mathanamathav/Trust-Me-Bro

"Trust-Me-Bro" is webapp which provides an analysis based on the comment section of the video. It tries to give a final solution whether the content or information said in the video is legit or not based on the comments provided for that video.

Trust-Me-Bro

Trust me, bro, is a simple and fun project that aims to eliminate YouTube videos that provide wrong information to users. It relies solely on user comments as input and does not currently take the entire video into consideration. The project's objective is to predict the validity of the video based solely on comments.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Contributing
4. License

About The Project

Based on the comments of the video it provides a Boolean Result whether the video is Legit(Valid) or Not(Invalid).

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Built With

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Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Ensure that you have python and pip installed on your computer

  • python

    python --version

  • pip

    pip --version

• For configuring Youtube API you can refer the documentation here

• After obtaining your API key create a ".env" file in your Project directory and paste "YOUTUBE_API=<your obtained key here>".

• Also create a variable "DEBUG=1" in the ".env" file to work in your Local / Development environment.

Installation

1. Install pipenv using pip

   pip install pipenv

2. Clone the repo

   git clone https://github.com/drMy5tery/Trust-Me-Bro

3. Install required dependencies using pipenv(navigate to the pipfile folder)

   pipenv install

4. Activate the virtual environment

   pipenv shell

5. Ensure that your python interpreter path for your current working project is set to the virtual environment

   python -c "import sys; print(sys.executable)"

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have suggestions for improving the method using better ML models, please reach out to us via email or through the GitHub repository's issues or pull requests. We welcome valid ideas and will consider integrating them into the main project. Please keep in mind that we prefer to utilize free tools and technologies whenever possible.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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