Saravanan666/pypi-org huggingface dataset:
Package: aistrigh-nlp
AistrighNLPAistrighNLP is a collection of tools and models used for Aistrigh, the BT Young Scientist 2021 project. Our aim is to bring Irish into the modern era with NLP tools to give it parity with English.
The tools included are based around the work inNeural Models for Predicting Celtic Mutations(Scannell, 2020). Included is all the tools needed to create a demutated Irish corpus, which can be used in all sorts of NLP tasks, and a model to reinsert them. For the Python API docs visitAistrighNLP Python APIInstalling the PackageAistrighNLP can be downloaded usingpippip install aistrigh-nlpLowercasingWhen lowercasing either Irish or Scots Gaelic for prediciting mutations, you must be aware of special cases outlined in the paper above. Our lowercaser handles thataistrigh-nlp lowercase -i input.txt -o output.txtRemoving mutationsTo remove mutations from an entire dataset for use for NLP tasks (like Machine Translation) usedemutate-corpus.-l/--langmust take ISO 639 language codes like 'ga'.aistrigh-nlp demutate-corpus -i input.txt -o output.txt -l gaTo remove mutations with a 'window' on either side to train a neural network, usedemutate-window, with-wset to your desired window length on each sideaistrigh-nlp demutate-window -i input.txt -o output.csv -l ga -w 16Predicting the mutationsTo predict mutations on each word, usepredict-mutations. As of right now, it's only compatible with PyTorch+Torchtext models but we are working on expanding to TensorFlow and Keras. You'll need your vocab, labels and model checkpoint in the same folder (-d/--data). We provide default models to be usedhere.aistrigh-nlp predict-mutations -i input.txt -o output.txt -w 16 -d nn_100kApplying the predicted mutationsTo apply the mutations predicted bypredict-mutations, useapply-mutations.aistrigh-nlp apply-mutations -i input.txt -o output.txt -l gaScoring Celtic NMT models using Standard and Demutated BLEUTo score NMT models using both these metrics run;aistrigh-nlp bleu -r reference.txt -p predictions.txt -l gaIf you're scoring a demutated NMT model and haven't reapplied mutations, pass your demutated reference (-d) and predictions, and the original reference (-r).aistrigh-nlp bleu -d demutated_reference.txt -r reference.txt -p predictions.txt -l gaNOTEAistrighNLP uses PyTorchTracesto save the full computational graphs as checkpoints. This way, the model architecture need not be declared into hard-coded scripts. See thisStackOverflow Threadfor instructions to save a traced checkpoint.
Package: aistudio-sdk
Python Sdk实现封装一言API的PythonSDK，支持AIStudio开发者进行基于一言的项目/应用/插件开发需求
实现封装AIStudio模型库API的PythonSDK，支持AIStudio开发者进行模型库的操作开发需求快速开始如何构建、安装、运行pythonsetup.pybdist_wheel
pipuninstall-yaistudio-sdk
pipinstalloutput/dist/aistudio_sdk-0.1.7-py3-none-any.whl测试如何执行自动化测试如何贡献贡献patch流程及质量要求版本信息本项目的各版本信息和变更历史可以在这里查看。维护者ownersxiangyiqing(xiangyiqing@baidu.com)committersxiangyiqing(xiangyiqing@baidu.com)linyichong(linyichong@baidu.com)suoyi(suoyi@baidu.com)讨论
Package: ai-sudoku-solver
ai-Sudoku-SolverSolving Sudoku Puzzles using Artificial Neural NetworksTable of Contentsai-Sudoku-SolverTable of ContentsInstallationUsageModel GalleryReferencesInstallationpipinstallai-sudoku-solverUsageInstantiate a SudokuSolver objectfromai_sudoku_solverimportSudokuSolversolver=SudokuSolver("Ritvik19/sudoku-net-v1")Call the model on your puzzlespuzzle=np.array([[[0,0,4,3,0,0,2,0,9],[0,0,5,0,0,9,0,0,1],[0,7,0,0,6,0,0,4,3],[0,0,6,0,0,2,0,8,7],[1,9,0,0,0,7,4,0,0],[0,5,0,0,8,3,0,0,0],[6,0,0,0,0,0,1,0,5],[0,0,3,5,0,8,6,9,0],[0,4,2,9,1,0,3,0,0]]])solution=solver(puzzle)# array([[#     [8, 6, 4, 3, 7, 1, 2, 5, 9],#     [3, 2, 5, 8, 4, 9, 7, 6, 1],#     [9, 7, 1, 2, 6, 5, 8, 4, 3],#     [4, 3, 6, 1, 9, 2, 5, 8, 7],#     [1, 9, 8, 6, 5, 7, 4, 3, 2],#     [2, 5, 7, 4, 8, 3, 9, 1, 6],#     [6, 8, 9, 7, 3, 4, 1, 2, 5],#     [7, 1, 3, 5, 2, 8, 6, 9, 4],#     [5, 4, 2, 9, 1, 6, 3, 7, 8]# ]])Model Gallerymodel# parameterstrained onaccuracysudoku-net-v13,784,7291M puzzles98.138sudoku-net-v23,784,72910M puzzles98.212References1 million Sudoku games9 Million Sudoku Puzzles and Solutions
