ForumIpsum is a web forum application designed and developed as part of the Big Data and Machine Learning elective course by a group of 4 students studying Applied Data Science at HAW Hamburg. The forum is unique in its use of OpenAI's GPT model for offensive speech detection and intentional misinformation detection, providing a safer and more reliable online community experience.
The application handles several essential features of a web forum, including:
- User authentication
- Post creation and deletion
- Post labelling
- Comments
- Database management (utilizing MongoDB)
Please note, as of this writing, the forum is still under development, and some features may be lacking.
- Backend: Python Flask
- Frontend: JavaScript, HTML/CSS
- Database: MongoDB
- AI Moderation: OpenAI's GPT-3.5 and GPT-4 model and a DNN (RNN and LSTM) Architecture trained on detecting real and fake news
The project is containerized using Docker, which handles the necessary dependencies.
Ensure you have Docker installed on your system and an OpenAI key with GPT-3.5 and GPT-4 access and credit.
For the AI moderation feature, you need to provide your OpenAI key. This can be set in an environment variable.
- Create a file named
.envin the root directory of the project. - Inside this file, add the following line, replacing your-key with your OpenAI key:
OPENAI_API_KEY=yourkey
- Clone the repository:
git clone https://github.com/MarkCarsonDev/forumipsum.git
- Navigate to the project directory:
cd forumipsum
- Build and run the Docker container:
docker build -t forumipsum .
docker compose up --build
The forum should now be accessible at localhost:5001.
This project was developed for a university course, and contributions are currently closed. We appreciate your interest and encourage you to explore and learn from the code. For any inquiries, please open an issue in this GitHub repository.
This project is licensed under the MIT License.
MIT License
Copyright (c) 2023 Mark Carson
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
We would like to express our gratitude to the instructors Professor Dr. Olaf Zukunf and Jakob Andersen of the Big Data and Machine Learning elective course at HAW Hamburg for their guidance and support.
For any inquiries, please open an issue in this GitHub repository.