Python

With this kit, you can

1. Use a pre-trained model for detecting fake news.

2. Train the model on your custom dataset.

3. Expose the fake news detection as an API

Add-on on examples are also included as given below

1. Use web scraper to automatically make your training dataset.

2. Visualise training and prediction data for useful insights.

Instructions to Run

Follow the below instructions to run the solution.

1. Locate and open the FakeNewsDetection-starter.ipynb notebook from the Jupyter Notebook browser window.

2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar

3. Once all the cells of the notebook are executed, the prediction result will be written to the file 'fake_news_test_output.csv'

Training with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. Add corresponding labels as 'real' or 'fake' denoting whether a news article is real or not.

3. You can refer to the file 'fake_news_train.csv' for an example.

4. Set the variable for training file in the notebook under Variables section.

Testing with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. You can refer to the file 'fake_news_test.csv' for an example.

3. Set the variable for testing file in the notebook under Variables section.

You can execute the cells of notebook by selecting Cell from the menu bar.

For any support, you can reach us at FAQ & Support

Libraries useful for this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools

2.While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.

3.During kit installer, if you encounter Windows security alert, click Allow.

4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Click here to install python

2. Click here to download the repository

3. Extract the zip file and navigate to the directory 'fakenews-detection-main'

4. Open terminal in the extracted directory 'fakenews-detection-main'

5. Install dependencies by executing the command 'pip install -r requirements.txt'

6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.

Support

For any support, you can reach us at FAQ & Support

The use case of AI Course Recommender System is to provide personalized recommendation to the user based on their interest, course they can take and their current knowledge. This system will be able to recommend course based on user’s interest, current knowledge, analytical view of students’ performance in mathematics and recommends if a student can consider math subject for his/ her higher education. The recommended course will be based on the information of user’s profile, analysis of grades of students, visualization of patterns, prediction of grade in final test, and some rules that were set by their instructor. Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn. kandi kit provides you with a fully deployable AI Course Recommender System. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Describe your solution here. e.g. Our solution is a Mental Health Monitor and Virtual Companion for students in the digital classroom. It acts as a virtual friend to the student and keeps her company during online classes. It also reminds her of class schedules, submissions, and other aspects of the classroom. It monitors for mental wellbeing vitals like class participation, connecting with friends, and sends motivational messages. In future versions, we can add connect with parents, teachers, and other stakeholders to address the well-being and academic excellence in a holistic manner. We have used the below techniques for our solution. 1. Machine learning to train for the virtual conversations for the virtual companion. 2. Data exploration to explore the academic and digital activity data to use in training for predicting behavior patterns

Machine Learning

You may be using multiple libraries for different functions in your solution. Please create one group for each function and add the libraries to each group. e.g. I have created a group for Machine Learning which has the libraries used in my solution. The below libraries helps in capturing the embeddings for the text. The embeddings are vectoral representation of text with their semantics.

Data Exploration

You may be using multiple libraries for different functions in your solution. Please create one group for each function and add the libraries to each group. e.g. I have created a group for the libraries used for Data Exploration in my solution. The data exploration helps in doing extensive analysis of different data types and in assisting to understand the patterns. Pandas is used in our solution for data manipulation and analysis.

We are AI-Wave team and we present Moody, a platform that allows us to monitor and assist student’s mental health through time, by using natural language processing and system recommendation algorithms, day to day student’s information, virtual assistant, emotional tracking and a real time dashboard. Website (MVP) : https://buildwithai-aiwave.netlify.app Solution info : https://docs.google.com/document/d/1AQqLaMC4d99qQ34ylzDyK2JHvoobNgjogHfQld03kQs/edit Youtube video: https://www.youtube.com/watch?v=kEx9w8sUhMA Github: https://github.com/shahbaazkyz/AI-Wave

Development Environment

We use Jupyter for development environment

Machine Learning

All the machine learning libraries used for the solution

Data Manipulation & Adquisition

All the libraries used for data adquisition and manipulation

MVP

The link to the website of the solution is : https://buildwithai-aiwave.netlify.app

This kit is created for Impact of COVID 19 on the mental health of students by team Phoenix

Development Environment

Library related to development and debugging environment

Machine Learning (overall implementation)

Library for using Machine Learning algorithm

Data Manipulation

Libraries for data manipulation

Data Visualization

Libraries for data visualization

This is the Online Class Companion Project by Team Geeky Plats

Development Environments

These are the development environments used for the project.

