Saravanan666/pypi-org huggingface dataset:
Package: aichat
.. image:: https://travis-ci.org/aira/aichat.svg?branch=master:target: https://travis-ci.org/aira/aichat======aichat======A chatbot framework for configuring and testing chatbot dialog specifications.Description===========`aichat` combines 2 approaches (more to come):1. pattern matching (grammar-based appraoch)2. search (retrieval-based approach)Like AIML `aichat` allows you to control context and build a graph of behaviors (edges are chatbot actions/utterances).You can hand-craft your behaviors or "machine learn" them from example dialog corpora.In the future `aichat` will incorporate a knowledge base (a grounding chatbot approach)Usage===========``` shell$ hey Bot, "What's up?"I heard you, but I'm not sure what you mean.```
Package: aichatbot
AI-Chatbot 🤖Python library for building custom AI Chatbot with just one line of code.Get StartedInstall the packagepip install aichatbotLoad the moduleimportaichatbotasbotCreate theintents fileby following format{"intents":[{"tag":"tag name","patterns":["example-1 of user query","example-2 of user query","example-3"],"responses":["bot response-1","bot response-2","bot response-3"]}]}Thepatternscontains a list of exampleexpected user query, which user will enter andresponsescontains the list ofbot response.Whenever a user inputs a query, bot will find the closest match with thepatterns, and then select a random response from the list ofresponsesspecified under that pattern name.Example{"intents":[{"tag":"greeting","patterns":["Hi","How are you","Is anyone there?","Hello","Good day"],"responses":["Hello, thanks for visiting","Good to see you again","Hi there, how can I help?"]},{"tag":"goodbye","patterns":["Bye","See you later","Goodbye"],"responses":["See you later, thanks for visiting","Have a nice day","Bye! Come back again soon."]}]}Create the modelfilenames={"intents":"./data/basic_intents.json","dir":"dumps"}bot_model=bot.Create(filenames,technique="bow")intents: Path to your intents file.dir: Specify the directory where you want to save the bot model.technique: Choose among [ bow | lstm | bert ]That's it 😊,Start your conversationbot.start(bot_model)Optional Parameter:end_conversation: Contains strings to stop the chatbot.end_response: Output Message when bot quitsExample:bot.start(bot_model,end_conversation=["/stop","quit"],end_response="Thankyou for your time :)")
Package: ai-chat-chain
simple chat context manageroverride everythingworks with any modelworks with any prompt recovery system (simple history, embedding search, whatever)chains functions welluses ai-functions and executes theminstallpoetry add or pip install this repo, for nowexample:fromai_chatimportAiConfig# only supports openai for now, todo: add other modelsfromai_chat.openaiimportOpenaiChatfromai_chat.storeimportSupabaseStorefromai_functionsimportAIFunctions# supports SqliteStore() as well as MemoryStore() for storing the chain of promptsstore=SupabaseStore()conf=AiConfig(id="persona-id-1",system="You are a silly friend.")session=OpenaiChat(store=store,ai=conf,thread_id="session-1")defsearch_google():...defget_calendar():...defadd_calendar():...session.functions=AIFunctions([search_google,get_calendar,add_calendar])print(session.chat("hello").content)# this contains the hello, and the reply in the contextprint(session.chat("cool").content)
Package: ai-chatter
ai-chatterAboutAI is pretty cool, but a lot of fuss around it is just chatter.
This package tries to provide a toolkit to easily implement basic applications using ChatGPT, so you can get your hands dirty, and see whether you can do something useful with it.It automatically takes care of some issues around interacting with a chatbot, like settings and session management, data persistence, communication, and gluing it together.Installationpip install ai-chatterLicenseai-chatteris copyright (C) 2023 Fynn Freyer.This program is free software: you can redistribute it and/or modify it under the terms of theGNU Affero General Public Licenseas published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
See the GNU Affero General Public License for more details.You can find a copy of the GNU Affero General Public Licensealong with this program.
You can also get a copy online athttps://www.gnu.org/licenses/.
