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What makes Python language an ideal choice for developing applications? It offers higher-level functions and higher-level data types than other programming languages. It also provides easy way to access and manipulate those data in an efficient way. Python is used regularly in mainstream software such as AI, data science, networking, gaming and more.

Popular New Releases in Python

youtube-dl 2021.12.17

TensorFlow Official Models 2.7.1

v4.18.0: Checkpoint sharding, vision models

youtube-dl

youtube-dl 2021.12.17

models

TensorFlow Official Models 2.7.1

thefuck

transformers

v4.18.0: Checkpoint sharding, vision models

flask

Popular Libraries in Python

public-apis

by public-apis python

star image 184682 MIT

A collective list of free APIs

system-design-primer

by donnemartin python

star image 143449 NOASSERTION

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

Python

by TheAlgorithms python

star image 117097 MIT

All Algorithms implemented in Python

Python-100-Days

by jackfrued python

star image 114192

Python - 100天从新手到大师

youtube-dl

by ytdl-org python

star image 108335 Unlicense

Command-line program to download videos from YouTube.com and other video sites

awesome-python

by vinta python

star image 102379 NOASSERTION

A curated list of awesome Python frameworks, libraries, software and resources

models

by tensorflow python

star image 73392 NOASSERTION

Models and examples built with TensorFlow

thefuck

by nvbn python

star image 65678 MIT

Magnificent app which corrects your previous console command.

django

by django python

star image 63447 NOASSERTION

The Web framework for perfectionists with deadlines.

public-apis

by public-apis python

star image 184682 MIT

A collective list of free APIs

system-design-primer

by donnemartin python

star image 143449 NOASSERTION

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

Python

by TheAlgorithms python

star image 117097 MIT

All Algorithms implemented in Python

Python-100-Days

by jackfrued python

star image 114192

Python - 100天从新手到大师

youtube-dl

by ytdl-org python

star image 108335 Unlicense

Command-line program to download videos from YouTube.com and other video sites

awesome-python

by vinta python

star image 102379 NOASSERTION

A curated list of awesome Python frameworks, libraries, software and resources

models

by tensorflow python

star image 73392 NOASSERTION

Models and examples built with TensorFlow

thefuck

by nvbn python

star image 65678 MIT

Magnificent app which corrects your previous console command.

django

by django python

star image 63447 NOASSERTION

The Web framework for perfectionists with deadlines.

Trending New libraries in Python

yolov5

by ultralytics python

star image 25236 GPL-3.0

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

yt-dlp

by yt-dlp python

star image 22499 Unlicense

A youtube-dl fork with additional features and fixes

MockingBird

by babysor python

star image 20425 NOASSERTION

🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time

Depix

by beurtschipper python

star image 19784 NOASSERTION

Recovers passwords from pixelized screenshots

PaddleOCR

by PaddlePaddle python

star image 19581 Apache-2.0

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

GFPGAN

by TencentARC python

star image 17269 NOASSERTION

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

copilot-docs

by github python

star image 16816 CC-BY-4.0

Documentation for GitHub Copilot

diagrams

by mingrammer python

star image 16552 MIT

:art: Diagram as Code for prototyping cloud system architectures

jina

by jina-ai python

star image 14316 Apache-2.0

Cloud-native neural search framework for 𝙖𝙣𝙮 kind of data

yolov5

by ultralytics python

star image 25236 GPL-3.0

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

yt-dlp

by yt-dlp python

star image 22499 Unlicense

A youtube-dl fork with additional features and fixes

MockingBird

by babysor python

star image 20425 NOASSERTION

🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time

Depix

by beurtschipper python

star image 19784 NOASSERTION

Recovers passwords from pixelized screenshots

PaddleOCR

by PaddlePaddle python

star image 19581 Apache-2.0

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

GFPGAN

by TencentARC python

star image 17269 NOASSERTION

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

copilot-docs

by github python

star image 16816 CC-BY-4.0

Documentation for GitHub Copilot

diagrams

by mingrammer python

star image 16552 MIT

:art: Diagram as Code for prototyping cloud system architectures

jina

by jina-ai python

star image 14316 Apache-2.0

Cloud-native neural search framework for 𝙖𝙣𝙮 kind of data

Top Authors in Python

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20636

Trending Kits in Python

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 is Stella, an AI chatbot that runs on a web browser and capable of maintaining conversations with humans and also handle to-do lists. This project is for the HackMakers hackathon. - from Team Stellars.

