd $PROJECT_DIR
+
+# Create a virtual environment
+$PYTHON_VERSION -m venv $VENV_DIR
+
+# Activate the virtual environment
+source $VENV_DIR/bin/activate
+
+# Create requirements.txt
+cat <<EOL > $REQUIREMENTS_FILE
+flask
+transformers
+datasets
+numpy
+pandas
+EOL
+
+# Install required libraries
+pip install -r $REQUIREMENTS_FILE
+
+# Create necessary directories
+mkdir -p agents integration model dataset
+
+# Create agent files
+cat <<EOL > agents/front_end_agent.py
+class FrontEndAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+EOL
+
+cat <<EOL > agents/back_end_agent.py
+class BackEndAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+EOL
+
+cat <<EOL > agents/database_agent.py
+class DatabaseAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+EOL
+
+cat <<EOL > agents/devops_agent.py
+class DevOpsAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+EOL
+
+cat <<EOL > agents/project_management_agent.py
+class ProjectManagementAgent:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def process(self, task_data):
+        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+EOL
+
+# Create integration layer
+cat <<EOL > integration/integration_layer.py
+class IntegrationLayer:
+    def __init__(self, front_end_agent, back_end_agent, database_agent, devops_agent, project_management_agent):
+        self.agents = {
+            'front_end': front_end_agent,
+            'back_end': back_end_agent,
+            'database': database_agent,
+            'devops': devops_agent,
+            'project_management': project_management_agent
+        }
+
+    def process_task(self, task_type, task_data):
+        if task_type in self.agents:
+            return self.agents[task_type].process(task_data)
+        else:
+            raise ValueError("Unknown task type")
+EOL
+
+# Create model files
+cat <<EOL > model/load_pretrained_model.py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def load_model_and_tokenizer():
+    model_name = "gpt-3"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer
+EOL
+
+cat <<EOL > model/fine_tune_model.py
+from datasets import load_dataset
+from transformers import Trainer, TrainingArguments
+
+def fine_tune_model(model, tokenizer, dataset_path):
+    dataset = load_dataset('json', data_files=dataset_path)
+    
+    def preprocess_function(examples):
+        return tokenizer(examples['input'], truncation=True, padding=True)
+
+    tokenized_datasets = dataset.map(preprocess_function, batched=True)
+
+    training_args = TrainingArguments(
+        output_dir="./results",
+        evaluation_strategy="epoch",
+        learning_rate=2e-5,
+        per_device_train_batch_size=8,
+        per_device_eval_batch_size=8,
+        num_train_epochs=3,
+        weight_decay=0.01,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets['train'],
+        eval_dataset=tokenized_datasets['validation']
+    )
+
+    trainer.train()
+EOL
+
+# Create dataset file
+cat <<EOL > dataset/code_finetune_dataset.json
+[
+  {
+    "task": "front_end",
+    "input": "Create a responsive HTML layout with CSS",
+    "output": "<!DOCTYPE html><html><head><style>body {margin: 0; padding: 0;}</style></head><body><div class='container'></div></body></html>"
+  },
+  {
+    "task": "back_end",
+    "input": "Develop a REST API endpoint in Node.js",
+    "output": "const express = require('express'); const app = express(); app.get('/api', (req, res) => res.send('Hello World!')); app.listen(3000);"
+  }
+]
+EOL
+
+# Create app.py
+cat <<EOL > app.py
+from flask import Flask, request, jsonify
+from transformers import AutoModelForCausalLM, AutoTokenizer
+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
+
+app = Flask(__name__)
+
+# Load the model and tokenizer
+model_name = "gpt-3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Initialize agents
+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)
+
+@app.route('/')
+def home():
+    return "Welcome to the Mixture of Agents Model API!"