Libraries Used

The various libraries used in the project

Student well beings help us to predict mental disorder and parent satisfaction based on the the students information. Both are binary classification problem and we used to solved by traditional machine learning algorithms. This is the project from Team Neuron Fire.

Development Environment

We used Jupyter Notebooks by developing and debugging. Jupyter Notebook is a web based interactive environment often used for experiments.

Data Mining

We used pandas and numpy for Data Mining.

Data Visualizations

We used matplotlib and seaborn python libraries for data visualizations.

Machine learning

We used Sklearn python libraries for binary classification problem solve.

We are Team HungryHacks trying to solve food insecurity issues using Data Science and AI.

Group Name 1

Data Preprocessing

Group Name 2

Data Exploration and Visualization

Group Name 6

Front-end Development

Group Name 5

GITHUB Repo

This is the Project present by TeamAlphaPro

Development Environment

We used Jupiter Notebook for development and debugging

Data Mining

We used Numpy and Pandas for Data Mining

Machine Learning

We used scikit-learn for Machine Learning

Development Environment

Vs code : used to write python scripts for data preprocessing and deployment jupyter notebook : used for data analysis and model training streamlit : used for model deployment and web app creation heroku : used for deployment of our web app to make it accessible globally

Data Mining

numpy : used for computational mathematics Pandas : used for loading data and Exploratory data analysis

Data Visualization

Machine Learning

Sklearn : used for training our ML models and evaluation joblib : used to store and load our ML model

Build WIth AI 2021 Challenge 2 Kit Submission

Machine Learning

Libraries used for voice activity detection(VAD), embedding extraction and spectral embedding clustering.

Audio Processing

Libraries used for importing, converting, exporting, playing, and processing audio.

User Interface

Libraries used for user interface.

Miscellaneous

Other libraries used.

Data Summary : Resources that have been shared for the problem statement has info about food items and their description. Also we had order info from both donor and from consumer side orders on daily basis. We have done data cleaning and preprocessing as required. Recommendation system : In order to control the food wastage we have built Recommendation engine using "item-item based collaborative filtering" to recommend the items which expire early and are more in consumption. Data Analysis : We have developed a dashboard on tableau using cleaned datasets and these analysis can be used to match supply-demand of different types of food and to give an overview to the NGO, donors and consumers on how to reduce the food wastage. Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

analysis

machine learning

this is related to water treatment and purification

DataViz Group

This helps with DaaViz

Cyber Data Analytics Group

This is for Cyber Data Analytics Group

zeronet group

this is zeronet group

Automation Group

this is part of Automation Group

We can create any animation with suitable libraries or combinations of libraries in Python that are well-known for their functionalities and functionalities. For developers looking for options with less complex code with maximum customization options, users can customize their plots and designs depending on their preferences.   

Choosing a suitable library plays a key role in any machine learning or data science project, and we should do it properly to avoid other related issues which may arise. Some libraries offer an interactive plot to attract playing with the graph and visualize it uniquely. It will allow you to edit videos, create animations, and create a map or geological animations where we can analyze the geological data.  

Here is the list of handpicked 18 best Python Animation Libraries in 2023 which will help you with your animation requirements: 

manim - 3b1b: 

• Is a Python library for creating mathematical animations and educational videos that Grant Sanderson develops.  
• Is an open source library that allows users to create high-quality animations which visualize mathematical concepts like animations of graphs, functions, fractals, and more.   
• Uses Python code for creating animations which we can export to animated GIFs or video files.  

PythonRobotics: 

• Is a Python library for implementing different robotics simulations, visualizations, and algorithms. 
• Offers various resources and tools for robotics developers, like algorithms for path planning, localization, motion control, mapping, and many more.   
• Includes simulation environments like 2D and 3D simulators, that will allow developers to test their algorithms in virtual environments before deploying them on real robots.   

matplotlib: 

• Is a comprehensive library for creating animated, interactive visualizations and static in Python. 
• Produces publication-quality figures in different interactive environments and hardcopy formats across platforms. 
• Can be used in Python Scripts, web application servers, various graphical user interface toolkits, and Python/IPython shells.  

manim - ManimCommunity:  

• Is an animation engine for explanatory math videos used for programmatically creating precise animations. 
• Includes various tools for creating animations like support for vector graphics, 3D objects, and complex mathematical equations. 
• Also includes features for creating animations with custom fonts, styles, and colors.  

plotly.py: 

