Saravanan666/pypi-org huggingface dataset:
Package: chatgpt-mixin
chatgpt-mixinDemo bot: 7000101691Installationon unix like platform, installchatgpt-mixinwith the following command:python3-mpipinstall-Uchatgpt-mixinon the Windows platform, use the following command to installchatgpt-mixin:python-mpipinstall-Uchatgpt-mixinInstall chatgpt-mixin with the support of accessing openai model in a browser:python3-mpipinstall-Uchatgpt-mixin[browser]playwrightinstallfirefoxconfigurationFirst, you need to create a mixin bot fromdevelopers.mixin.one.
And then under theSecrettab, clickEd25519 sessionto generate an App Session configuration.To get started, you will need at least one ChatGPT account. If you don't have one already, you can create an account atchat.openai.com. Additionally, you can create an api key atplatform.openai.com/account/api-keys."After that, you can start this bot with the following command:python3-mchatgpt_mixinbot-config.yamlwhich bot-config.yaml contains mixin bot configuration and chatgpt accounts as shown below.bot_config:pin:""client_id:""session_id:""pin_token:""private_key:""openai_api_keys:[]accounts:-user:""psw:""bot_configsection specify mixin bot configure.openai_api_keyssection specify openai api keys.accountssection specify chatgpt test accounts.userfield can not be empty, but you can leavepswto empty. If it is left empty, the user will need to manually enter the password upon login. Multiple accounts can be specified in the accounts section to improve ChatGPT responses. You can leaveaccountssection to empty if you only need to access openai models withopenai_api_keys.If you are running a bot of accessing model via browser in a server, you need to installXvfbon the server, and useVNCat the client side to connect toXvfb. For more information, refer toRemote_control_over_SSH.On the first time you start this bot, automated processes such as auto-filling of account names and passwords will be carried out, but you will still need to manually solve CAPTCHAs during the login process.Helpfull CommandsThis is a list of helpful commands to use with the bot./roleThe/rolecommand is used to get or set the role of the bot.Set roleTo set the role of the bot, use the following format:Usage:/role <role description>Example:/role You are a helpful assistantGet roleTo get the current role of the bot, use the following command:/roleThe bot will respond with the current role./reset_roleThe/reset_rolecommand is used to reset the role of the bot to the default role.Usage:/reset_role/resetThe/resetcommand is used to clear the context of the bot.Usage:/resetThese commands should help you better interact with the bot.Acknowledgementschatgpt-apichatGPT-telegram-botChatGPT
Package: chatgpt-plugin
ChatGPT PluginThe ChatGPT Plugin simplifies chatbot creation by reverse engineering OpenAI's ChatGPT project. To build your own
LLM chatbot,all you need is an OpenAPI specification. LLM chatbots offer powerful features out of the box such as:Responds and understand multiple languagesAble to generate responses by utilising past messagesGood understanding of user intentions...Getting Startedpipinstallchatgpt-pluginHow does it workThe framework generates for you an JSON that contains:The endpoint that needs to be calledThe endpoint body & params that needs to be passed to fulfill the users wishA response message for each documented caseFor the case of anTODO app:fromchatgpt_plugin.pluginimportChatPluginwithopen("todo_openapi.json")asf:openapi_specs=f.read()todo_plugin=ChatPlugin(openapi_specs=openapi_specs)todo_plugin.chat(message="Add buying frozen pizza as an todo")The plugin framework will generate following python object:endpoint='/todos/' 
endpoint_params={} 
endpoint_body={'title': 'Buy frozen pizza', 'description': 'Remember to buy frozen pizza', 'completed': False} 
message=[MessageResponse(response_message='Todo created successfully', response_code=200), 
         MessageResponse(response_message='Validation Error', response_code=422)]ExamplesYou can find more examples under theexamplesfolder.Tips and TricksHere are some recommendations to make full use of this framework.Add a description to your query parameter to make the usage more clearMake the language specific e.g. if your query parameter only accept English then specify it in the descriptionAdd all possible responses. If your API can fail for a reason, document it.LicensesApache License Version 2.0, seeLICENSE
Package: chatgptprtk