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.

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

We used to following libraries to train our model.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

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

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.

This kit is helpful for audio analysis. Audio information plays a rather important role in the increasing digital content that is available today; resulting in a need for methodologies that automatically analyze such content. Speaker Identification is one of the vital field of research based upon Voice Signals. Its other notable fields are: Speech Recognition, Speech-to-Text Conversion, and vice versa, etc. Mel Frequency Cepstral Coefficient (MFCC) is considered a key factor in performing Speaker Identification. But, there are other features lists available as an alternate to MFCC; like- Linear Predictor Coefficient (LPC), Spectrum Sub-band Centroid (SSC), Rhythm, Turbulence, Line Spectral Frequency (LPF), ChromaFactor, etc. Gaussian Mixture Model (GMM) is the most popular model for training on our data. The training task can also be executed on other significant models; viz. Hidden Markov Model (HMM). Recently, most of the model training phase for a speaker identification project is executed using Deep learning; especially, Artificial Neural Networks (ANN). In this project, we are mainly focused on implementing MFCC and GMM in pair to achieve our target. We have considered MFCC with “tuned parameters” as the primary feature and delta- MFCC as secondary feature. And, we have implemented GMM with some tuned parameters to train our model. We have performed this project on two different kinds of Dataset; viz. “VoxForge” Dataset and a custom dataset which we have prepared by ourselves. We have obtained an outstanding result on both of these Datasets; viz. 100% accuracy on VoxForge Dataset and 95.29 % accuracy on self prepared Dataset. We demonstrate that speaker identification task can be performed using MFCC and GMM together with outstanding accuracy in Identification/ Diarization results.

Group Name 1

Pre-processing audio

Group Name 2

Audio analysis

AI Doctor

IDE ENV AI Doctor

Libraries for Development Environment

EDA

Libraries needed for exploratory data analysis and visualization

Modelling

Libraries needed for building models

Group Name 4

The Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm. In this challenge, we are inviting AI-powered solutions for the digital school of tomorrow.

DATASET: Feel free to use any dataset of your choice.

There is no restriction and you can use any data set. Please see the section - DATASETS below for sample datasets to help as a reference. Here are sample areas you could choose to tackle in this challenge. Feel free to come up with your own ideas as well. 1. Higher Education and Career Recommendation 2. Mental Health Monitor and Virtual Companion 3. Adaptive Learning Curriculum 4. Class availability scheduling for social distancing 5. Compliance of COVID guidelines - masking, distancing, temperature Please see below for guidelines and reusable libraries to jumpstart your solution. This kit provides reference to open-source libraries which can be reused as core building blocks for creating a predictive solution. You may use any other open-source libraries also as relevant to your solution. Reusability is a key design principle and will be scored positively in your submission. These reference reusable libraries are spread over functions in Data Analysis and Mining, Data Visualization, Machine Learning, and other key areas to build AI solution. Below are the guidelines for creating your submission kit for this challenge. 1. See Product Tour > Creating a kit from the kandi header. This will guide you on creating your kit. 2. Your submission kit should contain the kit heading/ name, description of the solution, and other relevant information. 3. Create groups with logical names and add the libraries to the respective sections. 4. You solution can be built with any libraries other than the libraries provided here for reference. 5. The project source library for the solution built in the hackathon should be hosted in GitHub and listed in your kit under 'Kit Solution Source' section. 6. Any deployment instructions should be added under 'Kit Deployment Instructions' section of the kit. 7. Add any additional information, links under the kit description or group descriptions.

