YouTube Spam Detection with python

Overview

YouTube Spam Detection

This code deletes spam comment on youtube videos based on two characteristics (currently)

  • If the author of the comment has a sexually explicit profile picture (credits: nudenet)
  • If the comment is a duplicate of someone else's comment who has commented it earlier

API Key

  • Follow the instructions to create a google app in google developer console and get an API_KEY for that app. The google account you use for this process must be the same as the owner of the youtube channel in order to have to permissions to delete a comment.

Arguments

  • video_id: Part of the youtube video url between "v=" and "&" (if present)
  • dry_run: Either true or false. If true it won't delete comments and just prints their ids
  • api_key: You can either put the key in command line or use the instruction in following sections to keep it safer.

Usage

Hard Way

  • You can either create a file in inner "youtube_spam_detection" folder named ".env" and put that API_KEY in it like api_key=blablabla or just put it in make run.
  • Create a python virtual environment with virtualenv venv --python=python3
  • Activate the venv source venv/bin/activate
  • Install dependencies make deps
  • Run the spam detection make run video_id dry_run api_key.

Docker (Easy Way)

  • Make sure you have docker installed and running.
  • You can either create a docker secret and put the api_key in it or pass it in the command below.
  • Run the code with docker run -it mrtaalebi/youtube-spam-detection:latest video_id dry_run api_key.

Docker Image

Owner
MohamadReza Taalebi
Computer Engineering student at Sharif University of Technology
MohamadReza Taalebi
Katana project is a template for ASAP 🚀 ML application deployment

Katana project is a FastAPI template for ASAP 🚀 ML API deployment

Mohammad Shahebaz 100 Dec 26, 2022
A handy tool for common machine learning models' hyper-parameter tuning.

Common machine learning models' hyperparameter tuning This repo is for a collection of hyper-parameter tuning for "common" machine learning models, in

Kevin Hu 2 Jan 27, 2022
fMRIprep Pipeline To Machine Learning

fMRIprep Pipeline To Machine Learning(Demo) 所有配置均在config.py文件下定义 前置环境(lilab) 各个节点均安装docker,并有fmripre的镜像 可以使用conda中的base环境(相应的第三份包之后更新) 1. fmriprep scr

Alien 3 Mar 08, 2022
Painless Machine Learning for python based on scikit-learn

PlainML Painless Machine Learning Library for python based on scikit-learn. Install pip install plainml Example from plainml import KnnModel, load_ir

1 Aug 06, 2022
EbookMLCB - ebook Machine Learning cơ bản

Mã nguồn cuốn ebook "Machine Learning cơ bản", Vũ Hữu Tiệp. ebook Machine Learning cơ bản pdf-black_white, pdf-color. Mọi hình thức sao chép, in ấn đề

943 Jan 02, 2023
Learning --> Numpy January 2022 - winter'22

Numerical-Python Numpy NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along

Shahzaneer Ahmed 0 Mar 12, 2022
Open source time series library for Python

PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array

Ross Taylor 2k Jan 02, 2023
Made in collaboration with Chris George for Art + ML Spring 2019.

Deepdream Eyes Made in collaboration with Chris George for Art + ML Spring 2019.

Francisco Cabrera 1 Jan 12, 2022
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Jan 03, 2023
Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

DIAL | Notre Dame 220 Dec 13, 2022
Fit interpretable models. Explain blackbox machine learning.

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

InterpretML 5.2k Jan 09, 2023
A GitHub action that suggests type annotations for Python using machine learning.

Typilus: Suggest Python Type Annotations A GitHub action that suggests type annotations for Python using machine learning. This action makes suggestio

40 Sep 18, 2022
PLUR is a collection of source code datasets suitable for graph-based machine learning.

PLUR (Programming-Language Understanding and Repair) is a collection of source code datasets suitable for graph-based machine learning. We provide scripts for downloading, processing, and loading the

Google Research 76 Nov 25, 2022
Machine Learning from Scratch

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

ShawnWang 0 Jul 05, 2022
Simple Machine Learning Tool Kit

Getting started smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during data preparation testing your model The g

Alessandra Bilardi 1 Dec 30, 2021
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Firebase + Cloudrun + Machine learning

A simple end to end consumer lending decision engine powered by Google Cloud Platform (firebase hosting and cloudrun)

Emmanuel Ogunwede 8 Aug 16, 2022
Self Organising Map (SOM) for clustering of atomistic samples through unsupervised learning.

Self Organising Map for Clustering of Atomistic Samples - V2 Description Self Organising Map (also known as Kohonen Network) implemented in Python for

Franco Aquistapace 0 Nov 16, 2021
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023
Accelerating model creation and evaluation.

EmeraldML A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various mo

Yusuf 0 Dec 06, 2021