ChatGptPrtkChatGPT is a prototype artificial intelligence chatbot developed byOpenAIwhich specializes in dialogue.ChatGptPrtk is a python wrapper around OpenAI ChatGPT for easy access of its functionality.FeaturesProgrammable use as SDKCreate server and run API with customizable payloadNo ModerationAPI Key based authenticationInstallationInstall ChatGptPrtk with pippipinstallchatgptprtkUsagesCreate an account inOpenAIand get API KeyGet AI Responseimportchatgptprtkaschapi_key="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx83r"ch.setApiKey(api_key)msg="write a pallindrome program in java"response=ch.sendMessage(msg)Start a serverch.startServer()API ReferenceEndpoint{Hosturl}/getChatgptResponseRequestParameterTypeDescriptionmsgstringRequired. Your Message to AIResponseParameterTypeDescriptionchatGptRespstringResponse from chatGPTresponseCodeintAPI Response CodeAPI KeySet a valid API Key usingsetApiKey()before starting server.LicenseMIT
Package: chatgpt-search
chatgpt-search package for quick CLI searchchatgpt-search pypi is a simple integration of openAI chatGPT-3.0 models like (text-davinci-003, code-davinci-002, text-curie-001, text-babbage-001, text-ada-001 etc.) with python
to feed your queries answers in terminal.Tips:updatemodels.tomlfor tuning models based on your need and use-cases.InstallationFrom Pypi$ pip install chatgpt-searchSource code$pipenv--python3.9.13
$pipenvshell
$gitclonehttps://github.com/abhijeetkr93/chatgpt-search.gitSetupset env 'CHATGPT_KEY' for api queries. get free api key by signing up herehttps://beta.openai.com/signup$exportCHATGPT_KEY='XXXXXXXXXXXXX'$python-msearch
AskAnything(typeexittoclose):source code:$ python src/search/main.pyStart PlayingText Completion: 
"write short story about colonisation."Coding Queries:
"How do you implement a bucket sort algorithm?"
"How to convert list of dicts in dict of lists in python?"
"Explain 'const groupsData = await this.getMasterGroup(this.masterGroupName, this.userData.company);'Q&A: 
"What is the distance between earth and neptune?"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, contact @abhijeetkr93.
Package: chatgpt-serpucga
DocumentaciónLibrería para comunicación con ChatGPT. Proporciona dos modos de interacción básicos:simple: envía cualquier texto a la API de ChatGPT y devuelve una respuestainteractive: abre un diálogo interactivo en el que el historial se almacena y se
utiliza para proporcionar un contexto a la conversación.
Package: chatgpt-telegram
chatgpt-telegramInstallationon unix like platform, installchatgpt-telegramwith the following command.python3-mpipinstall-Uchatgpt-telegram
playwrightinstallfirefoxon the Windows platform, use the following command to installchatgpt-telegram:python-mpipinstall-Uchatgpt-telegram
playwrightinstallfirefoxconfigurationYou can start this bot with the following command:python3-mchatgpt_telegrambot-config.yamlOn the Windows platform, with the following command:python-mchatgpt_telegrambot-config.yamlwhich bot-config.yaml contains telegram bot configuration and chatgpt accounts as shown below.telegram_api_key:""accounts:-user:""psw:""telegram_api_keysection specify telegram bot api key.accountssection specify chatgpt test accounts.userfield can not be empty, but you can leavepswto empty. If it is left empty, the user will need to manually enter the password upon login. Multiple accounts can be specified in the accounts section to improve ChatGPT responses.If you are running a bot in a server, you need to installXvfbon the server, and useVNCat the client side to connect toXvfb. For more information, refer toRemote_control_over_SSH.After you start this bot, send/startto bot to start the initialization process, which will pop up a browser navigating tohttps://chat.openai.com/chat. On the first time you start the browser, you need to handle the login processes. Some automated processes such as auto-filling of account names and passwords will be carried out, but you will still need to manually solve CAPTCHAs during the login process.Acknowledgementschatgpt-apichatGPT-telegram-botChatGPT
Package: chatgpt-test-generator
Chatgpt Test GeneratorEasy-to-use test generation tool. Powered by ChatGPT.Project Structure:├── main.py
├── poetry.lock
├── pyproject.toml
├── settings.toml
└── testsUsage:Generates automatic test for all functions with#GPT ->syntax.Available syntax rules will increase in next releases.main.pyimport chatgpt_test_generator