DATASETS

https://data.ed.gov/ https://data.world/datasets/education https://data.gov.in/sector/higher-education https://github.com/mdsaifk/Student-Dropout-Prediction/tree/main/Data https://github.com/hilmarh/student-dropout-prediction/tree/master/datasets https://github.com/iampratheesh/Student-Dropout-Prediction/blob/master/student%20info.csv https://www.kaggle.com/spscientist/students-performance-in-exams https://www.kaggle.com/aljarah/xAPI-Edu-Data https://www.kaggle.com/janiobachmann/math-students?select=student-mat.csv https://www.kaggle.com/kwadwoofosu/predict-test-scores-of-students https://www.kaggle.com/namanmanchanda/entrepreneurial-competency-in-university-students https://www.kaggle.com/uciml/student-alcohol-consumption?select=student-por.csv https://www.kaggle.com/passnyc/data-science-for-good https://www.kaggle.com/landlord/education-and-covid19

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 Analysis and Mining

Data Mining and Analysis plays vital role in Predictive Analytics. It lets you inspect, cleanse, explore, manipulate, transform your data to identify hidden patterns and relationships in data. You can make use of these popular libraries to model the solution.

Data Visualization

Data Visualization helps you depict insight found in data. Avail the libraries added here to represent identified patterns and relationships from data graphically for better understanding and presentation.

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.

Image Analysis

Image Analysis plays vital role in Visual Analytics. It lets us inspect, cleanse, explore, augment and transform images to prepare data for training and prediction.

Machine learning algorithms and techniques

To build a model for Predictive Analytics, you can apply traditional machine learning algorithms and techniques using the most popular scikit-learn. Or you can build your own neural network to implement deep learning techniques by using the library of your choice from this section.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

Open Source Intelligence has played a pivotal role in key events like tracing Covid-19 origins, MH17 downing, the Boston Marathon bombing, and the Myanmar refugee crisis. Approximately 500 million tweets are published every day, totaling over 200 billion posts in a year. Facebook users upload 350 million photos per day. YouTube users add nearly 720,000 hours of new video every day. Almost all devices are online today in the connected world.

While monitoring messages was exclusive to intelligence agencies, the tons of information available in the public realm today has made it possible for general and security enthusiasts to look for insights that might not have been possible earlier. The U.S. Department of State defines OSINT as "intelligence that is produced from publicly available information and is collected, exploited, and disseminated promptly to an appropriate audience to address a specific intelligence requirement."

Designed correctly, OSINT can reduce risk across a variety of common risks such as weather conditions, disease outbreaks, corporate risk management, data privacy, reputation management, in addition to higher-order tasks like national security and cybersecurity. Do not construe this as legal advice, promotion, or authorization to indulge in any activity whatsoever.

OSINT Framework

The OSINT framework enables gathering information from free tools or resources. The below open source libraries introduce and enable gathering information based on the OSINT Framework.

Target Reconnaissance

Recon-ng is a full-featured reconnaissance framework designed with the goal of providing a powerful environment to conduct open source web-based reconnaissance quickly and thoroughly.

Information Collection

theHarvester and similar tools gather emails, names, subdomains, IPs and URLs using multiple public data sources.

Track Online Assets

Shodan and Amass enable researchers to see the exposed assets.

Google Search

Google dorks provides information through the usage of operators, which are otherwise difficult to extract using simple searches.

Everyone loves to play games, especially online games. Sudoku is one of the great and prominent online games that helps us to develop problem-solving skills. Sudoku is one of the logic-based, combinatorial number-placement puzzles. The benefits of playing sudoku are that it improves concentration, promotes a healthy mind. The ultimate goal of the sudoku game is to fill a 9×9 grid with numbers. Python is preferable for building sudoku games; the reason behind that is python is free and open-source, with vast library support. Before technology evolution, we could play sudoku in magazines, article books. Modern technology has brought the opportunity to digitally create and play sudoku, so let's get started with the bellow libraries without delay. This kit aids the development of the Sudoku game using python by following the below steps. 1. Select a development environment of your choice 2. knowledge of Graphical user interface 3. Idea of the key binding controller 4. Fill the grid with default numbers. 5. Assign a specific key for each operation and listen to it. 6. Implement sudoku solver 7. Conjoin the backtracking algorithm into it. 8. Apply a set of colors to visualize auto-solving.

