ChatGPT AI App in Flutter – ChatGPT is a chat-bot launched by OpenAI in November 2022. It is built on top of OpenAI’s GPT-3.5 family of large language models, and is fine-tuned with both supervised and reinforcement learning techniques.
Unofficial
“community-maintained” library.
Features
- Install Package
- Create OpenAI Instance
- Change Access Token
- Complete Text
- Support Server Sent Event
- Chat Complete GPT-4
- Support GPT3.5 and GPT-4
- Support Server Sent Event
- Support Function Calling
- Error Handle
- Example Q&A
- Generate Image With Prompt
- Editing
- Cancel Generate
- File
- Audio
- Embedding
- Fine-Tune
- Support Server Sent Event
- Moderations
- Model And Engine
- Translate Example
- Video Tutorial
- Docs
Install Package
chat_gpt_sdk: 2.2.4
Create OpenAI Instance
- Parameter
- Token
- Your secret API keys are listed below. Please note that we do not display your secret API keys again after you generate them.
- Do not share your API key with others, or expose it in the browser or other client-side code. In order to protect the security of your account, OpenAI may also automatically rotate any API key that we’ve found has leaked publicly.
- https://beta.openai.com/account/api-keys
- Token
- OrgId
- Identifier for this organization sometimes used in API requests
- https://beta.openai.com/account/org-settings
final openAI = OpenAI.instance.build(token: token,baseOption: HttpSetup(receiveTimeout: const Duration(seconds: 5)),enableLog: true);
Change Access Token
openAI.setToken('new-access-token'); ///get toekn openAI.token;
Complete Text
- Text Complete API
- Translate Method
- translateEngToThai
- translateThaiToEng
- translateToJapanese
- Model
- kTranslateModelV3
- kTranslateModelV2
- kCodeTranslateModelV2
- Translate natural language to SQL queries.
- Create code to call the Stripe API using natural language.
- Find the time complexity of a function.
- https://beta.openai.com/examples
- Translate Method
- Complete with Feature
void _translateEngToThai() async{ final request = CompleteText( prompt: translateEngToThai(word: _txtWord.text.toString()), maxToken: 200, model: TextDavinci3Model()); final response = await openAI.onCompletion(request: request); ///cancel request openAI.cancelAIGenerate(); print(response); }
- Complete with FutureBuilder
Future<CTResponse?>? _translateFuture; _translateFuture = openAI.onCompletion(request: request); ///ui code FutureBuilder<CTResponse?>( future: _translateFuture, builder: (context, snapshot) { final data = snapshot.data; if(snapshot.connectionState == ConnectionState.done) return something if(snapshot.connectionState == ConnectionState.waiting) return something return something })
- GPT-3 with SSE
void completeWithSSE() { final request = CompleteText( prompt: "Hello world", maxTokens: 200, model: TextDavinci3Model()); openAI.onCompletionSSE(request: request).listen((it) { debugPrint(it.choices.last.text); }); }
Chat Complete (GPT-4 and GPT-3.5)
- GPT-4
void chatComplete() async { final request = ChatCompleteText(messages: [ Map.of({"role": "user", "content": 'Hello!'}) ], maxToken: 200, model: Gpt4ChatModel()); final response = await openAI.onChatCompletion(request: request); for (var element in response!.choices) { print("data -> ${element.message?.content}"); } }
- GPT-4 with SSE(Server Send Event)
void chatCompleteWithSSE() { final request = ChatCompleteText(messages: [ Map.of({"role": "user", "content": 'Hello!'}) ], maxToken: 200, model: Gpt4ChatModel()); openAI.onChatCompletionSSE(request: request).listen((it) { debugPrint(it.choices.last.message?.content); }); }
- Support SSE(Server Send Event)
- GPT-3.5 Turbo
void chatCompleteWithSSE() { final request = ChatCompleteText(messages: [ Map.of({"role": "user", "content": 'Hello!'}) ], maxToken: 200, model: GptTurboChatModel()); openAI.onChatCompletionSSE(request: request).listen((it) { debugPrint(it.choices.last.message?.content); }); }
- Chat Complete