+
+@app.route('/process', methods=['POST'])
+def process_task():
+    data = request.json
+    task_type = data.get('task_type')
+    task_data = data.get('task_data')
+    
+    if not task_type or not task_data:
+        return jsonify({"error": "task_type and task_data are required"}), 400
+
+    try:
+        result = integration_layer.process_task(task_type, task_data)
+        return jsonify({"result": result})
+    except ValueError as e:
+        return jsonify({"error": str(e)}), 400
+
+if __name__ == '__main__':
+    app.run(debug=True)
+EOL
+
+# Provide instructions for running the app
+echo -e "\nSetup complete. To run the application:\n"
+echo "1. Activate the virtual environment:"
+echo "   source $VENV_DIR/bin/activate"
+echo "2. Start the Flask application:"
+echo "   python app.py"
+chmod +x setup.sh
+./setup.sh

systemagents.py

@@ -1,72 +1,72 @@
-class FrontEndAgent:
-    def create_ui(self, requirements):
-        # Design UI based on requirements
-        pass
-
-    def develop_ui(self):
-        # Code the UI using HTML, CSS, JavaScript, React
-        pass
-
-class BackEndAgent:
-    def design_api(self, requirements):
-        # Design RESTful APIs
-        pass
-
-    def develop_backend(self):
-        # Implement server-side logic using Node.js, Django, etc.
-        pass
-
-class DatabaseAgent:
-    def design_schema(self, requirements):
-        # Design database schema
-        pass
-
-    def manage_database(self):
-        # Handle database operations
-        pass
-
-class DevOpsAgent:
-    def setup_ci_cd(self):
-        # Setup CI/CD pipelines
-        pass
-
-    def deploy_application(self):
-        # Automate deployment process
-        pass
-
-class ProjectManagementAgent:
-    def gather_requirements(self):
-        # Interact with client to gather requirements
-        pass
-
-    def manage_project(self):
-        # Oversee project progress and task assignment
-        pass
-
-class IntegrationLayer:
-    def integrate_agents(self):
-        # Ensure seamless communication between agents
-        pass
-
-# Main Workflow
-def main():
-    pm_agent = ProjectManagementAgent()
-    fe_agent = FrontEndAgent()
-    be_agent = BackEndAgent()
-    db_agent = DatabaseAgent()
-    devops_agent = DevOpsAgent()
-    integration_layer = IntegrationLayer()
-
-    requirements = pm_agent.gather_requirements()
-    fe_agent.create_ui(requirements)
-    fe_agent.develop_ui()
-    be_agent.design_api(requirements)
-    be_agent.develop_backend()
-    db_agent.design_schema(requirements)
-    db_agent.manage_database()
-    integration_layer.integrate_agents()
-    devops_agent.setup_ci_cd()
-    devops_agent.deploy_application()
-
-if __name__ == "__main__":
-    main()
+class FrontEndAgent:
+    def create_ui(self, requirements):
+        # Design UI based on requirements
+        pass
+
+    def develop_ui(self):
+        # Code the UI using HTML, CSS, JavaScript, React
+        pass
+
+class BackEndAgent:
+    def design_api(self, requirements):
+        # Design RESTful APIs
+        pass
+
+    def develop_backend(self):
+        # Implement server-side logic using Node.js, Django, etc.
+        pass
+
+class DatabaseAgent:
+    def design_schema(self, requirements):
+        # Design database schema
+        pass
+
+    def manage_database(self):
+        # Handle database operations
+        pass
+
+class DevOpsAgent:
+    def setup_ci_cd(self):
+        # Setup CI/CD pipelines
+        pass
+
+    def deploy_application(self):
+        # Automate deployment process
+        pass
+
+class ProjectManagementAgent:
+    def gather_requirements(self):
+        # Interact with client to gather requirements
+        pass
+
+    def manage_project(self):
+        # Oversee project progress and task assignment
+        pass
+
+class IntegrationLayer:
+    def integrate_agents(self):
+        # Ensure seamless communication between agents
+        pass
+
+# Main Workflow
+def main():
+    pm_agent = ProjectManagementAgent()
+    fe_agent = FrontEndAgent()
+    be_agent = BackEndAgent()
+    db_agent = DatabaseAgent()
+    devops_agent = DevOpsAgent()
+    integration_layer = IntegrationLayer()
+
+    requirements = pm_agent.gather_requirements()
+    fe_agent.create_ui(requirements)
+    fe_agent.develop_ui()
+    be_agent.design_api(requirements)
+    be_agent.develop_backend()
+    db_agent.design_schema(requirements)
+    db_agent.manage_database()
