Telegram

Python Markov Chain chatbot running on Telegram

Hanasubot (Japanese 話すボット, talking bot) is a Python chatbot running on Telegram. The bot is based on Markov Chains so it can learn your word instantly, unlike neural network chatbots which require training. It uses a modified version of markovify library for that purporse. However, the output may not make sense at all, though it can sometimes generate hilarious replies. In theory, the bot can learn in any languages, but for some languages word segmentation is required. The bot currently […]

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Games

Built with Python and QT library and Guess the number in three easy

Built with Python programming language and QT library and Guess the number in three easy, medium and hard rolls Number guessing game guide: First you choose the difficulty of the game. After selecting the difficulty level in the number box, you enter the number you want. After selecting and entering your number, press the Enter key to check your number program. At the bottom of the number box is a section called Game Help, which will guide you according to […]

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Transformer

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

optional arguments: -h, –help show this help message and exit –gpu_device GPU_DEVICE Select specific GPU to run the model –batch-size N Input batch size for training (default: 64) –epochs N Number of epochs to train (default: 20) –num-class N Number of classes to classify (default: 10) –lr LR Learning rate (default: 0.01) –weight-decay WD Weight decay (default: 1e-5) –model-path PATH Path to save    

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PyTorch

Unofficial PyTorch implementation of MobileViT

Overview This is a PyTorch implementation of MobileViT specified in “MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer“, arXiv 2021. Usage import torch from mobilevit import mobilevit_xxs net = mobilevit_xxs() img = torch.randn(1, 3, 256, 256) out = net(img) Credits Code adapted from MobileNetV2 and ViT. GitHub https://github.com/chinhsuanwu/mobilevit-pytorch    

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Graphics

Turtle graphics Built With Python

Turtle graphics || Python Rainbow (রংধনু) : Rainbow.using.Python.–.Python.Turtle.graphics.mp4 Human robot (মানব রোবট) : Draw.a.human.robot.using.python.–.Turtle.graphics.mp4 Orthocoronavirinae (করোনাভাইরাস ): Draw.Orthocoronavirinae.using.python.mp4 GitHub https://github.com/Jubair-Ahmed-Junjun/turtle    

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Scrape

Library support get vocabulary from MEM

Features: Support scraping the courses in MEM to take the vocabulary Support scraping IPA of English Language (US and UK) Support translate to your mother language Appplication Requires Install DB Browser : SQLite Install Library: Window python -m pip install memrise Linux macOS Guidelines How to take Course ID? Access the Website: Memrise and copy the Course ID as the following picture: Import library and initialize database

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Generator

Generate textual reports from the BibTeX files

Generate report for the given bibtex file. For use: python -m venv venv source venv/bin/activate pip install –upgrade pip pip install https://github.com/igordejanovic/textx-bibtex/archive/refs/heads/master.zip pip install https://github.com/igordejanovic/textx-gen-bibtex-report/archive/refs/heads/main.zip For development: git clone [email protected]:igordejanovic/textx-gen-bibtex-report.git cd textx-gen-bibtex-report python -m venv venv source venv/bin/activate pip install –upgrade pip pip install https://github.com/igordejanovic/textx-bibtex/archive/refs/heads/master.zip pip install -e .[dev,test] Note: for zsh escape [] -> pip install -e .[dev,test] To generate a report from file my.bib:

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API

API to run FAIR evaluations of online resources (Findable, Accessible, Interoperable, Reusable)

An API to run evaluations to assess the FAIRness of a resource. Using FastAPI and MongoDB Requirements Docker Docker Compose Poetry if you need to install new Python packages. Node.js (with npm) if you need to do frontend development Edit the assessments Most of the Python code for the API is in https://github.com/MaastrichtU-IDS/fair-enough/tree/main/backend/app All assessments used to run evaluations are python scripts defined in the same folder: https://github.com/MaastrichtU-IDS/fair-enough/tree/main/backend/app/assessments Backend local development Start the stack for development locally with Docker Compose: […]

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