• This is the detection part of the prototype phase carbon eater.

• Humanity tries to achieve carbon neutrality by reducing the amount of carbon in the atmosphere, but unfortunately, every essential basic need whether going to grocery store or school, leaves behind a carbon footprint

• To make our earth a better place to live, we will utilize the knowledge of colloidal particles and the new advanced technology of machine learning and artificial intelligence to capture carbon emissions from the air.

• Abhigyan Pal - Hardware and Python
• Abhilash Gaurav - Python and Machine Learning
• Rajnikant dash - Full Stack Developer(Web)
• Mohit Kumar - Hardware and Web

Step-1 : Clone the Repository

git clone https://github.com/Hackdata2024/29-teamR.git
cd 29-teamR

Step-2 :

Run the Primary streamlit app

Note: Python is the Prerequisite

• Install essential libraries and packages:

pip install -r requirements.txt

• Run demo:

streamlit run future.py --server.maxUploadSize=700

• Project Demonstration

Run the Alert app

Note: Node is the Prerequisite

cd web-app # change the directory
npm install # install all the dependencies
npm run dev # run the app default on 3000

Use of Machine learning Tools and python Framework inorder to contribute to reducing pollution and managing the pollution increase rate in the best possible manner. Integrated with React App and machine learning.

1. Abhilash Gaurav "Machine Learning and Streamlit WebApplication"
2. Abhigyan Pal "Leading the team doing all documentation part. Heavily deployed on hardware setting up the hardware in-order to receive the signal from software unit."
3. Mohit Kumar "He worked on the hardware integration of components."
4. Rajnikant "Build the web application for the alert"

At last we enjoyed the RED BULLS "GIVE you a WINGS"