CogniGuard 🕵️‍♂️✨

Overview

CogniGuard is a powerful web extension designed to empower users by identifying and combatting dark patterns on various websites, particularly focusing on E-commerce platforms. Ensuring users a transparent and ethical online experience.

About Dark Patterns => https://www.deceptive.design/

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Cogni-BERT Model Scores

Sweeps Training Hyperparametrs

Project Setup Locally 🚀

Link of Cogni-BERT Trained Model

1. Huggingface spaces link --> https://huggingface.co/spaces/4darsh-Dev/dark_pattern_detector_app/tree/main/models

Setting up Django Backend

1. clone the git repository locally.

git clone https://github.com/4darsh-Dev/CogniGaurd.git

2. Install python and setup virtual envionment.

1. Open terminal

pip install virtualenv 

cd CogniGaurd

cd api

python -m venv myenv 

Activating virtual environment named as myenv

1. In Windows 🪟

.\myenv\Scripts\activate  

2. In Linux/Mac 🐧

 source myenv/bin/activate

3. Installing required modules and libraries

 pip install -r requirements.txt 

4. Running Django Development Server

python manage.py makemigrations 

python manage.py migrate 

python manage.py runserver 

-- Server will be started at localhost (example: http://127.0.0.1:8000/)

Setting up CogniGuard Web Extension

1. Open Google Chrome Browser and visit url

 chrome://extensions/ 

2. Turn on Developer Mode.
3. Click on load unpacked and then select the cogniguard-web folder with manifest.json
4. Click on extension icon and you will find the CogniGuard.
5. Open the desired website URL (https://snapdeal.com/) on web browser and then click on Analyze button.
6. The Analyzing process will start running on backend.

Tech Stack 🛠️

• Web Extension: HTML, CSS, JavaScript
• Python (BeautifulSoup, Scrapy): Web scraping for price data analysis.
• Django: Backend for API management and Dark pattern report pattern for CogniGuard
• BERT Model: Fine-tuned for sophisticated pattern recognition.

Screenshots 📸

[Include screenshots of the extension interface in action.] coming soon.

Documentation 📖

Detailed documentation on usage, contribution guidelines, and API integration can be found in the Documentation Link.

Contributors 🧑‍💻

• @4darsh-Dev (Adarsh Maurya) - Project Lead

• @amansingh494 (Aman Singh) - FrontEnd Developer

• @Anmolgoel29 (Anmol Goel) - Machine Learning

• @DharmeshTanwar56 (Dharmesh Tanwar) - UI/UX Designer

• @goldy-dev123 (Goldy) - Technical Writing

Acknowledgments 🙏

We express our gratitude to the incredible individuals who have contributed to the development and success of CogniGuard. 🌟 Your dedication, passion, and insights have played a pivotal role in shaping this project.

Special thanks to the open-source community for their continuous support and collaborative spirit. 🚀 Your contributions, whether big or small, have contributed to the growth and improvement of CogniGuard.

Feedback 📬

We value your feedback! Report issues at adarsh@onionreads.com Propose features, or submit pull requests. Let's create a fair and transparent digital environment together! 🌐✨

Don't forget to leave a star ⭐ Happy Coding!!❤️

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