Package: ai-chat-tool
AI-Chat-ToolAI-Chat-Tool is a tool that brings ChatGPT to the command line.To use this tool you will need a ChatGPT api key.Options--api-key(required) ChatGPT api key to use to access ChatGPT.--file(optional) File to write ChatGPT response to. Pass just the file(test.py) or the path and file(/first_dir/second_dir/test.py)--max-tokens(optional) The maximum number of tokens to generate in the completion. Default=7--temperature(optional) What sampling temperature to use. Default=0--line(optional) Line on a file where ChatGPT output will start.ExampleUse AI-Chat-Tool to create a Python function that checks for even numbersCLIpython -m ai-chat-tool --api-key=$CHATGPTKEY --file=test.py --max-tokens=2000 --line=4What can I do for you today?: Can you create a python function that checks for even numbers?
Text written to test.pyFile created by AI-Chat-Tooltest.pydefcheck_even(number):ifnumber%2==0:returnTrueelse:returnFalse
Package: aichopen-tools
aichopen-toolsRepository containing tools used by the AI-TeamJsonLoader: tool to load and dump json into filesLoggerFactory: tool to create a preconfigured logger using the singleton patternMake package publicly pip installableGenerate the distribution archivespython3-mpipinstall--upgradebuild
python3-mbuildUpload to TestPyPIpython3-mpipinstall--upgradetwine
python3-mtwineupload--repositorytestpypidist/*InstallationLocalFirst installpipinstall.If you have an existing install, and want to ensure package and dependencies are updatedpipinstall--upgrade.or pip uninstall aichopen-toolsRemoteFirst add your ssh key on GitHub and then pip install the repository as shown below:pipinstallgit+https://git@github.com/echOpen-factory/aichopen-tools.gitUsagefrom aichopen_tools.json_loader import JsonLoader

from aichopen_tools.logger import LoggerFactoryAdditionalYou can find examples of issue and pull requests templates in the directory .github
Package: aiclib
A declarative system to consume the NVP api.Current Build StatusUse of AIC wrapper libThe AIC wrapper command, or sentence, consists of two
parts:
- An object and its parameters
- A verb that acts on that objectTypical use looks as follows:
library.[possible parameters].object.[params].verbIt is possible for the object to be a collection of the
same type of object. The AIC wrapper lib will perform a
bulk operation on all of the objects.If an object is not given it is assumed that the user
wishes to create an object (this is finalized through the
CREATE verb).Object’s parameters are completely optional if they are
set through a ‘dot’ function. Parameters that are required
are set during the declaration of the object.A verb works on the object and is always the last portion
of a normal command.Querying using the wrapper libThe exception to the normal command pattern is when a user
wishes to query. A query works much like the typical use
but acts works as a modifer to the verb (an adverb).Typical query use is as follows:
library.[params].object.query.[params].verbThe object stated in the command is what query is looking
for. Parameters may be passed to the query to make the
search more precise.Extending the wrapper libThe creation of a custom entity requires that the entity,
somewhere in its inheritance chain, inherit from
core.Entity. For it to properly return responses from the
server it also needs to overload unroll.
Package: aiclipal
ai-assistantSmall terminal AI CLI pal for generating content.InstallationInstallaiclipal:pip install -U aiclipalUsageJust run from the command line. By default you need to have theAI_CLI_PAL_OPENAI_API_KEYenvironment variable exported to the command line containing your API key for OpenAI.Then runaiclipal -hto get more informationCopyrightCreated by Grzegorz Grzęda. Distributed under MIT license
Package: aicloudx
aicloud配合云平台使用，如果不在平台内部，程序包没有效果编译安装需要的软件： python3 -m pip install --user --upgrade setuptools wheel twine编译：python3 setup.py sdist bdist_wheel上传到测试服务器：python3 -m twine upload --repository-urlhttps://test.pypi.org/legacy/dist/aicloud-0.0.2-py3-none-any.whl安装：python3 -m pip install --index-urlhttps://test.pypi.org/simple/--no-deps aicloudx上传正式服务器：python3 -m twine upload dist/aicloudx-0.0.2-py3-none-any.whl安装：python3 -m pip install --no-deps aicloudx创建存放公共数据的bucket：mc mb minio/tfdatasets设置权限：mc policy set download minio/tfdatasets上传数据： mc cp --recursive tensorflow_datasets minio/tfdatasets
Package: ai-coach-xblock
AI Coach XBlockAI Coach Xblock helps learner in improving their answers by levering power of AI. It evaluates open response answer of a question using Open AI and provides feedback to learner.It has the followig features:Helps learner to get feedback on their open response answer using Open AILearner can improve answer on the basis of Open AI feedbackAbility to mark Question complete in open edXSave learner answer which can be retrieved laterSetuppipinstallai-coach-xblockUpdate  Settings of LMS and CMSAdd the OPEN AI secret key in the lms and cms configuration fileOPENAI_SECRET_KEY='set-secret-key'Update Advanced Settings of courseUpdate course advanced settings by addingai_coachas shown in below imageUpdate settings of AI Coach ComponentAI Coach Xblock will be available in Advanced component of course unit now. Add "AI Coach" xblock in unit and click "Edit" button to
addquestion&question contextand configure it.Contextis the context of question with answer which is provided to OpenAI for feedback generation. Question and Learner Answer is integrated into context using{{question}}and{{answer}}keywords which automatically picks question & learner aswer value.
