Saravanan666/pypi-org huggingface dataset:
Package: chatcli
ChatCLIChatCLI is a command line interface for OpenAI's Chat API.ChatCLI aims to provide a similar experience the ChatGPT frontend; like ChatGPT, ChatCLI streams tokens from the model as they are generated.$chatcliChatCLI v0.1.5 | [↩] to submit | [ALT/⌥] + [↩] for newline>>> Write 3 funny prompts for yourself.1. If you could only communicate through interpretive dance for the next 24 hours, how would you go about your day?2. You wake up in a world where everyone speaks in rhyme. How do you adapt to this unusual circumstance?3. You can only speak in questions for the rest of the day. How do you navigate conversations with friends, co-workers, and strangers?InstallationTo install ChatCLI, use pip:pip install chatcliYou'll need an OpenAI API key to use ChatCLI. You can get onehere.ChatCLI reads your key from theOPENAI_API_KEYenvironment variable, so you'll also need to set that.UsageCLI UsageTo use ChatCLI, simply run thechatclicommand:chatcliThis will start a chat session with the default OpenAI model. You can type your messages and the AI will respond.You can exit the chat at any time by typing "exit" into the prompt or pressing Ctrl+C.Python UsageChatCLI can also be used as a Python library. Here's an example:importchatcli# Create the chat generatorgenerator=chatcli.ChatGenerator()# Send a message to the generatorresponse=generator.send("Prove Riemann's Hypothesis.")# Print the responseprint(response["message"]["content"])This will send a message to the API and then print the response stream as it is generated.LicenseChatCLI is licensed under the MIT license. SeeLICENSEfor details.
Package: chat-cli
InstallationI recommend usingpipxfor installation. Followpipx's documentationto install it, then runpipx install chat-cli.BuildingChat uses thepoetrybuild tool.Install poetryClone the chat project repoRunpoetry buildin the project rootThe wheel and sdist will be built in thedist/directoryUsagechat [-h] [--apikey APIKEY] [--prompt PROMPT] [--save] [-f CONFIG] [-m MODEL] [--resume] [-s SEARCH]Options-h,--helpshow these options and exit--apikeySpecify the API key to use when connection to OpenAI--promptChange the prompt that is displayed for input. The sequences %t and %T can be used to display the current token and max token count for the model, respectively. Default prompt is '%t/%T > '--saveIf specified with --apikey and/or --prompt, save them to the config file-f,--configSpecify an alternate file to load the configuration from (Default: $XDG_CONFIG_HOME/Chat/chat.json)-m,--modelSpecify the model to use, refer tohttps://platform.openai.com/docs/modelsfor list--resumeResumes last conversation-s,--searchSearch your chat history for a given regexCommandsWhen chating, the following commands perform special actions.redoRegenerates the previous response. Removes the old response from the history.copyCopies the first code block in the previous response to the system clipboard. Print's whats been copied.exitExits the application and saves the chat history. Same as encounteringEOF. Prints the chat log location..summarizeAsks the model to summarize the conversation and starts a new conversation with the summary as the first message. Saves the conversation prior to the summary. Should be called before reaching the token limit to continue a conversation.
Package: chatclouds
Overview:Chatclouds is a wordcloud library which allows you to create and save a wordcloud of your whatsapp chats. The library uses wordcloud library to generate images. The preprocessing of the text from the txt files are already taken care of. The only thing you need to do is to extract your chat from WhatsApp and then using that file in your python code.Importing chatclouds:import chatcloudsFunctions:show()Code:from chatclouds import show
show(filename)
# filename needs to be the name/ path of txt file containing your WhatsApp Chat.What it does:
show() function returns an image of the WordCloud that has been formed from the chat after preprocessing the text and removing all the stopwords.save()Code:from chatclouds import save
save(filename)
# filename needs to be the name/ path of txt file containing your WhatsApp Chat.What it does:
show() function saves a .png image of the WordCloud that has been formed from the chat after preprocessing the text and removing all the stopwords.plot_time()Code:from chatclouds import common
plot_time(filename)
# filename needs to be the name/ path of txt file containing your WhatsApp Chat.What it does:
plot_time() function plots and shows a bar plot of number of messages on the y axis and 1 hour time slots on x axis.common()Code:from chatclouds import common
common(filename)
# filename needs to be the name/ path of txt file containing your WhatsApp Chat.What it does:
common() function prints the 20 most frequently used word in the chat between two people.Change Log0.0.4 (23/11/2020)Fourth Release0.0.5 (24/11/2020)Fifth Release
Package: chat-companion
CLI CompanionCLI Companion is a command line tool that you can use to ask questions and get responses back from a virtual companion. You can also review previous questions and responses.InstallationFirst you need get an api key for chat-gpt.