Package: ais-wordnet-sim
ais-wordnet-simInstallpip install ais-wordnet-simUsageGet list of similar sentencesGet a list which contains similar sentences (include original sentence)fromais_wordnet_simimportsimilar_sentencessentence='Thế lực thù đich có những âm mưu gì'# default limit = 1000result=similar_sentences(sentence,limit=5)result>>>['thế lực thù đich có những âm mưu gì','thế lực thù đich có những thủ đoạn gì','thế lực thù đich có những mưu kế gì','thế lực thù đich có những mưu mẹo gì','thế lực thù đich có những mưu mô gì']Create a categoryCreate a Category:{ question_list, answer ,topic }from a question and an answerfromais_wordnet_simimportgenerate_categoryquestion='Thế lực thù đich có những âm mưu gì'answer='Âm mưu phá hoại nhà nước'# default limit = 1000generate_category(question,answer,topic,limit=5)Get all categoriesGet all Categories generated in databasefromais_wordnet_simimportget_category_dataresult=get_category_data()result>>>[{'_id':ObjectId('5d86f35b944d00e06eb3a76b'),'question_list':['thế lực thù đich có những âm mưu gì','thế lực thù đich có những thủ đoạn gì','thế lực thù đich có những mưu kế gì','thế lực thù đich có những mưu mẹo gì','thế lực thù đich có những mưu mô gì'],'answer':'Âm mưu phá hoại nhà nước'}]Create Synonyms database from excel fileNote: Drop old database before creating new databaseExample Exel format:Sheet: n, a, v, r, eEach row of sheet: one or multiple words, synonyms words follow original wordfromais_wordnet_simimportadd_synonyms_excelfile='wordnet.xlsx'add_synonyms_excel(file)Enrich AIML FileCreate an enriched AIML file from original AIML filefromais_wordnet_simimportaiml_enrichaiml_enrich('original.aiml','enriched.aiml')
Package: aisy-database
Database package to support AISY Frameworkpip install aisy-sca
pip install aisy-database
If you use our framework, please consider citing:
@misc{AISY_Framework,      author = {Guilherme Perin and Lichao Wu and Stjepan Picek},      title  = {{AISY - Deep Learning-based Framework for Side-Channel Analysis}},      howpublished = {AISyLab repository},      note   = {{\url{https://github.com/AISyLab/AISY_Framework}}},      year   = {2021}   }
Package: aisy-sca
# AISY - Deep Learning-based Framework for Side Channel Analysis

Welcome to the first deep learning-based side-channel analysis framework.
This AISY Framework was developed by AISYLab from TU Delft.

If you use our framework, please consider citing:

@misc{AISY_Framework,
    author = {Guilherme Perin and Lichao Wu and Stjepan Picek},
    title  = {{AISY - Deep Learning-based Framework for Side-Channel Analysis}},
    howpublished = {AISyLab repository},
    note   = {{\url{https://github.com/AISyLab/AISY_Framework}}},
    year   = {2021}
}
Package: ai-talks
AI TalksA ChatGPT API wrapper, providing a user-friendly Streamlit web interface.Enhance your ChatGPT experience with our user-friendly API wrapper, featuring a seamless Streamlit web interface. Effortlessly interact with ChatGPT, while enjoying an intuitive and responsive design. Discover simplified access to advanced AI technology in just a few clicks.SetupClone repo:gitclonehttps://github.com/dKosarevsky/AI-Talks.gitInstall dependencies:pipinstall-rrequirements.txtAdd API key to.streamlit/secrets.toml:[api_credentials]api_key="sk-..."UsageTo run the app use the following command:bashrun.shAnother way:streamlitrunai_talks/chat.pyOnce the script is started, you can go to the URLhttp://localhost:8501to start using the bot.LicenseThis project is released under the MIT License.DonationSupport projectAI Talkscollects donations solely for the purpose of paying for theOpen AIAPI.