Example:I am grade 8 student studying leadership. My teacher asked me question: {{question}} . I answered {{answer}} . Provide two tips to improve my answer as a grade 8 student.Publish ContentUse "Preview" button to preview it or publish your content and use "View Live Version" button to see how it appears on LMSAsk For FeedbackAfter adding answer, learner click onAsk from Coachbutton for feedback. Feedback is provided by OPEN AI on the basis of question context and answer.Setting the feedback thresholdThe feedback threshold represents the maximum number of times a learner can request feedback. You can configure the feedback threshold by accessing the AI Coach Component settings in the CMS.The tooltip which appears when hovering over theAsk from coachbutton represents the chances to request feedback from AI Coach.Once all available chances to request help from the coach have been utilized,Ask from coachbutton will be disabled and learners will see the following message on tooltip: "You have exhausted all available opportunities to seek guidance from the coach."Submitting AnswerSubmit button saves the learner answer and mark the completion of Xblock.
Package: aicoe-donkeycar
donkeycar: a python self driving libraryDonkeycar is minimalist and modular self driving library for Python. It is
developed for hobbyists and students with a focus on allowing fast experimentation and easy
community contributions.NOTE: this package is a non-official build by the AICoE. Please check the upstream project links for official information.Quick LinksDonkeycar Updates & ExamplesBuild instructions and Software documentationDiscord / ChatUse Donkey if you want to:Make an RC car drive its self.Compete in self driving races likeDIY RobocarsExperiment with autopilots, mapping computer vision and neural networks.Log sensor data. (images, user inputs, sensor readings)Drive your car via a web or game controller.Leverage community contributed driving data.Use existing CAD models for design upgrades.Get driving.After building a Donkey2 you can turn on your car and go tohttp://localhost:8887to drive.Modify your cars behavior.The donkey car is controlled by running a sequence of events#Define a vehicle to take and record pictures 10 times per second.importtimefromdonkeycarimportVehiclefromdonkeycar.parts.cvimportCvCamfromdonkeycar.parts.tub_v2importTubWriterV=Vehicle()IMAGE_W=160IMAGE_H=120IMAGE_DEPTH=3#Add a camera partcam=CvCam(image_w=IMAGE_W,image_h=IMAGE_H,image_d=IMAGE_DEPTH)V.add(cam,outputs=['image'],threaded=True)#warmup camerawhilecam.run()isNone:time.sleep(1)#add tub part to record imagestub=TubWriter(path='./dat',inputs=['image'],types=['image_array'])V.add(tub,inputs=['image'],outputs=['num_records'])#start the drive loop at 10 HzV.start(rate_hz=10)Seehome page,docsor join theDiscord serverto learn more.