Graphical user interface

The graphical user interface is a user interface that permits users to interact with electronic devices through graphical icons and an audio indicator. Tkinter is the standard GUI library for Python. Python, when combined with Tkinter, provides a fast and easy way to create GUI applications.

Puzzle generator

Sudoku Generator algorithm uses the standard type of Sudoku Solver Algorithm, which is a backtracking algorithm. A backtracking algorithm is used to investigate all possible solutions of a given grid.

Development Environment

Pycharm and Jupyter NoteBook are used for development. Pycharm offers code analysis, an integrated unit tester, a graphical debugger. Jupyter Notebook is a tremendous web application that allows us to share and create documents that consist of live code and collaboration.

Puzzle Solver

A Sudoku Solver is implemented using Python and PyGame Library by visualizing the Sudoku Board using the Backtracking Algorithm.

One of the most intellectual indoor games which keep the player engaged is Sudoku. Sudoku is a classic logic-based puzzle game. It requires a keen focus of mind and a logical vision. It is a number-placement game where you will be given a 9*9 grid which contains sub-grids of nine 3*3 matrices. The ultimate goal is to fill a grid with numbers from 1 to 9 in such a way each of its rows and columns contains each number exactly once. The outgrowth of technology in the last decade brought this intriguing game online. How about you creating this brilliant Sudoku game? How about building this complex game in a single-page application like React? Sounds interesting! Isn't it? Let's get into it with the help of the following libraries. This kit aids the development of Sudoku games using React by following the below steps. 1. Choose a development environment 2. Create a 2D array 3. Set up a track to look into the game's progress 4. Set up a track to determine the number of conflicts left 5. Create a component to indicate the connection between cells 6. Write a script to indicate connections using signals 7. Manage user's input 8. Create a component to drag and drop the numbers 9. Set up the tools to perform operations 10. Do the scripting to track the history of actions done

Development Environment

React is used for development. With React, it becomes easy and simple to develop an interactive UI. The state management in React makes the process of developing an application more flexible.

Graphical user interface

GUIs act as intermediaries to communicate with your device through UI components. In React, building UI components gets easy with the aid of CSS. React can be used for desktop applications and mobile applications as well.

Puzzle Solver

The puzzle-solving is simplified by creating cell components that throw signals indicating the relationship or connection between similar cell components using different colors.

Puzzle generator

Generating a puzzle is one of the key steps in creating a logic-based game. State management in React optimizes the puzzle generation.

As we are in the digital era, real-time video games are ruling the young generation. Tank games are one of the addictive games of this generation. The objective of this game is to destroy the enemy's tank with our tank, which will decrease the energy level of the opponent. Similarly, our energy level will be reduced when the opponent attacks us with their tank. The attacking capacity ultimately depends on the energy level. The more is the energy level, the high is the attacking capacity. Following are the steps to be followed for building Tank Fight Game, 1.Graphic design & Sound effects 2.Firing and exploiting the tanks 3.Customize control over keyboard 4.Multi-player 5.3D Tank game

Customize control over keyboard

Key mapper is an open-source library that allows users to use a key or combination of keys to perform a specific action, which can be used for navigating and shooting. The below libraries can help you to create your control.

Graphic design & Sound effects

Listed below libraries help in creating the best graphic design and sound effects for gaming applications using python, C#, JavaScript, which can be used to design tanks, animate the movement of tanks, explosion of tanks, and display energy level bars.

Firing and exploiting the tanks

Random module, an open-source library, generates a random number provided the range, which can be used for firing and exploiting the tanks, decides the playing turn at the start of every game.

3D Tank game

The tank game can be built in 3D by using the below library.

Multi-player

Tank game can be played as multiplayer by using the below libraries. Multiplayers will play on the game field they shoot each other. The more is the energy level. The high is the attacking capacity.

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 is Stella, an AI chatbot that runs on a web browser and capable of maintaining conversations with humans and also handle to-do lists. This project is for the HackMakers hackathon. - from Team Stellars.