Then you create an environment variable named$CHATKEYbashexport CHATKEY=<your-key>fishset CHATKEY=<your-key>Using pippip install chat-companionUsing Poetrypoetry add chat-companionUsageruncompanion --helpTo see the list of commands.custom commands
===============
generate_response  proof_read   review     talk     
help               resummarize  summarize  translateTalkTo ask a question, use thetalksubcommand. For example:companion talk "What is your name?"ReviewTo review previous questions and responses, use thereviewsubcommand. This will bring up a list of previous questions. You can then select a question to view the response.
Package: chatdollkit
ChatdollKit Server SDK for PythonSDK to create remote skill server for ChatdollKit. See also 👉ChatdollKit🇯🇵日本語のREADMEはこちらInstall$pipinstallchatdollkitAlso install application framework you like. We supportFlaskandFastAPI.$pipinstallflaskOr$pipinstallfastapiuvicornQuick startServer sideRun example API server that just echo what user says.$pythonrun_flask.pyOr$uvicornrun_fastapi:app--port12345--reloadClient sideSeeChatdollKit Documentation > Setup Skill client (ChatdollKit device)to useExamples/SkillServer.If you want to run skill server on host other than localhost configure URLs on the inspector ofHttpSkillRouterandHttpPrompter.After setting up both server and client, run your Chatdoll app and start conversation. Your 3D model will echo what you say.Create your own Skill ServerBasically Skill(s), Server application and Entrypoint are required to create your Skill Server as following chapters.First of all, makeallinone.pyand import required libraries.fromflaskimportFlaskfromchatdollkit.appimportSkillBase,AppBasefromchatdollkit.modelsimport(Request,Response,State,IntentExtractionResult,Intent)fromchatdollkit.controllers.flask_controllerimportbpasapi_bp1. SkillMakeEchoSkillclass that extendsSkillBaseand implementprocessmethods to return response that includes a Text-to-Speech voice request.classEchoSkill(SkillBase):topic="echo"defprocess(self,request:Request,state:State)->Response:# Just echoresp=Response(Id=request.Id)resp.AddVoiceTTS(request.Text)returnresp2. Server applicationMakeMyAppthat extendsAppBaseand implementget_promptmethods that requires voice input to user andextract_intentto route toEchoSkill.classMyApp(AppBase):# Register skill(s)skills=[EchoSkill]defget_prompt(self,request:Request,state:State)->Response:# Return prompt messageresp=Response(Id="_"ifrequestisNoneelserequest.Id)resp.AddVoiceTTS("This prompt is from server. Please say something.")returnrespdefextract_intent(self,request:Request,state:State)->IntentExtractionResult:# Always extract Echo intentreturnIntentExtractionResult(Intent=Intent(Name=EchoSkill.topic))3. Application entry pointLastly, Add the instance ofMyAppto Flask application and register API controller blueprint to app.# Create Flask appapp=Flask(__name__)# Create ChatdollKit server app and set it to Flask applicationapp.chatdoll_app=MyApp(app.logger,True)# Register API controllerapp.register_blueprint(api_bp)if__name__=="__main__":# Start APIapp.run(port="12345",debug=True)See the example if you want to create FastAPI-based skill server.Use other application frameworkTo use application framework other than Flask and FastAPI, create controller that handles http request from ChatdollKit client by your self. You can reuse Skill, Server application and models. Seechatdollkit.controllers.flask_controller.pyorfastapi_controller.py.