This allows you to provide access to communication with AI for all users.
Support us for joint development and interaction with the intelligence of the future!Crypto:USD Tether (USDT TRC20):TMQ5RiyQ7bv3XjB6Wf6JbPHVrGkhBKtmfAToncoin (TON):UQDbnx17N2iOmxfQF0k55QScDMB0MHL9rsq-iGB93RMqDhIHWorld:Buy Me A Coffeeko-fiPayPalRussia:TinkoffboostyCloudTips (Tinkoff)
Package: ait-bsc
Accelerator Integration Tool (AIT)The Accelerator Integration Tool (AIT) automatically integrates OmpSs@FPGA and OmpSs-2@FPGA accelerators into FPGA designs using different vendor backends.This README should help you install the AIT component of the OmpSs@FPGA toolchain from the repository.
However, it is preferred using the pre-built Docker image with the latest stable toolchain.
They are available atOmpSs@FPGA pre-built Docker imagesandOmpSs-2@FPGA pre-built Docker images.Moreover, there are pre-built SD images for the current supported board families: Zynq7000 and Ultrascale.
They are also available atOmpSs@FPGA pre-built SD imagesandOmpSs-2@FPGA pre-built SD images.PrerequisitesPython 3.7 or laterpipVendor backends:Xilinx Vivado 2018.3 or laterGit Large File StorageThis repository uses Git Large File Storage to handle relatively-large files that are frequently updated (i.e. hardware runtime IP files) to avoid increasing the history size unnecessarily. You must install it so Git is able to download these files.Follow instructions on their website to install it.Vendor backendsXilinx VivadoFollow installation instructions from Xilinx
Vivado, Vivado HLS and SDK, as well as the device support for the devices you're working, should be enabled during setup.
However, components can be added or removed afterwards.Current version supports Vivado 2018.3 onwards.InstallationYou can usepipto easily installaiton your system:python3 -m pip install ait-bscDevelopmentMake sure you have the following packages installed on your system.git-lfs(Git Large File Storage)setuptools >= 61.0(setuptools)Clone AIT's repositoryFrom GitHub:git clone https://github.com/bsc-pm-ompss-at-fpga/ait.gitFrom our internal GitLab repository (BSC users only):git clone https://pm.bsc.es/gitlab/ompss-at-fpga/ait.gitEnable Git LFS and installcd ait
git lfs install
git lfs pull
export AIT_HOME="/path/to/install/ait"
export DEB_PYTHON_INSTALL_LAYOUT=deb_system
python3 -m pip install . -t $AIT_HOMEAdd the installed binaries to your PATHexport PATH=$AIT_HOME/bin:$PATH
export PYTHONPATH=$AIT_HOME:$PYTHONPATHTestsPrerequisitespython3-flake8python3-unittestStyle testingThe python code follows PEP 8 style guide which is verified using theflake8tool.To check the current source code just executepython3 -m flake8.Unit testingThetestfolder contains some unitary tests for python sources.To run all tests the commandpython3 -m unittestcan be executed in the root directory of the repository.
Package: ait-dsn
AIT DSN provides APIS for connecting to spacecraft communication facilities via Consultative Committee for Space Data Systems (CCSDS) Space Link Extension (SLE) interfaces. AIT DSN supports the following interfaces:Return All Frames (RAF) interface (v4 and v5)Return Channel Frames (RCF) interface (v4 and v5)Forward Communications Link Transmission Unit (CLTU) interface (v4 and v5)CCSDS File Delivery Protocol Class 1 interfaceInterface specifications are available in theCCSDS Blue BooksGetting StartedYou can read through theInstallation Pagefor instruction on how to install AIT DSN.Join the CommunityThe project’sUser and Developer Mailing Listis the best way to communicate with the team, ask questions, brainstorm plans for future changes, and help contribute to the project.This project exists thanks to the dedicated users, contributors, committers, and project management committee members. If you’d like to learn more about how the project is organized and how to become a part of the team please check out theProject Structure and Governancedocumentation.ContributingFor information on how to contribute please see theAIT Contributing
Guide
Package: aitemplates
aitemplatesaitemplates is a Python package designed to simplify and streamline your work with the OpenAI API. It provides automatic function calling, helper classes, Python typing support, error checking, and a usage meter to help manage API costs. Additionally, aitemplates offers built-in examples of using ChromaDB and tools for efficient prompt engineering with OpenAI.FeaturesFunction Integration: Quick and easy integration with OpenAI functions for automatic execution and error handling.Error Checking: Automatically catch and handle errors during API calls.Usage Meter: Keep track of your OpenAI API usage with a built-in metering system.ChromaDB Integration: Work directly with ChromaDB from the aitemplates interface.Asynchronous