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.

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

We used to following libraries to train our model.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

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

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.

This kit is helpful for audio analysis. Audio information plays a rather important role in the increasing digital content that is available today; resulting in a need for methodologies that automatically analyze such content. Speaker Identification is one of the vital field of research based upon Voice Signals. Its other notable fields are: Speech Recognition, Speech-to-Text Conversion, and vice versa, etc. Mel Frequency Cepstral Coefficient (MFCC) is considered a key factor in performing Speaker Identification. But, there are other features lists available as an alternate to MFCC; like- Linear Predictor Coefficient (LPC), Spectrum Sub-band Centroid (SSC), Rhythm, Turbulence, Line Spectral Frequency (LPF), ChromaFactor, etc. Gaussian Mixture Model (GMM) is the most popular model for training on our data. The training task can also be executed on other significant models; viz. Hidden Markov Model (HMM). Recently, most of the model training phase for a speaker identification project is executed using Deep learning; especially, Artificial Neural Networks (ANN). In this project, we are mainly focused on implementing MFCC and GMM in pair to achieve our target. We have considered MFCC with “tuned parameters” as the primary feature and delta- MFCC as secondary feature. And, we have implemented GMM with some tuned parameters to train our model. We have performed this project on two different kinds of Dataset; viz. “VoxForge” Dataset and a custom dataset which we have prepared by ourselves. We have obtained an outstanding result on both of these Datasets; viz. 100% accuracy on VoxForge Dataset and 95.29 % accuracy on self prepared Dataset. We demonstrate that speaker identification task can be performed using MFCC and GMM together with outstanding accuracy in Identification/ Diarization results.

Group Name 1

Pre-processing audio

Group Name 2

Audio analysis

AI Doctor

IDE ENV AI Doctor

Libraries for Development Environment

EDA

Libraries needed for exploratory data analysis and visualization

Modelling

Libraries needed for building models

Group Name 4

The Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm. In this challenge, we are inviting AI-powered solutions for the digital school of tomorrow.

DATASET: Feel free to use any dataset of your choice.

There is no restriction and you can use any data set. Please see the section - DATASETS below for sample datasets to help as a reference. Here are sample areas you could choose to tackle in this challenge. Feel free to come up with your own ideas as well. 1. Higher Education and Career Recommendation 2. Mental Health Monitor and Virtual Companion 3. Adaptive Learning Curriculum 4. Class availability scheduling for social distancing 5. Compliance of COVID guidelines - masking, distancing, temperature Please see below for guidelines and reusable libraries to jumpstart your solution. This kit provides reference to open-source libraries which can be reused as core building blocks for creating a predictive solution. You may use any other open-source libraries also as relevant to your solution. Reusability is a key design principle and will be scored positively in your submission. These reference reusable libraries are spread over functions in Data Analysis and Mining, Data Visualization, Machine Learning, and other key areas to build AI solution. Below are the guidelines for creating your submission kit for this challenge. 1. See Product Tour > Creating a kit from the kandi header. This will guide you on creating your kit. 2. Your submission kit should contain the kit heading/ name, description of the solution, and other relevant information. 3. Create groups with logical names and add the libraries to the respective sections. 4. You solution can be built with any libraries other than the libraries provided here for reference. 5. The project source library for the solution built in the hackathon should be hosted in GitHub and listed in your kit under 'Kit Solution Source' section. 6. Any deployment instructions should be added under 'Kit Deployment Instructions' section of the kit. 7. Add any additional information, links under the kit description or group descriptions.