Package: aicoe-hello-world
A small example demonstrating Python package releases uploaded using AICoE-CI
to Operate First Pulp instance.How ToTo trigger a new package release to Pulp:Make sure you are stated as a maintainer of the package in.thoth.yamlfile (in “version” manager section)Trigger a new release by opening an issue - followKebechet version manager
documentation for more infoOnce the PR is opened and all CI checks pass, the pull request adjusting
version identifier is automatically mergedThe pipeline will release the Python package to the Pulp instance,
respecting.aicoe-ci.yamlconfiguration present in Git rootTo check progress, visitTekton on Operate First (PipelineRuns)and in the search bar type:project:<github-project-name>(e.g.project:aicoe-ci-pulp-upload-example)
Package: ai-commands
ai_commandsOverviewai_commandsis a Python package designed to simplify common AI-related tasks such as performing sentiment analysis on text data. It provides a collection of functions and utilities to streamline various AI tasks, making it easier for developers to work with artificial intelligence and natural language processing.FeaturesText Variation Generation: Use theai_similarisefunction to generate text variations by replacing words with synonyms and shuffling sentence structure.Sentiment Analysis: Employ theanalyze_sentimentfunction to perform sentiment analysis on text, providing sentiment labels and scores.InstallationYou can installai_commandsusing pip:pip install ai-commandsUsageHere's how you can use ai_commands in your Python projects:import ai_commands

# Generate text variations
original_message = "Sorry, you lost the game!"
similar_message = ai_commands.ai_similarise(original_message)
print("Similar Message:", similar_message)

# Perform sentiment analysis
text_to_analyze = "I love this product. It's fantastic!"
sentiment_result = ai_commands.analyze_sentiment(text_to_analyze)
print("Sentiment Analysis Result:", sentiment_result)DocumentationFor detailed usage instructions and additional functions, please refer to the documentation.Support and Contributionsai_commands is an open-source project, and contributions from the community are welcome! If you have ideas for improvements or new features, please check out our contribution guidelines.Licenseai_commands is licensed under the MIT License.
Package: aicommit
autocommitAI-Generated Git Commit MessagesIncludes two tools:commit.pyandscan_repo.pyInstall theaicommitcommand withpip install aicommitcommitAn interactive CLI tool that generates 5 commit message suggestions for all the changes in your current Git repo. After you pick and edit the commit message you want, it performs the commit.NOTE:it commits all changes, untracked and unstaged, in your current repo.On first run, it will prompt you for your OpenAI API key. Sign up for OpenAI if you haven't. Grab your API key by going to the dropdown on the top right, selecting "View API Keys" and creating a new key. Copy this key.scan_reposcan_reporuns through all the commits in your repository to generate a CSV with AI-suggested commit messages side-by-side with your original commit messages.To run scan_repo, copy.env.exampleto.envand add your OPENAI_KEY.To update the repo it runs on, modify theGITHUB_REPO_URLvariable at the top ofscan_repo.pyPublishing to pipVersion bump and clear outdist/python3 -m build
twine check dist/*
twine upload dist/* --verbose
Package: ai-commit-gen
Ai-Commit-GenAi-Commit-Gen is a Python binary that leverages OpenAI's language models to generate meaningful commit messages. There's approximately a thousand such binaries out there, but none that fit into all of my workflows, so here's the 1001st.ai-commit-gen can be called from scripts to automatically create commits, or integrated into editor workflows to create commit drafts.Installationpipinstallai-commit-genUsageai-commit-gen assumes that the current working directory is, or is a subdirectory of, the git root.-por--prompt: Override the default prompt for the OpenAI model.-cor--commit: Not only generate a commit message but also commit the changes in git.-mor--model: Specify the model to be used. The default is "gpt-3.5-turbo". The models "gpt-4" and "gpt-4-32k" are also supported.-dor--debug: Enable debug logging.OpenAI API KeyThe OpenAI API key can either be set as an environment variableOPENAI_KEY, or it can be stored in the system keyring, which is how I use it.If the key is not found in either of these locations when the script is run, you will be prompted to enter the key and given the option to store it in the keyring.ExampleHere is an example of how you might use Ai-Commit-Gen:# Stage your changesgitadd.# Just create the commit messageai-commit-gen# Generate a commit message and commit the changesai-commit-gen--commitIn the above example, Ai-Commit-Gen will consider the staged changes, generate a commit message using the OpenAI model, and directly commit the changes with the generated message.NotePlease make sure that you have the required permissions and credits to use the OpenAI API.ContributeFeel free to open an issue or submit a pull request if you have any issues or feature requests.