# GPT ->
def search(array: list, number: int):
    for idx, element in enumerate(array):
        if element == number:
            return idx

    return -1


if __name__ == "__main__":
    chatgpt_test_generator.generate_tests_on_background()In addition, the settings.toml file should be configured as follows:settings.toml[default]
CHATGPT_API_KEY = "YOUR OPEN-AI API KEY"If main.py is run, it will create tests for functions with GPT -> syntax under /test folder.├── main.py
├── poetry.lock
├── pyproject.toml
├── settings.toml
└── tests
    └── test_main.pytest_main.pyfrom main import search


def test_search():
    array = [1, 2, 3, 4, 5]
    assert search(array, 3) == 2
    assert search(array, 6) == -1It doesn't matter how complex the project folder is.├── comlex_folder1
│ ├── complex_folder2
│ └── complex_folder4
│   └── example.py
│ └── complex_folder_3
├── main.py
├── poetry.lock
├── pyproject.toml
├── settings.toml
└── tests
    └── test_main.pyexample.py# GPT ->
def divide_numbers(number1: int, number2: int):
    return number1 / number2If I run main.py again, the output will be like this.├── comlex_folder1
│   ├── complex_folder2
│   │   └── complex_folder4
│   │       └── example.py
│   └── complex_folder_3
├── main.py
├── poetry.lock
├── pyproject.toml
├── settings.toml
└── tests
    ├── test_example.py
    └── test_main.pytest_example.pyfrom comlex_folder1.complex_folder2.complex_folder4.example import divide_numbers


def test_divide_numbers():
    assert divide_numbers(4, 2) == 2
    assert divide_numbers(9, 3) == 3
    assert divide_numbers(2, 4) == 0.5
    assert divide_numbers(-4, -2) == 2
    assert divide_numbers(-9, 3) == -3
    assert divide_numbers(-2, 4) == -0.5
Package: chatgpt-to-sqlite
chatgpt-to-sqliteImport ChatGPT conversations into a SQLite databaseInstallationInstall this tool usingpip:pip install chatgpt-to-sqliteUsagePre-Requisite:OpenChatGPTand navigate to your account settings.Click "Data Controls and then "Export" under "Export data".For help, run:chatgpt-to-sqlite --helpYou can also use:python -m chatgpt_to_sqlite --helpExport ChatGPT data into a SQLite Data:chatgpt-to-sqlite path/to/chatGPT_conversations.json chatgpt.dbDatabase Schema:CREATE TABLE [conversations] (
    [chat_id] TEXT PRIMARY KEY,
    [title] TEXT,
    [create_time] TEXT,
    [update_time] TEXT
);
CREATE TABLE "messages" (
    [message_id] TEXT PRIMARY KEY,
    [sender] TEXT,
    [create_time] TEXT,
    [status] TEXT,
    [weight] FLOAT,
    [text] TEXT,
    [model] TEXT,
    [chat_id] TEXT REFERENCES [conversations]([chat_id])
);DevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd chatgpt-to-sqlite
python -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: chatgptTTS
п»ї# ChatGPT-TTS Integration Library!
TheChatGPT-TTS Integration Libraryis a powerful open-source tool that seamlessly combines the capabilities of ChatGPT and Text-to-Speech (TTS) technology. With this library, developers can effortlesslycreateconversational AI applications thatnot only understand text inputbut alsogenerate natural-sounding spoken responses. This integration allows for a more immersive and interactive user experience, making it ideal for chatbots, virtual assistants, and other AI-driven applications.Table of ContentsGetting StartedInstallationUsageModelsIntegrate with TelegramParametersGetting StartedInstallationInstall using pypipipinstallchatgpt-ttsorClone the GitHub repository:gitclonehttps://github.com/soyll/chatgpt-tts.gitNavigate to directory:cdchatgpt-tts(Recommended) Create a virtual environment to manage Python packages for your project:python3-mvenvvenvActivate the virtual enviropmentOn windows:.\venv\Scripts\activateOn linux or macOs:source venv/bin/activateInstall the required Python packages fromrequirements.txt:pipinstall-rrequirments.txtUsageimportchatgpt-tts# init ChatGPT-TTSchat_tts=ChatGpt_TTS(speaker,model_id,language,folder_to_save)# use ChatGPT-TTSchat_tts.create_tts(text,prompt,user_id)ModelsAllsupportmodeles check inSilero ModelsIntegrate with Telegram# code by bruhmnmimporttelebotfromchatgptTTSimportchatTOKEN="TELEGRAM_TOKEN"# init ChatGPT-TTSchat_tts=chat.ChatGpt_TTS("en_0","v3_en","en",folder="usr/audio/")bot=telebot.TeleBot(TOKEN)@bot.message_handler(content_types=['text'])defintegrate(message):audio=chat_tts.create_tts(text=message.text,prompt="prompt",id=message.from_user.id)# read wavwithopen(audio[1],'rb')asfd:audio=fd.read()# send voicebot.send_voice(message.from_user.id,audio)bot.polling(none_stop=True)ParametersnamedescriptionexamplespeakerA trained vocal pattern with gender, accent, and toneen_0, en_1model_idUnique id of the language, indicating its versionen_v3, v3_rulangLanguage indicating available model_id and speakerru, enfolderAbsolute path to the folder where Chat-GPT responses will be savedC:/Example/Path/promptA set of words denoting a task or dialog scenarioAct as an editor and correct the mistakes in this texttextText indicating a request to Chat-Gpt to which it will later respondAI help for your creativty, no mstakes, great wrting!idA unique number that, when integrated with social media, can be linked to an individual user for logsself.chat_id, self.user_id, id355367865, 34567653479
Package: chatgpt-tui
ChatGPT TUIcatuiis a Python-based Text User Interface (TUI) for ChatGPT, powered by OpenAI's official API. It provides an easy-to-use interface that allows you to interact with ChatGPT directly from your terminal.UsageInstallpipinstall-Uchatgpt_tuiExport your OpenAI API keyBefore using it, you need to export yourOpenAI API keyas an environment variable:exportOPENAI_API_KEY=your-api-keyLaunchTo start the app, simply run the following command in your terminal:catuiThis will launchcatuiand connect you to the ChatGPT chatbot.Road MapResume from previous chat historyImprove UI/UXBetter visualsMore keybindingsLicensecatuiis licensed under the MIT License.
Package: chatgpt-voice-assistant
ChatGPT Voice AssistantA simple interface to the OpenAI ChatGPT model with speech to text for input and text to speech for the output.
chatgpt-voice-assistant uses Google Translate's text-to-speech free API for audio input and output (not OpenAI Whisper).SetupMac PrerequisitesInstall dependencies:brewinstallportaudio
brewlinkportaudioUpdate your pydistutils config file for portaudio usage by running the following:echo"[build_ext]">>$HOME/.pydistutils.cfgecho"include_dirs="`brew--prefixportaudio`"/include/">>$HOME/.pydistutils.cfgecho"library_dirs="`brew--prefixportaudio`"/lib/">>$HOME/.pydistutils.cfgGeneral SetupOptionally create a new Python environment and activate it:# create a new environment in the current directory called envpython3-mvenvenv# activate the environmentsourceenv/bin/activateFinally, run the following to install all required Python packages and the chatgpt_voice_assistant package in editable mode:pipinstall-e.To install the bash commandchatgpt-assist, runpip install ..Running the ScriptEither set theOPENAI_API_KEYenvironment variable before running the script or pass in your secret key to the script like in the example below:# explicitlypythonchatgpt_voice_assistant/main.py--log-level=INFO--open-ai-key=<OPENAPISECRETKEYHERE>or run with the installed bash command:chatgpt-assist--log-level=INFO--open-ai-key=<OPENAPISECRETKEYHERE>Start speaking and turn up your volume to hear the AI assistant respond.Say the word "exit" or hit Ctrl+C in your terminal to stop the application.OptionsBelow is the help menu from the chatgpt-assist CLI detailing all available options:-h, --help
    show this help message and exit