void chatComplete() async { final request = ChatCompleteText(messages: [ Map.of({"role": "user", "content": 'Hello!'}) ], maxToken: 200, model: GptTurbo0301ChatModel()); final response = await openAI.onChatCompletion(request: request); for (var element in response!.choices) { print("data -> ${element.message?.content}"); } }
- Chat Complete Function Calling
/// work only with gpt-turbo-0613,gpt-4-0613 void gptFunctionCalling() async { final request = ChatCompleteText( messages: [ Messages( role: Role.user, content: "What is the weather like in Boston?",name: "get_current_weather"), ], maxToken: 200, model: GptTurbo0631Model(), functions: [ FunctionData( name: "get_current_weather", description: "Get the current weather in a given location", parameters: { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] }) ], functionCall: FunctionCall.auto); ChatCTResponse? response = await openAI.onChatCompletion(request: request); }
Error Handle
///using catchError openAI.onCompletion(request: request) .catchError((err){ if(err is OpenAIAuthError){ print('OpenAIAuthError error ${err.data?.error?.toMap()}'); } if(err is OpenAIRateLimitError){ print('OpenAIRateLimitError error ${err.data?.error?.toMap()}'); } if(err is OpenAIServerError){ print('OpenAIServerError error ${err.data?.error?.toMap()}'); } }); ///using try catch try { await openAI.onCompletion(request: request); } on OpenAIRateLimitError catch (err) { print('catch error ->${err.data?.error?.toMap()}'); } ///with stream openAI .onCompletionSSE(request: request) .transform(StreamTransformer.fromHandlers( handleError: (error, stackTrace, sink) { if (error is OpenAIRateLimitError) { print('OpenAIRateLimitError error ->${error.data?.message}'); }})) .listen((event) { print("success"); });
Q&A
- Example Q&A
- Answer questions based on existing knowledge.
final request = CompleteText(prompt:'What is human life expectancy in the United States?'), model: TextDavinci3Model(), maxTokens: 200); final response = await openAI.onCompletion(request:request);
- Request
Q: What is human life expectancy in the United States?
- Response
A: Human life expectancy in the United States is 78 years.
Generate Image With Prompt
- Generate Image
- prompt
- A text description of the desired image(s). The maximum length is 1000 characters.
- n
- The number of images to generate. Must be between 1 and 10.
- size
- The size of the generated images. Must be one of 256×256, 512×512, or 1024×1024.
- response_format
- The format in which the generated images are returned. Must be one of url or b64_json.
- user
- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
- prompt
- Generate with feature
void _generateImage() { const prompt = "cat eating snake blue red."; final request = GenerateImage(prompt, 1,size: ImageSize.size256, responseFormat: Format.url); final response = openAI.generateImage(request); print("img url :${response.data?.last?.url}"); }
Edit
- Edit Prompt
void editPrompt() async { final response = await openAI.editor.prompt(EditRequest( model: CodeEditModel(), input: 'What day of the wek is it?', instruction: 'Fix the spelling mistakes')); print(response.choices.last.text); }
- Edit Image
void editImage() async { final response = await openAI.editor.editImage(EditImageRequest( image: EditFile("${image?.path}", '${image?.name}'), mask: EditFile('file path', 'file name'), size: ImageSize.size1024, prompt: 'King Snake')); print(response.data?.last?.url); }
- Variations
void variation() async { final request = Variation(image: EditFile('${image?.path}', '${image?.name}')); final response = await openAI.editor.variation(request); print(response.data?.last?.url); }
Cancel Generate
- Stop Generate Prompt
_openAI .onChatCompletionSSE(request: request, onCancel: onCancel); ///CancelData CancelData? mCancel; void onCancel(CancelData cancelData) { mCancel = cancelData; } mCancel?.cancelToken.cancel("canceled ");
- Stop Edit
- image
- prompt