Package: chatendpoint
# chatendpoint | ChatrHubCreate custom actions for ChatrHub's automated chat agents https://chatrhub.com.  (Create a free account if you dont have one already here: https://chatrhub.com/signup/index.html).  Use this module when you want your chat dialog to interface with internal systems such as inventory or user management data stores.You can pass variables collected in the dialog to the endpoint for processing and you can also pass variables back to the dialog to display to the user.## Prerequisites- Linux Server- Python3.6## Getting Started### 1. Installpip install chatendpoint### 2. Create Endpoints```pythonfrom chatendpoint.chatendpoint import ChatEndpointsdef create_user(dialog_variables):# All dialog variables will be available in the dialog_variables dictionary# dialog_variables example:#   {#       'literal': 'F-150',#       'value': 'F-150',#   }# Using the dialog variables, access your data store, manipulate data, and# return a dictionary which will add/update variables back to dialog.return {'user_created': 'success'}if __name__ == "__main__":ce = ChatEndpoints()# Create as many endpoints as you need.ce.add_post_endpoint(path='/create_user', data_processor=create_user)ce.start(port=8888)```- See demos/car_dealership_demo.py for a more in-depth example- These endpoints will allow you to receive dialog variables, process the data as needed, then return any variables that should be be added or updated to the dialog.- Default port is 8888### 3. Dialog SetupIn the ChatrHub portal make sure you collect any variables via questionnaires before sending to your action endpoint.  Setup a free account at https://chatrhub.com/signup/index.html### 4. Open FirewallOpen port 8888 (Or whatever port you selected) to the ChatrHub IP address obtained by sending an email to admin@chatrhub.com.### 5. Run serverRun the server in the background by running the script created in step 2.## LicenseThis project is licensed under the MIT License.=======History=======0.1.0 (2018-07-10)------------------* First release on PyPI.
Package: chatette-qiu
Chatette-qiuChatito是用于创建聊天机器人训练数据集的自然语言生成工具
原始代码是参考https://github.com/SimGus/Chatette实现Intent of this project这个项目的目的和初衷，是为了更好的适配自己的需求。
比如：input的时候只能传一个文件，不支持传目录Quick Installpip install chatette_qiu
生成训练数据命令：
        python -m chatete_qiu input_file_or_folder -o output_path


        input_file_or_folder  输入的chatette模板文件路劲或者文件夹
        output_path           结果输出目录
Package: chatfaq_cli
ChatFAQ Command Line Interface (CLI) ToolThe full potential of ChatFAQ services, datasets, and models at the tip of your fingers.PrerequisitesMake sure the next list of packages are installed on your system:Python 3.10poetryInstallationLocal buildpoetry installPYPIpip install chatfaq-cliUsageFirst of all you should configure the remote target server:chatfaq config host <REMOTE_ADRESS>Then you can log in into the remote server:chatfaq config auth <TOKEN>You can retrieve the auth token from the backend server:curl -X POST -u username:password http://localhost:8000/back/api/login/by using an admin's user and password.Commandschatfaq --helpPublish packagePYPI testadd repository to poetry configpoetry config repositories.chatfaq-cli https://test.pypi.org/legacy/get token fromhttps://test.pypi.org/manage/account/token/store token usingpoetry config pypi-token.chatfaq-cli pypi-YYYYYYYYPYPI productionget token fromhttps://pypi.org/manage/account/token/store token usingpoetry config pypi-token.chatfaq-cli pypi-XXXXXXXXEach time you need to publishBump versionpoetry version prereleaseorpoetry version patchPoetry Buildpoetry buildPoetry PublishTo TestPyPipoetry publish -r chatfaq-cliTo PyPipoetry publish
Package: chatgdb
ChatGDBHarness the power of ChatGPT inside the GDB/LLDB debugger!ChatGDB is a tool designed to superpower your debugging experience with GDB or LLDB, debuggers for compiled languages. Use it to accelerate your debugging workflow by leveraging the power of ChatGPT to assist you while using GDB/LLDB!It allows you to explain in natural language what you want to do, and then automatically execute the relevant command. Optionally, you can ask ChatGPT to explain the command it just ran or even pass in any question for it to answer. Focus on what's important - figuring out that nasty bug instead of chasing down GDB/LLDB commands at the tip of your tongue.ContentsInstallationUpdatingUsageContributingGetting UpdatesInstallation instructionsFirst, make sure you installpip. ChatGDB also
requires a python version of 3.3 or above.To install, run the commandpip3 install chatgdb.It will create an executable calledchatgdbthat you will have to use to set your api key.
To do that, run the commandchatgdb -k <API KEY>You also need to set the model to use. There are two possible options,gpt-3.5-turboandgpt-4:chatgdb -m <MODEL>This information is stored in text in the same directory as the installed script, which is currently in your python site packages
folder along with the main script. You can easily find this location by running the following in your terminal:python -m site --user-siteOptionally, you can also download the compressed files in the releases page to get the scripts directly.
If you do this, navigate to thechatgdbfolder, and you can install withpip3 install ..UpdatingTo update ChatGDB, run the followingpip3 install chatgdb --upgradeUsageFor GDB usage, I first recommend editing your$HOME/.gdbinitto source the main script automatically on startup. Run the following command:echo "source $(python -m site --user-site)/chatgdb/gdb.py" > $HOME/.gdbinitThe same applies for LLDB. Edit your$HOME/.lldbinitand run the following command:echo "command script import $(python -m site --user-site)/chatgdb/lldb.py" > $HOME/.lldbinitWhile inside your debugger, you can run the command chat appended by your query, for examplechat list all breakpoints that I created.