--log-level LOG_LEVEL
    Whether to print at the debug level or not.

--input-device-name INPUT_DEVICE_NAME
    The input device name.

--lang LANG
    The language to listen for when running speech to text (ex. en or fr).

--max-tokens MAX_TOKENS
    Max OpenAI completion tokens to use for text generation.

--tld TLD
    Top level domain (ex. com or com.au).

--safe-word SAFE_WORD
    Word to speak to exit the application.

--wake-word WAKE_WORD
    (Optional) Word to trigger a response.

--open-ai-key OPEN_AI_KEY
    Required. Open AI Secret Key (or set OPENAI_API_KEY environment variable)

--tts {apple,google}
    Choose a text-to-speech engine ('apple' (say) or 'google' (gtts), defaults to 'google')

--speech-rate SPEECH_RATE
    The rate at which to play speech. 1.0=normalSpecifying an Output Language AccentSpecify both theLANGUAGEandTOP_LEVEL_DOMAINvars to override the default English (United States)pythonchatgpt_voice_assistant/main.py--open-ai-key=<OPENAI_KEY>--lang=en--tld=comLanguage ExamplesEnglish (United States)DEFAULTLANGUAGE=en TOP_LEVEL_DOMAIN=comEnglish (Australia)LANGUAGE=en TOP_LEVEL_DOMAIN=com.auEnglish (India)LANGUAGE=en TOP_LEVEL_DOMAIN=co.inFrench (France)LANGUAGE=fr TOP_LEVEL_DOMAIN=frSee Localized 'accents' section on gTTS docs for more informationReferencesSpeechRecognition library docsGoogle Translate Text-to-Speech API (gTTS)
Package: chatharuhi
ChatHaruhi 2.0中文版本ChatHaruhi: Reviving Anime Character in Reality via Large Language ModelChatHaruhi 2.0由李诚和米唯实创建chengli.thu@gmail.com,mws22@mails.tsinghua.edu.cn米唯实是清华大学电子信息专业二年级研究生，将于明年毕业，正在寻找工作或博士职位。项目主页https://github.com/LC1332/Chat-Haruhi-Suzumiya如果你使用了这个代码，请引用我们的论文：@misc{li2023chatharuhi,title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model}, 

