Here's a GitHub README formatted in Markdown:
# Dark Pattern Detector
In response to the growing prevalence of deceptive online practices, we present a sophisticated browser extension tailored for detecting and mitigating dark patterns. Leveraging the robust Navies Bayes model trained on a comprehensive dataset sourced from KG Mathur, comprising over 11,000 websites, our solution is equipped to identify and classify 12 distinct types of dark patterns with precision.
A key feature of our extension is its seamless integration with Gemini AI, which provides nuanced insights into the specific dark patterns employed by websites. This dual-layered approach not only enhances detection accuracy but also empowers users with actionable intelligence to navigate the digital landscape securely.
Moreover, our extension offers an innovative functionality whereby users can effortlessly assess the legitimacy of a website in real-time. By simply clicking the 'detect' button, users receive instant notifications via SMS on their registered phone number, enabling informed decision-making and safeguarding against potential online threats.
Through rigorous validation and testing, our solution demonstrates efficacy in promoting transparency and enhancing user safety online, thereby contributing to a more trustworthy digital environment.
## Built With
- **Languages**:
- Python: For developing the machine learning model and backend functionality.
- JavaScript: For building the browser extension and handling user interactions.
- HTML/CSS: For the frontend of the browser extension.
- **Frameworks and Libraries**:
- Flask: For creating the backend server to handle requests and run the machine learning model.
- Scikit-learn: For implementing and training the Naive Bayes model.
- Gemini AI: For integrating advanced AI capabilities to enhance detection accuracy.
- Twilio API: For sending real-time SMS notifications to users.
- **Platforms and Cloud Services**:
- GitHub: For version control and project repository.
- Heroku: For deploying the backend server (if needed).
- **Databases**:
- SQLite: For storing user data and logs (optional, depending on the implementation).
- **APIs**:
- Twilio API: For sending SMS notifications to users.
- Gemini API: For enhancing detection capabilities with advanced AI insights.
## How to Run the Extension
### Clone the Repository
```bash
git clone https://github.com/SHLOK333/Dark-Pattern-Detector-.git
cd Dark-Pattern-Detector-Ensure you have Python and pip installed. Then, install the required packages:
pip install -r requirements.txt- Sign up for a Twilio account at Twilio.
- Get your API key and phone number from the Twilio console.
Create a .env file in the project root and add your Twilio credentials:
TWILIO_ACCOUNT_SID=your_account_sid
TWILIO_AUTH_TOKEN=your_auth_token
TWILIO_PHONE_NUMBER=your_twilio_phone_numberpython app.py- Open your browser’s extension management page.
- Enable "Developer mode."
- Click "Load unpacked" and select the directory where the project is located.
- Navigate to any website.
- Click the extension icon and press the 'Detect' button.
- Receive an SMS notification on your registered phone number with the detection results.
Feel free to reach out if you need further assistance running the extension or understanding its working.
This project is licensed under the MIT License - see the LICENSE file for details.
This README includes a comprehensive overview of the project, a list of technologies used, and detailed instructions on how to run the extension.