Code gpt

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

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.

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

Types of queries were tested in GPT-3.5 and GPT-4. I would like to note that the results in GPT-4 were not necessarily more or less insightful than GPT-3.5, what matters is the technical accuracy and not the depth or composition of the response. With that in mind, let’s look at some of the queries I tested in ChatGPT.Research SummarizationOne task where I think ChatGPT is very useful is as a research summarizer. For example, I sometimes need to get some overview of industry standards as part of a PCB design project, video, or article. For example, I like to use ChatGPT to determine:Lists of industry standards in certain areas (EMC, military, automotive, etc.)An overview of the contents in certain industry standardsAn overview of interface standards and how they are usedCertain calculations that might be needed as part of engineering an interconnect or PCBGenerating formulas that would be needed to calculate somethingCode GenerationAnother example comes from embedded design and testing. In the example below, I’m generating a python class for my older LeCroy 9300 oscilloscope so that I can capture data from the device. This was generated using GPT-4; GPT-3.5 was also successful, but it used the pyvisa class as the basis for the generated code. While I have not tested the code below, it does generate code with the correct Python syntax. Make sure you QC any generated code before using it in your system.Learn more about ChatGPT for test code generation High-Level How-To TasksThis is an area where ChatGPT gives mixed results. In the first set of queries I ran, I found that overgeneralized questions will yield overgeneralized answers. The generated results could be useful for new designers who want to know what they still need to learn, but they are not actionable for an experienced designer and they overgeneralize information across multiple queries.For example, I queried ChatGPT for guidance on how to design three types of boards:High-speed PCBsRF PCBsHigh-density PCBsThe results that were generated were practically identical across all three types of boards. The system simply replaced “high-speed PCB” with “RF PCB” and “high-density PCB” when generating the