• Is a Python library used to create interactive data visualizations, built on the plotly JavaScript library that allows developers to create various interactive plots.   
• Is designed to be easy to use and includes different resources and tools for creating high-quality visualizations. 
• Includes support for complex data structures like pandas DataFrames and offers various customization options for fonts, styles, and colors.  

seaborn: 

• Is a Python data visualization library based on Matplotlib, offering a high-level interface to create attractive and informative statistical graphics.  
• Offers various plotting functions for visualizing various data types like continuous data, data distribution, and categorial data.  
• Its the ability to create visually appealing plots with minimal effort and supports the customization of plot elements like axes, titles, legends, and labels. 

moviepy: 

• Is a Python library for video editing, concatenations, cutting, video composting, title insertions, creation of custom effects, and video processing. 
• Has the ability to add audio to video clips easily and offers various filters and audio effects like changing pitch and speed, adding sound effects, and adjusting volume.  
• Includes support for creating animations like moving text, images, and shapes and allows users to export their video clips to different file formats. 

termtosvg: 

• Is a Python library that allows users to record terminal sessions and save them as SVG animations.  
• Produces clean-looking and lightweight still frames embeddable on the project page or animations.  
• Includes support for recording multiple terminal sessions, allowing users to control the size and speed of the resulting animation.   

altair: 

• Is a declarative statistical visualization library that can help you spend more time understanding your data and its meaning. 
• Offers a simple syntax for creating different visualizations, like line charts, histograms, scatterplots, and bar charts. 
• Its declarative syntax lets user's express visualizations as a series of high-level mappings between visual and data properties like color, size, and position. 

PathPlanning: 

• Is a Python library used for path and motion planning applications designed to be accessible to beginners and experts with a straightforward API. 
• Offers various algorithms for computing collision-free paths for drones, mobile robots, and manipulators in 2D and 3D environments. 
• Also offers tools for trajectory generation, motion control, and obstacle avoidance and supports simulation and visualization of robot motion. 

alive-progress: 

• Is a Python library for displaying spinners and progress bars in command-line applications designed to offer a customizable way of showing progress indicators for long-running processes or tasks. 
• Supports for pausing and resuming progress indicators, nested spinners, and progress bars.  
• Designed to be intuitive and simple with various default settings and a straightforward API for customizing the behavior and appearance of spinners and progress bars. 

asciimatics: 

• Is a package for helping people create full-screen text UIs on any platform and offers a single cross-platform Python class to do all the low-level console functions. 
• Includes cursor positioning, mouse input, screen scraping, colored/styled text, detecting and handling if the console resizes, and keyboard input like Unicode support. 
• Is a Python library for creating text-based animations and user interfaces in the terminal. 

pygal: 

• Is a Python library for creating interactive Scalable Vector Graphics (SVG) graphs and charts. 
• Offers various tools for generating customizable charts, graphs, and high-quality for use in presentations, reports, and web applications. 
• Includes built-in support for data/time axis labeling, responsive design, and integration with web frameworks and interactive charts elements.   

GANimation: 

• Is a Python implementation of the GANimation research project, which offers various tools for generating animations from still images using Generative Adversarial Networks (GANs).  
• Includes tools for augmenting and preprocessing input data, customizable GAN training parameters and architecture, and support for evaluating and visualizing GAN models.  
• Offers various tools for fine-tuning GAN models and generating high-quality animations for various applications. 

deep-motion-editing: 

• Offers advanced and fundamental functions to work with 3D character animations in deep learning with Pytorch. 
• Is a Python implementation of the research project of the same name, which offers tools for editing the motion of human characters in video sequences using deep learning methods.  
• Its ability to generate realistic, high-quality animations for various applications offers tools for fine-tuning the deep learning model and editing the generated motions to achieve the desired results. 

geoplotlib: 

• Is a Python library for creating geographical maps and visualizations and offers an easy-to-use interface for creating maps with different data types, like polygons, heatmaps, lines, and points. 
• Includes support for different tile providers and map projections, customizable styling options for data layers like size, transparency, and color. 
• Designed for creating interactive maps and visualizations and is suitable for various applications like data analysis, presentation, and exploration. 

Linux-Fake-Background-Webcam: 

• Is a Python library that will allow users to replace their webcam background with a custom video or image on Linux systems.  
• Works by creating a virtual webcam device that can be selected as the input source in video conferencing applications, allowing users to appear as if they are in various environments and locations.   
• Includes the ability to control the position and size of the custom background video or image and support for replacing the webcam background with a custom video or audio. 

celluloid: 

• Is a Python library that offers a simple interface for creating visualizations and animations in Matplotlib   
• Designed to make it easy for users to create animations without having to write to deal with low-level details and complex code.  
• Includes a simple interface for adding and updating data in the animation, the ability to save the animation as an MP4 or GIF video file, and support for customizing the animation style and appearance. 