DATASETS

https://data.ed.gov/ https://data.world/datasets/education https://data.gov.in/sector/higher-education https://github.com/mdsaifk/Student-Dropout-Prediction/tree/main/Data https://github.com/hilmarh/student-dropout-prediction/tree/master/datasets https://github.com/iampratheesh/Student-Dropout-Prediction/blob/master/student%20info.csv https://www.kaggle.com/spscientist/students-performance-in-exams https://www.kaggle.com/aljarah/xAPI-Edu-Data https://www.kaggle.com/janiobachmann/math-students?select=student-mat.csv https://www.kaggle.com/kwadwoofosu/predict-test-scores-of-students https://www.kaggle.com/namanmanchanda/entrepreneurial-competency-in-university-students https://www.kaggle.com/uciml/student-alcohol-consumption?select=student-por.csv https://www.kaggle.com/passnyc/data-science-for-good https://www.kaggle.com/landlord/education-and-covid19

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 Analysis and Mining

Data Mining and Analysis plays vital role in Predictive Analytics. It lets you inspect, cleanse, explore, manipulate, transform your data to identify hidden patterns and relationships in data. You can make use of these popular libraries to model the solution.

Data Visualization

Data Visualization helps you depict insight found in data. Avail the libraries added here to represent identified patterns and relationships from data graphically for better understanding and presentation.

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.

Image Analysis

Image Analysis plays vital role in Visual Analytics. It lets us inspect, cleanse, explore, augment and transform images to prepare data for training and prediction.

Machine learning algorithms and techniques

To build a model for Predictive Analytics, you can apply traditional machine learning algorithms and techniques using the most popular scikit-learn. Or you can build your own neural network to implement deep learning techniques by using the library of your choice from this section.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

Open Source Intelligence has played a pivotal role in key events like tracing Covid-19 origins, MH17 downing, the Boston Marathon bombing, and the Myanmar refugee crisis. Approximately 500 million tweets are published every day, totaling over 200 billion posts in a year. Facebook users upload 350 million photos per day. YouTube users add nearly 720,000 hours of new video every day. Almost all devices are online today in the connected world.

While monitoring messages was exclusive to intelligence agencies, the tons of information available in the public realm today has made it possible for general and security enthusiasts to look for insights that might not have been possible earlier. The U.S. Department of State defines OSINT as "intelligence that is produced from publicly available information and is collected, exploited, and disseminated promptly to an appropriate audience to address a specific intelligence requirement."

Designed correctly, OSINT can reduce risk across a variety of common risks such as weather conditions, disease outbreaks, corporate risk management, data privacy, reputation management, in addition to higher-order tasks like national security and cybersecurity. Do not construe this as legal advice, promotion, or authorization to indulge in any activity whatsoever.

OSINT Framework

The OSINT framework enables gathering information from free tools or resources. The below open source libraries introduce and enable gathering information based on the OSINT Framework.

Target Reconnaissance

Recon-ng is a full-featured reconnaissance framework designed with the goal of providing a powerful environment to conduct open source web-based reconnaissance quickly and thoroughly.

Information Collection

theHarvester and similar tools gather emails, names, subdomains, IPs and URLs using multiple public data sources.

Track Online Assets

Shodan and Amass enable researchers to see the exposed assets.

Google Search

Google dorks provides information through the usage of operators, which are otherwise difficult to extract using simple searches.

Everyone loves to play games, especially online games. Sudoku is one of the great and prominent online games that helps us to develop problem-solving skills. Sudoku is one of the logic-based, combinatorial number-placement puzzles. The benefits of playing sudoku are that it improves concentration, promotes a healthy mind. The ultimate goal of the sudoku game is to fill a 9×9 grid with numbers. Python is preferable for building sudoku games; the reason behind that is python is free and open-source, with vast library support. Before technology evolution, we could play sudoku in magazines, article books. Modern technology has brought the opportunity to digitally create and play sudoku, so let's get started with the bellow libraries without delay. This kit aids the development of the Sudoku game using python by following the below steps. 1. Select a development environment of your choice 2. knowledge of Graphical user interface 3. Idea of the key binding controller 4. Fill the grid with default numbers. 5. Assign a specific key for each operation and listen to it. 6. Implement sudoku solver 7. Conjoin the backtracking algorithm into it. 8. Apply a set of colors to visualize auto-solving.

Graphical user interface

The graphical user interface is a user interface that permits users to interact with electronic devices through graphical icons and an audio indicator. Tkinter is the standard GUI library for Python. Python, when combined with Tkinter, provides a fast and easy way to create GUI applications.