  author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},

  year={2023},

  eprint={2308.09597},

  archivePrefix={arXiv},

  primaryClass={cs.CL}}English versionChatHaruhi: Reviving Anime Character in Reality via Large Language ModelChatHaruhi 2.0, built by Cheng Li and Weishi Michengli.thu@gmail.com,mws22@mails.tsinghua.edu.cnWeishi Mi is a second-year graduate student at Tsinghua University, majoring in computer science.Weishi Mi is pursuing a job or a PhD position, which who will be available next yearhomepagehttps://github.com/LC1332/Chat-Haruhi-SuzumiyaChatHaruhi is a chatbot that can revive anime characters in reality.the 2.0 version was built by Cheng Li and Weishi Mi.Please cite our paper if you use this code for research:@misc{li2023chatharuhi,title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model}, 

  author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},

  year={2023},

  eprint={2308.09597},

  archivePrefix={arXiv},

  primaryClass={cs.CL}}Update log1.0.9Increase support for NaiveDB and Qwen .1.0.8Increase support for OpenAI api base.1.0.7Increase support for Ernie switch and Spark3.0.1.0.6Increase support for Ernie and fix baichuanAPI.1.0.5Increase support for baichuanAPI.1.0.4Increase support for other kinds of embedding models.1.0.3Increase support for baichuan2 and Ernie.1.0.2Increase support for hugging face and GLM2.1.0.1Increase support for GLMPro.1.0.0First released on August 22, 2023.
Package: chatHelper
ChatHelperHigh-level chat client Python API that makes sending messages between computers(using http) easy.InstallationUse the package managerpipto install chatHelper.pipinstallchatHelperQuickStartServerfromchatHelperimportServerserver=Server("192.168.xx.xxx:8000",1)ClientfromchatHelperimportClientclient=Client("http://192.168.xx.xxx:8000/","client1","12345")DocumentationYou can find the documentation for this packagehereContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseApache License 2.0
Package: chatllama
ChatLLaMA📢 Open source implementation for LLaMA-based ChatGPT runnable in a single GPU. 15x faster training process thanChatGPTMeta has recently released LLaMA, a collection of foundational large language models ranging from 7 to 65 billion parameters.
LLaMA is creating a lot of excitement because it is smaller than GPT-3 but has better performance. For example, LLaMA's 13B architecture outperforms GPT-3 despite being 10 times smaller. This new collection of fundamental models opens the door to faster inference performance and chatGPT-like real-time assistants, while being cost-effective and running on a single GPU.However, LLaMA was not fine-tuned for instruction task with a Reinforcement Learning from Human Feedback (RLHF) training process.The good news is that we introduceChatLLaMA, the first open source implementation of LLaMA based on RLHF:A complete open source implementation that enables you to build a ChatGPT-style service based on pre-trained LLaMA models.Compared to the original ChatGPT, the training process and single-GPU inference are much faster and cheaper by taking advantage of the smaller size of LLaMA architectures.ChatLLaMA has built-in support for DeepSpeed ZERO to speedup the fine-tuning process.The library also supports all LLaMA model architectures (7B, 13B, 33B, 65B), so that you can fine-tune the model according to your preferences for training time and inference performance.Image fromOpenAI’s blog.Installationpip install chatllamaGet started with ChatLLaMA:warning: Please note this code represents the algorithmic implementation for RLHF training process of LLaMA and does not contain the model weights. To access the model weights, you need to apply to Meta'sform.ChatLLaMA allows you to easily train LLaMA-based architectures in a similar way to ChatGPT, using RLHF.
For example, below is the code to start the training in the case of ChatLLaMA 7B.fromchatllama.rlhf.trainerimportRLTrainerfromchatllama.rlhf.configimportConfigpath="path_to_config_file.yaml"config=Config(path=path)trainer=RLTrainer(config.trainer)trainer.distillate()trainer.train()trainer.training_stats.plot()Note that you should provide Meta's original weights and your custom dataset before starting the fine-tuning process. Alternatively, you can generate your own dataset using LangChain's agents.pythongenerate_dataset.py