Use these Python Machine Learning libraries for developing supervised and unsupervised learning algorithms, data pre-processing and feature extraction tools, deep learning frameworks and more.

Following are the top use cases of these shortlisted libraries for Python Machine Learning,

·        Pre-processing of data that includes data cleaning and feature engineering tasks such as normalization, imputation, missing value treatment, and outlier detection.

·        Model selecting and optimizing, such as cross-validation, hyperparameter tuning, and model selection metrics.

·        Visualizations to understand data and results. This includes visualizing data distributions, feature importance, and model performance.

·        Developing algorithms, including supervised learning algorithms (e.g. linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks) as well as unsupervised learning algorithms (e.g. clustering, dimensionality reduction, and anomaly detection).

·        Calculating performance metrics such as accuracy, precision, recall, and F1 score.

The following is a list of the 18 most popular open-source Python libraries for Machine Learning,

Dilbert was dropped from hundreds of newspapers over Scott Adams’ racist comments. Multiple researchers have documented over the past few months how ChatGPT can be prompted to provide racist responses.

A three-decade globally famous comic strip has been canceled because of the creator’s racist comments in his YouTube show. ChatGPT, Bing Bot, and many such AI Bots are conversing with millions of users daily and have been documented to provide misleading, inaccurate, and biased responses. How can we hold AI to the same high standards we expect from society, especially when AI is now generative and scaled for global consumer use?

While no silver bullet exists, multiple aspects can make AI more responsible. Having open AI models is a great start. Hugging Face, EleutherAI, and many others are championing an open approach to AI. Openness and collaboration can bring in diverse contributions, reviews, and rigorous testing of AI models and help reduce bias.

NIST’s AI risk management guidelines released recently provide a comprehensive view across the AI lifecycle consisting of collecting and processing Data & Input, the build, and validation of the AI model, its deployment, and monitoring in the context of usage. Acknowledging the possibility of bias, eliminating data capture biases, or unconscious biases when generating synthetic data, designing for counterfactual fairness, and human-in-loop designs can reduce the risk of bias.

Use the below tools for assessment and to improve the fairness and robustness of your models.

Use the below tools for Explainability, Interpretability, and Monitoring.

Google toolkit on Tensorflow for Privacy, Federated Learning, and Explainability.

SPEAKER COUNTING It enhances understanding through automatic speech recognition Beneficial for real - world applications like call-center transcription and meeting transcription analytics Speaker Diarization is a developing field of study, with new approaches being published on a frequent basis. The Problem Not many studies have been done for estimating a large number of speakers. Diarization becomes extremely difficult when the number of speakers is huge. Providing the number of speakers to the diarization system can be advantageous Complete solution Architecture - Machine Learning model - To predict the no. of speakers and the time stamps of the speaker. Web App - Frontend for the user to use the feature. Middleware Flask Api - To connect Frontend and ML Model. We have build a Web App that a user can use to communicate and leverage the advantages of the our Machine learning model. Since the model we build and the web app are build on different platforms, we used REST API as a middleware to connect frontend and model.

ML Model Solution Process

These are used to create our Web UI using node as backend and VueJs as front end. 1. Preprocessing: Denoising -> Speech separation 2. Embedding Extraction: YAMNet sound & classification model 3. Speaker Counting: Machine learning model selection -> Model training -> Model prediction

Data Preprocessing

Technologies used for pre processing the audio data.

Audio Pre Processing

The additional libaries are use to processing the audio which are needed to be fed into the classifier model.

Model Trainning

This libaries are used to create the two classifier models which are then both combined into one.

ChatGPT is creating huge interest across all fields, including application development. You can use ChatGPT in your applications to provide additional intelligence. Here are some cool applications you can leverage ChatGPT for.

Start with Andrej Karpathy’s NanoGPT to get a feel for how GPT works at a smaller scale, even in your laptop.

ChatGPT Prompts is a great repo to help you leverage ChatGPT personas such as a translator, interviewer, JavaScript console, UI developer, IT architect, Excel sheet, stand-up comedian, philosopher, plagiarism checker, doctor, financial analyst, and many more.

If you'd like to use ChatGPT on your desktop try ChatGPT by lencx.

Up your chat game with ChatGPT apps for WhatApp, Telegram and Discord.