Puzzle generator

Sudoku Generator algorithm uses the standard type of Sudoku Solver Algorithm, which is a backtracking algorithm. A backtracking algorithm is used to investigate all possible solutions of a given grid.

Development Environment

Pycharm and Jupyter NoteBook are used for development. Pycharm offers code analysis, an integrated unit tester, a graphical debugger. Jupyter Notebook is a tremendous web application that allows us to share and create documents that consist of live code and collaboration.

Puzzle Solver

A Sudoku Solver is implemented using Python and PyGame Library by visualizing the Sudoku Board using the Backtracking Algorithm.

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QUESTION

Python/Docker ImportError: cannot import name 'json' from itsdangerous

Asked 2022-Mar-31 at 12:49

I am trying to get a Flask and Docker application to work but when I try and run it using my docker-compose up command in my Visual Studio terminal, it gives me an ImportError called ImportError: cannot import name 'json' from itsdangerous. I have tried to look for possible solutions to this problem but as of right now there are not many on here or anywhere else. The only two solutions I could find are to change the current installation of MarkupSafe and itsdangerous to a higher version: https://serverfault.com/questions/1094062/from-itsdangerous-import-json-as-json-importerror-cannot-import-name-json-fr and another one on GitHub that tells me to essentially change the MarkUpSafe and itsdangerous installation again https://github.com/aws/aws-sam-cli/issues/3661, I have also tried to make a virtual environment named veganetworkscriptenv to install the packages but that has also failed as well. I am currently using Flask 2.0.0 and Docker 5.0.0 and the error occurs on line eight in vegamain.py.

Here is the full ImportError that I get when I try and run the program:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10

Here are my requirements.txt, vegamain.py, Dockerfile, and docker-compose.yml files:

requirements.txt:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20

vegamain.py:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46

Dockerfile:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46FROM python:3.9
47ENV PYTHONUNBUFFERED 1
48WORKDIR /app
49COPY requirements.txt /app/requirements.txt
50RUN pip install -r requirements.txt
51COPY . /app
52

docker-compose.yml:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46FROM python:3.9
47ENV PYTHONUNBUFFERED 1
48WORKDIR /app
49COPY requirements.txt /app/requirements.txt
50RUN pip install -r requirements.txt
51COPY . /app
52version: '3.8'
53services:
54  backend:
55    build:
56      context: .
57      dockerfile: Dockerfile
58    command: 'python vegamain.py'
59    ports:
60      - 8004:5000
61    volumes:
62      - .:/app
63    depends_on:
64      - db
65
66#  queue:
67#    build:
68#      context: .
69#      dockerfile: Dockerfile
70#    command: 'python -u consumer.py'
71#    depends_on:
72#      - db
73
74  db:
75    image: mysql:5.7.22
76    restart: always
77    environment:
78      MYSQL_DATABASE: admin
79      MYSQL_USER: root
80      MYSQL_PASSWORD: root
81      MYSQL_ROOT_PASSWORD: root
82    volumes:
83      - .dbdata:/var/lib/mysql
84    ports:
85      - 33069:3306
86

How exactly can I fix this code? thank you!

ANSWER

Answered 2022-Feb-20 at 12:31

I was facing the same issue while running docker containers with flask.

I downgraded Flask to 1.1.4 and markupsafe to 2.0.1 which solved my issue.

Check this for reference.

Source https://stackoverflow.com/questions/71189819

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QUESTION

Python/Docker ImportError: cannot import name 'json' from itsdangerous

Asked 2022-Mar-31 at 12:49

I am trying to get a Flask and Docker application to work but when I try and run it using my docker-compose up command in my Visual Studio terminal, it gives me an ImportError called ImportError: cannot import name 'json' from itsdangerous. I have tried to look for possible solutions to this problem but as of right now there are not many on here or anywhere else. The only two solutions I could find are to change the current installation of MarkupSafe and itsdangerous to a higher version: https://serverfault.com/questions/1094062/from-itsdangerous-import-json-as-json-importerror-cannot-import-name-json-fr and another one on GitHub that tells me to essentially change the MarkUpSafe and itsdangerous installation again https://github.com/aws/aws-sam-cli/issues/3661, I have also tried to make a virtual environment named veganetworkscriptenv to install the packages but that has also failed as well. I am currently using Flask 2.0.0 and Docker 5.0.0 and the error occurs on line eight in vegamain.py.