There is also a command calledexplainthat you can use with no arguments to explain the previously run command,
and optionally, with a query to just ask GPT a question. For example, runningexplaindirectly after runningbreak 7would prompt the tool to explain how breakpoints work. Runningexplain how input formatting works in gdbwould prompt it to explain input formatting (see the image at the top).Run chat help to print out a short tutorial on how to use the tool.ContributingThanks for your interest in contributing to ChatGDB! SeeCONTRIBUTING.mdon ways to
help the development effort.Staying UpdatedIf you'd like to stay up-to-date on new features/fixes, follow mytwitter. There's plenty
of exciting features on the horizon such as complete context-awareness that will make it possible
for ChatGDB to not only help you use GDB, but to help you fix the code itself.
Package: chatglmclient
chatglmclientChatGLM Client，对 api 访问进行封装，方便调用构建在项目文件夹中运行以下命令pythonsetup.pysdistbdist_wheel上传：python-mtwineupload--repository-urlhttps://upload.pypi.org/legacy/dist/*
或
python-mtwineuploaddist/*按照提示，输入pypi的用户名、密码，就可以成功了。若中途提示有些库没有安装，则使用pip安装一下，需要用到twine库。使用安装或更新pip3 install chatglmclientpip3 install --upgrade chatglmclient代码使用fromchatglmclient.chatglm_clientimportChatGLMClientclient=ChatGLMClient(params={'api_host':"http://192.168.1.111:5000"})prompt={"prompt":"一只狗有几条腿","history":[]}result,info=client.chat(prompt)print(result,info,)
Package: chatgpt4pcg
ChatGPT4PCGChatGPT4PCG is a python package containing required and utility functions as a part ofChatGPT4PCGcompetition.InstallationUse the package managerpipto install ChatGPT4PCG.pipinstallchatgpt4pcgDependencyThis file uses the following Python libraries:openaiFunctionsrun_evaluation(team_name: str, fn: Type[TrialLoop], num_trials=10, characters: list[str] = None)This function runs a trial for each character in the alphabet for a given team. It creates directories for logging and
output, and generates a log file with a timestamp and timezone in the filename. It then runs trials for each character,
skipping any that already exist.run_trial(ctx: TrialContext, fn: Type[TrialLoop])This function runs a single trial. It writes the result of the trial to the log file and the final response to a text
file in the output directory.chat_with_chatgpt(ctx: TrialContext, messages: []) -> list[str]This function chats with ChatGPT. It sends a list of messages to the ChatGPT model and writes the response and token
counts to the log file. It also checks for time and token limits, raising errors if these are exceeded.UsageTo use this file, import it and call therun_evaluationfunction with the team name and trial loop function as
arguments. You can also specify the number of trials to run and the characters to run trials for.fromchatgpt4pcg.competitionimportrun_evaluation,TrialLoop,TrialContext,chat_with_chatgptclassZeroShotPrompting(TrialLoop):@staticmethoddefrun(ctx:TrialContext,target_character:str)->str:message_history=[{"role":"user","content":"Return this is a test message."}]response=chat_with_chatgpt(ctx,message_history)returnresponse[0]run_evaluation("x_wing",ZeroShotPrompting)
Package: chatgpt-api
💻 ChatGPT APIUnofficial API client and CLI for ChatGPT.This project is based onhttpx(uses only HTTP) and usesTyper (with Rich)for CLI, so responses are looking good (markdown is supported). Also, It has 100% code coverage unlike other ChatGPT packages.StatusChatGPT API often changes. I'm trying to make updates as soon as possible. So, if something is not working properly and you recently didn't upgradechatgpt-api, try to do this first:pipinstall--upgradechatgpt-apiIf that doesn't help, please open an issue.Installationpip install chatgpt-apiUsageAs a Command Line InterfaceSetupRequired to authenticate. In this step you have to provide session key.chatgptsetupStart chattingchatgptstartAs an APIChatGPTclass inherits fromhttpx.ClientRecommended usage:fromchatgpt.apiimportChatGPTwithChatGPT(session_token="your-session-token")aschat:response=chat.send_message("Hello!")print(response.content)Without context manager you have to explicitly authenticate:fromchatgpt.apiimportChatGPTchat=ChatGPT(session_token="your-session-token")chat.authenticate()response=chat.send_message("Hello!")print(response.content)chat.close()How to acquire session key?After you log in to ChatGPT in your browser, get value of__Secure-next-auth.session-tokencookie. In this project, this is named as a "session key".Chrome instructionOpen ChromeDevTools (F12).Click on "Application" tab.Click on "Cookies", on the left bar.Copy the value of__Secure-next-auth.session-token:Now, you can use it in CLI or directly from Python code.LicenseDistributed under the MIT License. SeeLICENSEfor more information.DisclaimerThis is a personal project, not affiliated in any way with OpenAI. If you have any objections, please contact me.