If you would like to use ChatGPT to compare with Google results from your browser, or integrate ChatGPT into your IDE or a general-purpose API to integrate with any other application like Telegram and others to create your custom ChatGPT integrations, below are useful repositories. Try them to build and get familiar with ChatGPT use cases.

Here are the best open source Python Telegram Bot libraries for your application. You can use these components to build an intelligent telegram bot that can automatically or on request send text, video, images, documents, and other information and perform different activities. 

Using a Python Telegram Bot Library, you can Broadcast, Teach, Collect Leads, Search, Reply, Remind, Play, Connect, etc. To help developers, these bots act as a BotFather. Also, you can implement the pair-to-pair security protocol and end-to-end encryption to ensure that each exchange of messages between the Bot and the user is secure. 

We have handpicked the top and trending Python Telegram Bot libraries for your next project below.  

python-telegram-bot:  

• Used for building a bot in telegram easily by coupling with Python libraries like Flask, Requests, and Viz.  
• Provides an asynchronous interface for the Telegram Bot API.  
• The library features many high-level classes to make the development straightforward.  

Telethon:  

• An asynchronous Python 3 MTProto library that helps interact with Telegram’s API as a user through a bot API alternative.  
• If we install cryptg, the library will work faster as the encryption and decryption will be done using C instead of Python.  
• If we install pillow, larger images will be automatically resized while sending photos to prevent telegram from failing with ‘Invalid Image’ messages.  

pyTelegramBotAPI:  

• Used as the Python implementation for the telegram bot as it supports both synchronous and asynchronous methods.  
• Provides functions like send_message, send_xyz, send_document, etc. And listens to all incoming messages.  
• Can have an arbitrary name, but it should have only one parameter: the message.  

aiogram:  

• Used for building bots quickly and efficiently using the available template and a fully asynchronous framework for Telegram Bot API.  
• Used in Bot applications and Automation.  
• Can reply into webhook, i.e., making requests in response to any latest updates.  

pyrogram:  

• Modern, elegant, and asynchronous Telegram MTProto API framework in Python for bots and users.  
• Enables you to easily interact with the main Telegram API using a user interface or a bot API alternative using Python.  
• Types and methods are all type-hinted, which will enable excellent editor support.  

telepot:  

• Helps build applications for Telegram Bot API and works on Python 2.7 and Python 3.  
• Use telepot.glance() function for extracting the headline information.  
• Supports synchronous and asynchronous methods of programming.  

mtprotoproxy:  

• Fully asynchronous and can process a lot of connections.  
• Not just a tool but has an API that can help customize the Telegram MTProto proxy.  
• Can be used for logging, limiting access, and creating proxy farms that are hard to filter.  

BrainDamage:  

• Used in Runtime environments, Docker applications, and Servers.  
• Can destroy the active slaves, remove the stub from the host and registry entries, run shell commands on the host, and download files on a host computer.  
• Used for Artificial Intelligence and Machine Learning.  

informer:  

• Used in Docker applications, Bot, and Automation.  
• Allows you to masquerade as multiple REAL users on telegram and spy on 500+ Telegram channels per account.  
• Is a Telegram Mass Surveillance Bot in Python.  

TeleGram-Scraper:  

• Used to export competitor groups, channel members, and add them to your own group or channel.  
• You can scrape search results and extract the contents produced from those search results.  
• Supports telegram premium API.  

telebot:  

• Provides the best-of-its-kind API for command routing, keyboards, and inline query requests and callbacks.  
• Are Concise API, supports Command routing, Middleware, Effortless bot callbacks, and Transparent File API.  
• Is a highload-ready solution that has APIs that are easy to memorize and use.  

mautrix-telegram:  

• A hybrid Matrix- telegram puppeting or relaybot bridging library.  
• Has 2-factor authentication enabled for logging in with a bot token.  
• Includes a simple script to help migration between different database management systems.  

python-aria-mirror-bot:  

• A telegram bot for mirroring files on the internet to our Google Drive or Telegram.  
• Supports Mirroring direct download links to Google Drive, Upload and download progress, Docker support, Download or upload speed and ETAs, Index Link support, and many more.  
• Stops duplicates for all tasks except for qBittorrent and youtube-dl tasks.  

TorrentLeech-Gdrive:  

• Based on the Pyrogram library and Leecher.  
• Supports Telegram file mirroring to the cloud with its unzipping, untar, and unrar.  
• Help change the rclone destination configuration on the fly.  