Package: ai-commits
An AI-powered git commit message generator written in python. Inspired by @NutlopeDescriptionAICommits is a Python script that generates a git commit message using OpenAI's GPT-3 language model. The script takes the git diff of the staged changes and generates a commit message using GPT-3.The generated commit message is presented to the user who has the option to accept or reject it. If the user accepts the commit message, the script commits the changes using the generated message.The script requires an OpenAI API key, which can be obtained from the OpenAI website.DependenciesPython 3.6 or higher
openai 0.10.2
inquirer 3.7.0
python-dotenv 0.19.1FeaturesIn this version, AICommit provides the following features:Automatic generation of commit messages based on the diff of staged changes.Support for conventional commit messages.Language selection for generated commit messages.User selection of generated commit messages.Support for specifying diff per file.Installation and UsageTo use AICommits, do the following:Export your OpenAI API key as an environment variable by running:exportOPENAI_API_KEY=YOUR_API_KEYinstall aicommit from pypi by running:pipinstallai_commitsThen, run the script using the commandaicommit.This will prompt AICOMMIT to generate up to 5 commit messages based on the diff of staged changes. The user can then select one of the generated commit messages to use as the commit message for their changes.If the --diff-per-file flag is included, AICOMMIT will generate separate commit messages for each file.If the --conventional flag is included, AICOMMIT will generate commit messages in conventional commit format.If the --language flag is included, AICOMMIT will generate commit messages in the specified language.Install from SourceTo install AICommits from source, follow these steps:Clone the repository using the command git clonehttps://github.com/nneji123/aicommit.git.Navigate to the project directory using the commandcd aicommit.Install the required dependencies by running pip install -r requirements.txt.Set your OpenAI API key as an environment variable by running export OPENAI_API_KEY=YOUR_API_KEY.Run the script using the commandpython "src/ai_commits.py".The script will prompt you to confirm the generated commit message. If you accept the message, the changes will be committed using the generated message.Changelogv1.0.0 (2023-02-16)Added support for generating commit messages in multiple languagesImproved performance when generating commit messages for large diffsAdded ability to select from multiple generated commit messages using the inquirer libraryFixed various bugs and improved error handlingv0.0.5 (2023-02-15)Fixed a bug that caused the script to fail when no changes were stagedv0.0.4 [Initial Release] (2023-02-15)Initial release of aicommitsSupports generating conventional commit messagesSupports generating commit messages based on the changes in the staged filesUses OpenAI's GPT-3 to generate commit messagesContributingOpen an issue or submit a pull request if you want to contribute to the project.LicenseMIT
Package: aicompleter
AI-CompleterA totally automatic AI to interact with environments.Now it only supports OpenAI API.SetupNote:Please use stable branch if the dev branch is brokenClone the Repositrygitclonehttps://github.com/AI-Completer/AI-Completer.gitcdAI-CompleterModify the config fileYou can use either vim or your editor to modify the fileconfig-example.json, and save it toconfig.jsonRun and enjoy yourselfNote: Use Python 3.11+ to run this program.python3-maicompletertalk--aibingai# This will start a conversation with bing ai.python3-maicompleterhelper--enable-agent--includepythoncode# This will start a helper with OpenAI APIYou can add custom interface to this program to add more function.Quick StartThe code below will start a talk with gpt, the detailed model varies with your configure, default is gpt-3.5-turbo.fromaicompleterimport*fromaicompleter.implementsimport*importasynciocfg=Config({# 'openaichat' is the namespace, you can change to your own namespace'openaichat':{'openai':{'api-key':'sk-...',# Put you OpenAI API key here'model':'gpt-3.5-turbo',# AI model, default is gpt-3.5-turbo}}})# ChatAI, use openai model gpt-3.5-turbo-0301chater=ai.openai.Chater(cfg['openaichat'])# Chat Interface, based on chater -> OpenAI APIchatinterface:ai.ChatInterface=ai.ChatInterface(ai=chater,namespace='openaichat')# Handler, interacting between interfaceshand:Handler=Handler(cfg)asyncdefmain():# Add Interfacesawaithand.add_interface(chatinterface)# Start a new sessionsession:Session=awaithand.new_session()print("Please start a conversation")whileTrue:# Get input from consoleword=input(">>> ")ifword=='exit':break# Send a ask command to the chat interface, the ai is asked by the content (text, the question of user)print(awaitsession.asend('ask',word))# Start the loopasyncio.run(main())ExamplesSeeexamplesDocumentReference:DocumentTo-do ListWe are adding more support to this program.Add Commands Intergation with AI model.Add Commands SupportAdd memory systemAdd HistoryAdd Memory AnalyseAdd Extra Interface Package