openAI.edit.editImage(request,onCancel: onCancel); ///CancelData CancelData? mCancel; void onCancel(CancelData cancelData) { mCancel = cancelData; } mCancel?.cancelToken.cancel("canceled edit image");
- Stop Embedding
openAI.embed.embedding(request,onCancel: onCancel); ///CancelData CancelData? mCancel; void onCancel(CancelData cancelData) { mCancel = cancelData; } mCancel?.cancelToken.cancel("canceled embedding");
- Stop Audio
- translate
- transcript
openAI.audio.transcribes(request,onCancel: onCancel); ///CancelData CancelData? mCancel; void onCancel(CancelData cancelData) { mCancel = cancelData; } mCancel?.cancelToken.cancel("canceled audio transcribes");
- Stop File
- upload file
- get file
- delete file
openAI.file.uploadFile(request,onCancel: onCancel); ///CancelData CancelData? mCancel; void onCancel(CancelData cancelData) { mCancel = cancelData; } mCancel?.cancelToken.cancel("canceled uploadFile");
File
- Get File
void getFile() async { final response = await openAI.file.get(); print(response.data); }
- Upload File
void uploadFile() async { final request = UploadFile(file: EditFile('file-path', 'file-name'),purpose: 'fine-tune'); final response = await openAI.file.uploadFile(request); print(response); }
- Delete File
void delete() async { final response = await openAI.file.delete("file-Id"); print(response); }
- Retrieve File
void retrieve() async { final response = await openAI.file.retrieve("file-Id"); print(response); }
- Retrieve Content File
void retrieveContent() async { final response = await openAI.file.retrieveContent("file-Id"); print(response); }
Audio
- Audio Translate
void audioTranslate() async { final mAudio = File('mp3-path'); final request = AudioRequest(file: EditFile(mAudio.path, 'name'), prompt: '...'); final response = await openAI.audio.translate(request); }
- Audio Transcribe
void audioTranscribe() async { final mAudio = File('mp3-path'); final request = AudioRequest(file: EditFile(mAudio.path, 'name'), prompt: '...'); final response = await openAI.audio.transcribes(request); }
Embedding
- Embedding
void embedding() async { final request = EmbedRequest( model: TextSearchAdaDoc001EmbedModel(), input: 'The food was delicious and the waiter'); final response = await openAI.embed.embedding(request); print(response.data.last.embedding); }
Fine Tune
- Create Fine Tune
void createTineTune() async { final request = CreateFineTune(trainingFile: 'The ID of an uploaded file'); final response = await openAI.fineTune.create(request); }
- Fine Tune List
void tineTuneList() async { final response = await openAI.fineTune.list(); }
- Fine Tune List Stream (SSE)
void tineTuneListStream() { openAI.fineTune.listStream('fineTuneId').listen((it) { ///handled data }); }
- Fine Tune Get by Id
void tineTuneById() async { final response = await openAI.fineTune.retrieve('fineTuneId'); }
- Cancel Fine Tune
void tineTuneCancel() async { final response = await openAI.fineTune.cancel('fineTuneId'); }
- Delete Fine Tune
void deleteTineTune() async { final response = await openAI.fineTune.delete('model'); }
Moderations
- Create Moderation
void createModeration() async { final response = await openAI.moderation .create(input: 'input', model: TextLastModerationModel()); }
Model&Engine
- Model List
- List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.
- https://beta.openai.com/docs/api-reference/models
final models = await openAI.listModel();
- Engine List
- Lists the currently available (non-finetuned) models, and provides basic information about each one such as the owner and availability.
- https://beta.openai.com/docs/api-reference/engines
final engines = await openAI.listEngine();
Translate App
ChatGPT Demo App
Learn more about chatgpt project. click here
Credit
This project is developed by redevrx
Download this project from the below link.
https://github.com/redevrx/chat_gpt_sdk/archive/refs/heads/main.zip
You can visit original source page from the below link:
https://github.com/redevrx/chat_gpt_sdk
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