+    integration_layer.integrate_agents()
+    devops_agent.setup_ci_cd()
+    devops_agent.deploy_application()
+
+if __name__ == "__main__":
+    main()

unified model.py

@@ -1,57 +1,57 @@
-class UnifiedModel:
-    def __init__(self):
-        self.shared_parameters = self.initialize_shared_parameters()
-        self.front_end_agent = self.initialize_front_end_agent()
-        self.back_end_agent = self.initialize_back_end_agent()
-        self.database_agent = self.initialize_database_agent()
-        self.devops_agent = self.initialize_devops_agent()
-        self.project_management_agent = self.initialize_project_management_agent()
-        self.coordination_mechanism = self.initialize_coordination_mechanism()
-
-    def initialize_shared_parameters(self):
-        # Initialize shared parameters for general tasks
-        pass
-
-    def initialize_front_end_agent(self):
-        # Initialize parameters for front-end tasks
-        pass
-
-    def initialize_back_end_agent(self):
-        # Initialize parameters for back-end tasks
-        pass
-
-    def initialize_database_agent(self):
-        # Initialize parameters for database tasks
-        pass
-
-    def initialize_devops_agent(self):
-        # Initialize parameters for DevOps tasks
-        pass
-
-    def initialize_project_management_agent(self):
-        # Initialize parameters for project management tasks
-        pass
-
-    def initialize_coordination_mechanism(self):
-        # Initialize mechanism for coordinating between agents
-        pass
-
-    def process_task(self, task_type, task_data):
-        # Route the task to the appropriate agent
-        if task_type == 'front_end':
-            return self.front_end_agent.process(task_data)
-        elif task_type == 'back_end':
-            return self.back_end_agent.process(task_data)
-        elif task_type == 'database':
-            return self.database_agent.process(task_data)
-        elif task_type == 'devops':
-            return self.devops_agent.process(task_data)
-        elif task_type == 'project_management':
-            return self.project_management_agent.process(task_data)
-        else:
-            raise ValueError("Unknown task type")
-
-# Example usage
-model = UnifiedModel()
-result = model.process_task('front_end', {'html': '<div></div>'})
-print(result)
+class UnifiedModel:
+    def __init__(self):
+        self.shared_parameters = self.initialize_shared_parameters()
+        self.front_end_agent = self.initialize_front_end_agent()
+        self.back_end_agent = self.initialize_back_end_agent()
+        self.database_agent = self.initialize_database_agent()
+        self.devops_agent = self.initialize_devops_agent()
+        self.project_management_agent = self.initialize_project_management_agent()
+        self.coordination_mechanism = self.initialize_coordination_mechanism()
+
+    def initialize_shared_parameters(self):
+        # Initialize shared parameters for general tasks
+        pass
+
+    def initialize_front_end_agent(self):
+        # Initialize parameters for front-end tasks
+        pass
+
+    def initialize_back_end_agent(self):
+        # Initialize parameters for back-end tasks
+        pass
+
+    def initialize_database_agent(self):
+        # Initialize parameters for database tasks
+        pass
+
+    def initialize_devops_agent(self):
+        # Initialize parameters for DevOps tasks
+        pass
+
+    def initialize_project_management_agent(self):
+        # Initialize parameters for project management tasks
+        pass
+
+    def initialize_coordination_mechanism(self):
+        # Initialize mechanism for coordinating between agents
+        pass
+
+    def process_task(self, task_type, task_data):
+        # Route the task to the appropriate agent
+        if task_type == 'front_end':
+            return self.front_end_agent.process(task_data)
+        elif task_type == 'back_end':
+            return self.back_end_agent.process(task_data)
+        elif task_type == 'database':
+            return self.database_agent.process(task_data)
+        elif task_type == 'devops':
+            return self.devops_agent.process(task_data)
+        elif task_type == 'project_management':
+            return self.project_management_agent.process(task_data)
+        else:
+            raise ValueError("Unknown task type")
+
+# Example usage
+model = UnifiedModel()
+result = model.process_task('front_end', {'html': '<div></div>'})
+print(result)