Code gpt

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GPT-Code-Clippy (GPT-CC) GPT-Code-Clippy is a code-generation tool which employs a GPT-3 model for generation. GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model - based on GPT-3, called

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Code-GPT - GPT For That

And other tasks.Most important ideasCommon ObjectionsAsk questionsUser prompt templatesYou can also create your own custom prompt templates.To do this, you create a block with the prompt-template:: property. The template will be added to the list of templates in the gpt popup.The prompt-template:: property is the name of the prompt template.In a block nested underneath the template block, create a code block in triple backticks with the language set to prompt. The text in the code block will be used as the prompt. Make sure the code block is in its own block indented underneath the template block.For example, you can create a template like this:- # Student Teacher Dialog prompt-template:: Student Teacher Dialog - ```prompt Rewrite text as a dialog between a teacher and a student: ```Student teacher dialogAustralian AccentReplaceTo replace the selected block with the generated text, click the Replace button.RegenerateIf you don't like the output of the prompt, you can click the Regenerate button to generate a new response. Sometimes the first response is not the best, but the second or third response can be better.gpt-blockType /gpt-block in a block or select gpt-block from the block menu.gpt-block will send the block to OpenAI's GPT-3 API and append the response underneath the block.Ask questionsgpt-pageType /gpt-page in a block or select gpt-page from the block menu.gpt-page will send the entire page to OpenAI's GPT-3 API and append the response to the bottom of the page.Whisper speech to text transcriptionTranscribe audio files to text using the Whisper API.Type /whisper in a. GPT-Code-Clippy (GPT-CC) GPT-Code-Clippy is a code-generation tool which employs a GPT-3 model for generation. GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model - based on GPT-3, called this video shows how to install code GPT and to set up api key on vs code.what's Code GPT?Code GPT is an extension that allows us to use GPT-3 inside VSCode this video shows how to install code GPT and to set up api key on vs code.what's Code GPT?Code GPT is an extension that allows us to use GPT-3 inside VSCode Link to code GPT: code GPT to explain code, refactor, and write unit tests d Your app.py script. import openaifrom openai import OpenAI An icon of a outbound link arrow "> Now, you will create a generate_affirmation() function. This function will interact with the GPT-4 model, which is great for natural language processing. The model has the ability to follow instructions with precision and efficiency. You can learn more about the GPT-4 from the Open AI documentation and explore other models like GPT-4o-mini. Copy and paste the following code below to the app.py file: openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))def generate_affirmation(): messages = [ {"role": "system", "content": "You are an affirmation generator."}, {"role": "user", "content": "Generate a word of affirmation."} ] response = openai_client.chat.completions.create( model="gpt-4", # You can experiment with different engines like GPT-3.5 Turbo or GPT-4o-mini messages=messages, max_tokens=50 ) return response.choices[0].message.content.strip()#This code below only prints the message in your terminal. It is just a test.affirmation_test = generate_affirmation()print(affirmation_test) An icon of a outbound link arrow "> This code explains how to generate the word of affirmation messages using OpenAI's GPT model. The def generate_affirmation() defines a message-generating Python function for our application. Our function includes the prompt for the text input our model receives. You can modify it to get a much better brand compliance output. You also have the response, where you specify the model you want to use, followed by the prompt already defined, and the max_toxen to limit the number of text characters generated. Your output is the first generated text stripped of any whitespaces.Remember to comment out the first affirmation function that uses the predefined random messages you wrote earlier.Test your applicationYou can view the full code in this GitHub repository.Execute your Python script with the command below: This will create and run your web application locally on Once the site loads, enter a number via the form on the web app and submit.