Package: chatgpt-api-cli
ChatGPT CLIA simple python CLI wrapper for theChatGPT API.Installation$ pip install chatgpt-api-cliSetup$ chatgpt init "your openai API key"UsageSingle prompts:$ chatgpt prompt "Explain the current state of the economy as if you're Christopher Moltisanti from the Sopranos"


"Listen up, Tony. The economy? Fuggedaboudit. It's in the shitter. The recession's got us all by the balls, and businesses are falling like dead flies. The GDP's down, unemployment's up - it's a real shitshow. People are struggling just to make ends meet, while the fat cats on Wall Street keep raking in the dough. And we all know who's responsible for this mess - the damn politicians and their crooked deals. I'm telling you, Tone, this whole system is screwed up, and it's getting worse every day. It's enough to make a guy wanna start his own damn business, just so he can make a buck without getting screwed over by the man."Continuous chat:$ chatgpt chat
To exit, type `exit` or press CTRL+C

You: why did the chicken cross the road?
ChatGPT: As an AI language model, I don't have access to the context as to why the chicken might cross the road. However, the most common answer to that age-old question has been "to get to the other side."
Package: chatGPT-automator
ChatGPT AutomatorRegarding automating ChatGPT using Selenium in Python, it's an intriguing approach that combines the power of conversational AI with web automation tools. ChatGPT, as we know, is a state-of-the-art language model by OpenAI, capable of producing human-like text based on the input it receives. On the other hand, Selenium is a widely used tool for controlling web browsers through programs and performing browser automation. It's primarily used for automating web applications for testing purposes, but it's versatile enough for various other tasks as well.By integrating ChatGPT with Selenium in a Python environment, one can potentially automate various web-based tasks. For instance, it might be possible to build a system where ChatGPT can interact with web platforms, fill forms, retrieve information, or even interact with users on web interfaces autonomously. This fusion can pave the way for more interactive and dynamic web applications, especially when they are AI-driven.Such an integration would require a deep understanding of both ChatGPT's capabilities and Selenium's features. Given the potential applications and the synergy between conversational AI and web automation, this could be a promising area of exploration for developers and enthusiasts alike.InstructionsInstall:pip install chatGPT-automatorHow to initialize:fromchatGPT_automatorimportChatGPTAutomatorchatgpt=ChatGPTAutomator()login_check (bool):Specifies whether to check for a successful login when initializing the ChatGPT session. If set to True, the automator will verify that the login was successful before proceeding with any further actions. The default value is True.How to interact with ChatGPT:question="How ChatGPT Ignited the A.I. Competition Wave"chatgpt.send_prompt_to_chatgpt(question)# wait a moment ...answer=chatgpt.return_last_response()print(answer)
Package: chatgpt-bot
ChatGPT BotThis is a small library that tries to make it easier to create a ChatGPT bot. It has
a simple interface that keeps conversational context in a SQLite database:fromchatgpt_botimportConversation>>>conversation=Conversation("some random ID",api_key="YOUR_OPENAI_API_KEY")>>>conversation.ask("Hi, how are you today?")"As an AI language model, I don't have feelings, but I'm always ready to assist youwithanyquestionsortasksyouhave.HowcanIhelpyoutoday?">>>conversation.set_metadata({"anything":"here"})>>>conversation.get_metadata(){"anything":"here"}
Package: chatgpt-client
ChatGPT ClientPython client for the unofficialChatGPTAPI byOpenAI.InstallationTo install the ChatGPT Client, run the following command:pipinstallchatgpt-clientUsageSign up for anOpenAI API keyand store it in your environment.importasyncioimportosfromchatgpt_clientimportChatGPTClientasyncdefmain()->None:api_key=os.getenv("OPENAI_API_KEY")client=ChatGPTClient(api_key)response=awaitclient.get_completion("What is the meaning of life?")print(response["choices"][0]["text"])if__name__=="__main__":asyncio.run(main())LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.