Chat Completions: Use the-asncflag to run asynchronous chat completions. The built-inprint_everyoption prints every time a completion finishes in parallel. If you'd like to maintain the order of your completions, pass in thekeep_orderboolean asTrue.Prompt Engineering Examples: Get started quickly with included examples of prompt engineering techniques.Python Typing Support: Enjoy the benefits of Python's dynamic typing system while using OpenAI API.InstallationYou can install aitemplates directly from PyPI:pipinstallaitemplatesTo get the latest version of the package, you can also clone the repository and install the package by runningpip install -e .in the repository directory.Creating Jupyter Notebook TemplatesCreate Jupyter notebook templates for prompt engineering with the following command:aitemplatesname_of_notebookInclude a Chroma database in the template with the-dbflag:aitemplatesname_of_notebook-dbFor asynchronous chat completions, add the-asncflag:aitemplatesname_of_notebook-db-asncFor chat completions with function boilerplate add the-funcflag (async do not support functions):aitemplatesname_of_notebook-db-funcDocumentationThe package includes example notebooks, which provide comprehensive guides and demonstrations of the functionalities provided by aitemplates. To access these notebooks, access or clone the repository athttps://github.com/SilenNaihin/ai_templatesand navigate to the/notebooksdirectory.Here are the available notebooks:oai_examples.ipynb: Provides examples for the OpenAI API including functions.chroma_examples.ipynb: Demonstrates usage of ChromaDB.prompt_engineering_example.ipynb: A comprehensive guide on prompt engineering, including various techniques, usage of ChromaDB and the OpenAI library together.RequirementsBe sure to have a.envfile with theOPENAI_API_KEYdefined in the root of your project or your api key defined in some way for OpenAI. AWS env variables are for ChromaDB deploymenthttps://docs.trychroma.com/deploymentThis library doesn't currently support text models liketext-davinci-003. Streaming and logit_bias parameters are also not supportedContributingI welcome and appreciate contributions to aitemplates. If you'd like to contribute a feature, please submit a pull request. For bugs, please open a new issue, and I'll address it promptly.Future?https://twitter.com/ItakGol/status/1668336193270865921Support for other LLMsSupport for other dbs
Package: aiter
aiter -- Asynchronous Iterator PatternsPEP 525describesasynchronous iterators, a merging of iterators with async functionality. Python 3.6 makes legal constructs such asasync for event in peer.event_iterator:
    await process_event(event)which is a huge improvement over usingasync.Queueobjects which have no built-in way to determine "end-of-stream" conditions.This module implements some patterns useful for python asynchronous iterators.Documentation available onreadthedocs.io.Atutorialis available.github versionCAVEATThis project is still in its infancy, and I reserve the right to rename things and cause other breaking changes.
Package: aiter-timeouts
aiter-timeoutsTimeout functionality for asynchronous iterators. Supports timeouts on the total time to exhaust an asynchronous iterator, or the time it takes for any given step.Supports Python 3.7+.ExampleJust wrap your async iterator in a call towith_timeout, like so:fromaiter_timeoutsimportwith_timeoutasyncdefasync_iter():foriinrange(10):awaitasyncio.sleep(0.5)yielditry:asyncforvalinwith_timeout(async_iter(),timeout=6,timeout_per_step=1):...exceptIterationTimeoutErrorase:print(f"step{e.step}took too long")exceptIteratorTimeoutErrorase:print(f"only reached step{e.step}before timing out")LicenseMIT License
Copyright (c) 2023 Elias Gabriel
Package: ai-test-craft
AI Test CraftAutomated Unit Test Generation for PythonAITestCraft is a Python package that assists developers in generating unit tests for their code. It leverages OpenAI's GPT models to create test cases based on the structure and requirements of your codebase.Features:Automated generation of unit tests from JSON configuration.Support for Python 3.10 and other versions.Allows for additional comments to guide test case creation.Sequential file processing for context-aware test generation.Prerequisites:To use AITestCraft, you need an OpenAI API key. To obtain it, follow these steps:Sign up for an account atOpenAI.Navigate to the API section and follow the instructions to generate your API key.Before running AITestCraft, ensure that yourOPENAI_API_KEYenvironment variable is set with your OpenAI API key. Alternatively, you can specify a custom environment variable name for your OpenAI API key using the command-line interface.Installation:Install AITestCraft using pip:pipinstallaitestcraftUsage:First, set