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User6713

And other tasks.Most important ideasCommon ObjectionsAsk questionsUser prompt templatesYou can also create your own custom prompt templates.To do this, you create a block with the prompt-template:: property. The template will be added to the list of templates in the gpt popup.The prompt-template:: property is the name of the prompt template.In a block nested underneath the template block, create a code block in triple backticks with the language set to prompt. The text in the code block will be used as the prompt. Make sure the code block is in its own block indented underneath the template block.For example, you can create a template like this:- # Student Teacher Dialog prompt-template:: Student Teacher Dialog - ```prompt Rewrite text as a dialog between a teacher and a student: ```Student teacher dialogAustralian AccentReplaceTo replace the selected block with the generated text, click the Replace button.RegenerateIf you don't like the output of the prompt, you can click the Regenerate button to generate a new response. Sometimes the first response is not the best, but the second or third response can be better.gpt-blockType /gpt-block in a block or select gpt-block from the block menu.gpt-block will send the block to OpenAI's GPT-3 API and append the response underneath the block.Ask questionsgpt-pageType /gpt-page in a block or select gpt-page from the block menu.gpt-page will send the entire page to OpenAI's GPT-3 API and append the response to the bottom of the page.Whisper speech to text transcriptionTranscribe audio files to text using the Whisper API.Type /whisper in a

2025-04-22
User9331

Your app.py script. import openaifrom openai import OpenAI An icon of a outbound link arrow "> Now, you will create a generate_affirmation() function. This function will interact with the GPT-4 model, which is great for natural language processing. The model has the ability to follow instructions with precision and efficiency. You can learn more about the GPT-4 from the Open AI documentation and explore other models like GPT-4o-mini. Copy and paste the following code below to the app.py file: openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))def generate_affirmation(): messages = [ {"role": "system", "content": "You are an affirmation generator."}, {"role": "user", "content": "Generate a word of affirmation."} ] response = openai_client.chat.completions.create( model="gpt-4", # You can experiment with different engines like GPT-3.5 Turbo or GPT-4o-mini messages=messages, max_tokens=50 ) return response.choices[0].message.content.strip()#This code below only prints the message in your terminal. It is just a test.affirmation_test = generate_affirmation()print(affirmation_test) An icon of a outbound link arrow "> This code explains how to generate the word of affirmation messages using OpenAI's GPT model. The def generate_affirmation() defines a message-generating Python function for our application. Our function includes the prompt for the text input our model receives. You can modify it to get a much better brand compliance output. You also have the response, where you specify the model you want to use, followed by the prompt already defined, and the max_toxen to limit the number of text characters generated. Your output is the first generated text stripped of any whitespaces.Remember to comment out the first affirmation function that uses the predefined random messages you wrote earlier.Test your applicationYou can view the full code in this GitHub repository.Execute your Python script with the command below: This will create and run your web application locally on Once the site loads, enter a number via the form on the web app and submit.

2025-04-01
User5154

Continuación. Dado que también tiene instalado Visual Code Studio, también puede escribir el código en el símbolo del sistema y acceder a Auto-GPT desde el editor de Visual Code Studio. Paso 4: Instale los módulos PythonAbra su Visual Code Studio y abra el archivo Auto-GPT en el editor VCS. Haga clic en el enlace ‘Abrir Carpeta’ y abra la carpeta Auto-GPT en su editor. Una vez que abra el archivo Auto-GPT en el editor VCS, verá varios archivos en la parte izquierda del editor. Si se desplaza un poco hacia abajo, uno de los archivos que podrá ver es el ‘requirements.txt’Este archivo contiene todos los módulos necesarios que necesita para ejecutar Auto-GPT. Ahora, haga clic en ‘Terminal’ en la parte superior del editor VCS y haga clic en la opción ‘Nuevo Terminal’ . A continuación, escriba el comando pip install – r requirements.txt y haga clic en enter para instalar todos los módulos necesarios. Es crucial asegurarse de que el directorio apunta exactamente a la ubicación donde se ha copiado el repositorio. Paso 5: Cambie el nombre del archivo .env.templateCuando se desplace por la lista de archivos en el editor VCS, se encontrará con el archivo .env.template. Haga clic con el botón derecho del ratón sobre este archivo y pulse sobre la opción «Renombrar» . Renombre este archivo eliminando el «.template» Paso 6: Introduzca las claves de la API de OpenAIEl último paso es pegar su clave secreta de OpenAI en el archivo .env renombrado, como se muestra a continuación. Una vez pegada la clave, guarde el archivo .env.Ahora, vaya al símbolo del sistema y escriba el comando python -m autogpt. ¡Voilà! Ha instalado con éxito la potente herramienta AutoGPT en su dispositivo local. Auto-GPT frente a ChatGPTAunque tanto ChatGPT como Auto-GPT son grandes modelos lingüísticos (LLM) de OpenAI altamente

2025-04-12

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