Here are the top Python Email Libraries you will need to get built-in support to send emails using the SMTP protocol. The smtplib module will define an SMTP client session object which we can use for sending email to any machine connected to the internet with an ESMTP or SMTP listener daemon.  

We can use Python for sending an email in many ways, like an email with an attachment, an HTML/Markdown-formatted email message, or plain text. Many third-party libraries are available in the market, like EmailReplyParser for parsing the response from email threads, EmailMessage module in Python’s Standard Library, Mailgun-py, which is an official and actively maintained library, and Flanker which is a parsing and validation library.  

We have handpicked the top 15 Python Email Libraries in 2023 which fulfill all the developer requirements: 

GHunt: 

• Is an offensive Google Framework that is designed to evolve effectively.  
• Is fully asynchronous and uses CLI & modules.  
• Will automatically use venvs to avoid dependency conflicts with other projects.  

mailinabox: 

• Helps us in taking back control of our email by defining an easy-to-deploy, one-click SMTP+everything else server: a mail server in a box. 
• Promotes innovation, privacy, and decentralization on the web. 
• Has auditable, idempotent, and automated configuration.  

Mailpile: 

• Is a fast web-mail client, modern, with user-friendly privacy and encryption features.  
• Places emphasize providing an elegant user interface and a clean and pleasant user experience.  
• Allows email to be organized with tags and lets us automatically configure tag incoming email depending on Bayesian classifiers or static rules.  

theHarvester: 

• Is a simple yet powerful tool designed for use during the reconnaissance stage of a penetrated or a red team assessment test.  
• Performs Open Source Intelligence (OSINT) by gathering help in determining a domain’s external threat landscape. 
• Gathers emails, subdomains, URLs, names, and IPs with the help of multiple public resources. 

pyWhat: 

• Will easily let you identify IP addresses, emails, and more. 
• Helps in filtering outputs, exporting, and sorting according to your requirements. 
• Supports filters like bug bounties and many more. 

sync-engine: 

• Offers a RESTful API on top of a powerful email sync platform to make it easier to build apps on top of the email.  
• The API service will provide a REST API to help interact with your data.  
• The sync engine will automatically begin synchronizing your account with your current provider. 

yagmail: 

• Is an SMTP/GMAIL client which aims to make sending emails the simplest and easiest task. 
• With yagmail, sending emails to many people by offering a list of email strings rather than mentioning a single string is possible. 
• Is a wrapper library on top of smtplib that is a standard library to send emails using Python.  

mailin: 

• Is an SMTP server that listens to emails, parses them, and posts them in json format to the URL you choose.  
• Checks incoming email’s spf, spam score, and dkim to tell you the language in which it is written.  
• Can be used as a standalone application embedded inside a node application or directly from the command line. 

offlineimap: 

• Is software that helps download your email mailbox or mailboxes as local Maildirs.  
• OfflineIMAP will synchronize both sides using IMAP.  
• Allows you to read your email offline without requiring your mail reader to support your IMAP operations. 

gmail: 

• Is a Pythonic interface for Google’s Gmail with all the necessary tools.  
• Will search, archive, delete, read, and send multipart emails, manage labels, and mark as read/unread emails. 
• Connects with live Gmail for accessing real-time data. 

flanker: 

• Is an open source parsing library in Python which contains an address and a MIME parsing library.  
• Both can parse addresses, validate them, and convert headers to Unicode, which detects and preserves encodings when possible.  
• Stores the message in a single string, tracks changes, and returns unchanged parts during serialization. 

inbox.py: 

• Is an asynchronous simplest SMTP server.  
• Can handle one instance of over one thousand emails per second.  
• Is a library for managing email inboxes that provides an API for creating, deleting, reading, and retrieving emails. 

django-anymail: 

• Let's send and receive an email in Django with your choice of transactional Email Service Provider (ESPs).  
• Includes integration to each ESP’s sending APIs into Django’s built-in email package, simplified inline images from HTML email, and ‘Batch Transactional’ sends with your ESP’s merging and templating features.  
• Maintains compatibility with all Django versions and is extensively tested on all Python versions which Django supports.  

python-o365: 

• Aims in making interact with Office 365 and Microsoft Graph in a Pythonic and easy way. 
• Helps with access to Calendar, OneDrive, Email, and Contacts.  
• Provides automatic handling between local and server datetimes. 

SimplyEmail: 

• Is an email recon that is easy and quick using the framework built on.  
• Uses built-in Parsers for the rawest results.  
• Multiprocessing Queue for modules and Results Queue for easy Email data handling.