Here is the full ImportError that I get when I try and run the program:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10

Here are my requirements.txt, vegamain.py, Dockerfile, and docker-compose.yml files:

requirements.txt:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20

vegamain.py:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46

Dockerfile:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46FROM python:3.9
47ENV PYTHONUNBUFFERED 1
48WORKDIR /app
49COPY requirements.txt /app/requirements.txt
50RUN pip install -r requirements.txt
51COPY . /app
52

docker-compose.yml:

1veganetworkscript-backend-1  | Traceback (most recent call last):
2veganetworkscript-backend-1  |   File "/app/vegamain.py", line 8, in <module>
3veganetworkscript-backend-1  |     from flask import Flask
4veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/__init__.py", line 19, in <module>
5veganetworkscript-backend-1  |     from . import json
6veganetworkscript-backend-1  |   File "/usr/local/lib/python3.9/site-packages/flask/json/__init__.py", line 15, in <module>
7veganetworkscript-backend-1  |     from itsdangerous import json as _json
8veganetworkscript-backend-1  | ImportError: cannot import name 'json' from 'itsdangerous' (/usr/local/lib/python3.9/site-packages/itsdangerous/__init__.py)
9veganetworkscript-backend-1 exited with code 1
10Flask==2.0.0
11Flask-SQLAlchemy==2.4.4
12SQLAlchemy==1.3.20
13Flask-Migrate==2.5.3
14Flask-Script==2.0.6
15Flask-Cors==3.0.9
16requests==2.25.0
17mysqlclient==2.0.1
18pika==1.1.0
19wolframalpha==4.3.0
20# Veganetwork (C) TetraSystemSolutions 2022
21# all rights are reserved.  
22# 
23# Author: Trevor R. Blanchard Feb-19-2022-Jul-30-2022
24#
25
26# get our imports in order first
27from flask import Flask # <-- error occurs here!!!
28
29# start the application through flask.
30app = Flask(__name__)
31
32# if set to true will return only a "Hello World" string.
33Debug = True
34
35# start a route to the index part of the app in flask.
36@app.route('/')
37def index():
38    if (Debug == True):
39        return 'Hello World!'
40    else:
41        pass
42
43# start the flask app here --->
44if __name__ == '__main__':
45    app.run(debug=True, host='0.0.0.0') 
46FROM python:3.9
47ENV PYTHONUNBUFFERED 1
48WORKDIR /app
49COPY requirements.txt /app/requirements.txt
50RUN pip install -r requirements.txt
51COPY . /app
52version: '3.8'
53services:
54  backend:
55    build:
56      context: .
57      dockerfile: Dockerfile
58    command: 'python vegamain.py'
59    ports:
60      - 8004:5000
61    volumes:
62      - .:/app
63    depends_on:
64      - db
65
66#  queue:
67#    build:
68#      context: .
69#      dockerfile: Dockerfile
70#    command: 'python -u consumer.py'
71#    depends_on:
72#      - db
73
74  db:
75    image: mysql:5.7.22
76    restart: always
77    environment:
78      MYSQL_DATABASE: admin
79      MYSQL_USER: root
80      MYSQL_PASSWORD: root
81      MYSQL_ROOT_PASSWORD: root
82    volumes:
83      - .dbdata:/var/lib/mysql
84    ports:
85      - 33069:3306
86

How exactly can I fix this code? thank you!

ANSWER

Answered 2022-Feb-20 at 12:31

I was facing the same issue while running docker containers with flask.

I downgraded Flask to 1.1.4 and markupsafe to 2.0.1 which solved my issue.

Check this for reference.

Source https://stackoverflow.com/questions/71189819