theOPENAI_API_KEYenvironment variable or a custom one:For Unix-based systems (Linux/Mac):exportOPENAI_API_KEY='your_api_key_here'For Windows:set OPENAI_API_KEY=your_api_key_hereAlternatively, use a custom environment variable:exportMY_ENV_VAR_NAME='your_api_key_here'To generate unit tests, create a JSON configuration file namedto-test.json:{"language":"python","language_version":"3.11","model":"gpt-3.5-turbo","overwrite":"never","additional_comments":["Use functions for each testcase and not unittest.TestCase"],"files":[{"code":"aitestcraft/conf_representation.py","test":"tests/test_conf_representation.py"},{"code":"aitestcraft/validators.py","test":"tests/test_validators.py"},{"code":"aitestcraft/ai_generator.py","test":"tests/test_ai_generator.py"}]}Then run the package with:aitestcraftto-test.jsonOr if you're using a custom environment variable for the API key:pythonaitestgen.py--open-ai-env-varMY_ENV_VAR_NAME./to-test.jsonConfiguration Fields:language: Programming language used.language_version: Version of the programming language.model: OpenAImodelused for generating tests.overwrite: ["neve"/"always"] If set to "never", existing test files will not be overwritten.additional_comments: Optional, global comments for test generation.files: A list of objects representing the source code and the test files.In thefiles, if a comment starts withAI-TEST, it indicates a message for the AI to include specific demands for the next line, like so:# AI-TEST: don't test this conditionNote:The generated tests may require minor adjustments to fit the exact needs of your project.Order the files into-test.jsonfrom the most standalone files to those with the most dependencies. This helps to provide context that can improve the quality of the generated tests.Disclaimer:The test generation is not guaranteed to be perfect and might need adjustments to work seamlessly with your codebase.
Package: aitestgen
AITestGenPython Library to generate unit tests using OpenAI APIInstallationCreate a virtual enviroment (Optional)python-mvenvvenvsourcevenv/bin/activate# venv/Scripts/activate on WindowsInstall package (require python ^3.11 version)pipinstallaitestgenAdd OpenAI API Key (Required)exportOPENAI_API_KEY=<my_api_key># or add variable in your .env fileExampleCreate a python file and add the following code:fromaitestgen.autotestimportautotest@autotest()defsum(num1:float,num2:float)->float:"""This function is responsible for calculating the sum of two numbers"""returnnum1+num2@autotest()defmul(num1:float,num2:float)->float:"""This function is responsible for calculating the multiplication of two numbers"""returnnum1*num2Important:To improve results, add type annotations and documentation to the function.Generate tests:Run the following command (change the filename)aitestgengenerate--inputfilesrc/operations.pyAnd then you will get the following result in a .py file created in a tests folder:Result# Function: mul - test1:deftest_mul_positive_numbers():assertmul(5.5,2)==11.0# Function: mul - test2:deftest_mul_negative_numbers():assertmul(-3,4)==-12.0# Function: mul - test3:deftest_mul_zero():assertmul(0,10)==0.0# Function: sum - test1:deftest_sum_positive_numbers():assertsum(3.5,2.5)==6.0# Function: sum - test2:deftest_sum_negative_numbers():assertsum(-3.5,-2.5)==-6.0# Function: sum - test3:deftest_sum_mixed_numbers():assertsum(3.5,-2.5)==1.0
Package: ait-gui
AMMOS Instrument Toolkit (AIT) GUIAIT GUI provides a framework for building a custom website for realtime
telemetry monitoring, commanding, and other MOS operations with only
minimal HTML and configuration file changes. Built on top of the AIT
Core libraries, AIT GUI provides web-development friendly access to the
underlying telemetry, commanding, EVR, system logging, and other AIT
Core functions in addition to a suite of pre-built UI Components and a
REST API to handle common MOS use cases.Getting StartedYou can read through theInstallation Pagefor
instructions on how to install AIT GUI.Browser RequirementsAIT GUI requires one of the following browsers:Firefox version 52.0 or newerChrome version 57.0 or newerSafari v10.1 or newer will likely work as well but compatibility is not regularly checked or guaranteed.Join the CommunityThe project’sUser and Developer Mailing Listis the best way to communicate with the team, ask questions, brainstorm plans for future changes, and help contribute to the project.This project exists thanks to the dedicated users, contributors, committers, and project management committee members. If you’d like to learn more about how the project is organized and how to become a part of the team please check out theProject Structure and Governancedocumentation.ContributingFor information on how to contribute please see theAIT Contributing Guide