O estudante estava muito animado para a grande feira de ciências amanhã, mas tinha esquecido de fazer o projeto. Eles estavam tão ocupados com o treino de futebol e outras atividades extracurriculares que perderam completamente a noção do tempo. Eles sabiam que teriam que ficar acordados até tarde para terminar o projeto, mas estavam determinados a fazê-lo. Eles trabalharam por horas e, por fim, pouco antes da meia-noite, terminaram. Eles estavam tão cansados, mas também estavam aliviados. Eles sabiam que tinham feito o melhor que podem e estavam confiantes de que se sairiam bem na feira de ciências.
Transforme este parágrafo em uma lista com marcadores.
Exemplos de respostas:
Esta é a lista do parágrafo com marcadores:
O estudante estava animado para a grande feira de ciências amanhã.
Eles tinham esquecido de fazer o projeto.
Eles estavam ocupados com o treino de futebol e outras atividades extracurriculares.
Eles sabiam que teriam que ficar acordados até tarde para terminar o projeto.
Eles trabalharam por horas e terminaram pouco antes da meia-noite.
Eles estavam cansados, mas aliviados.
Eles sabiam que tinham feito o melhor que tinham.
Eles estavam confiantes de que se sairiam bem na feira de ciências.
Chame esse comando da nossa API Gemini integrando o código a seguir ao projeto.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Não contém as informações de que eu preciso","missingTheInformationINeed","thumb-down"],["Muito complicado / etapas demais","tooComplicatedTooManySteps","thumb-down"],["Desatualizado","outOfDate","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Problema com as amostras / o código","samplesCodeIssue","thumb-down"],["Outro","otherDown","thumb-down"]],[],[],[],null,["# Step by step instructions from paragraph\n\n[Prompt gallery](/prompt-gallery)\n\nStep by step instructions from paragraph\n========================================\n\nOrganize the main ideas in a paragraph as bullet points \nPrompt type: [subject\nText](/prompt-gallery/?type=text) \nUse case: \nSummarize Extract \nRelated: \n[Generate questions from fiction](/prompts/questions-from-fiction) [Science fiction writer](/prompts/scifi-novel-writer) [Product descriptions](/prompts/product-descriptions)\n[Open in Google AI Studio](https://makersuite.google.com/app/prompts/step-by-step-instructions) \n\n#### Prompt text\n\nThe student was so excited for the big science fair tomorrow, but they had forgotten to do their project. They had been so busy with soccer practice and their other extracurricular activities that they had completely lost track of time. They knew that they would have to stay up late to finish their project, but they were determined to do it. They worked for hours, and finally, just before midnight, they were finished. They were so tired, but they were also relieved. They knew that they had done their best, and they were confident that they would do well in the science fair.\n\nPlease turn this paragraph into a bulleted list. \n\n#### Sample responses\n\nHere is the bulleted list of the paragraph:\n\n- The student was excited for the big science fair tomorrow.\n- They had forgotten to do their project.\n- They had been busy with soccer practice and other extracurricular activities.\n- They knew that they would have to stay up late to finish their project.\n- They worked for hours and finished just before midnight.\n- They were tired but relieved.\n- They knew that they had done their best.\n- They were confident that they would do well in the science fair. \n\nGet code\n--------\n\n[Enable API key](https://makersuite.google.com/app/apikey) to develop with the Gemini API\n\nYou can call this prompt from our Gemini API by integrating the following code into your project. \nPython \nJavascript \n1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 \n\"\"\" At the command line, only need to run once to install the package via pip: $ pip install google-generativeai \"\"\" import google.generativeai as genai genai.configure(api_key=\"YOUR API KEY\") defaults = { 'model': 'models/text-bison-001', 'temperature': 0.7, 'candidate_count': 1, 'top_k': 40, 'top_p': 0.95, 'max_output_tokens': 1024, } prompt = \"\"\"The student was so excited for the big science fair tomorrow, but they had forgotten to do their project. They had been so busy with soccer practice and their other extracurricular activities that they had completely lost track of time. They knew that they would have to stay up late to finish their project, but they were determined to do it. They worked for hours, and finally, just before midnight, they were finished. They were so tired, but they were also relieved. They knew that they had done their best, and they were confident that they would do well in the science fair. Please turn this paragraph into a bulleted list.\"\"\" response = genai.generate_text( \\*\\*defaults, prompt=prompt ) print(response.result) \n1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 \nconst { TextServiceClient } = require(\"@google-ai/generativelanguage\"); const { GoogleAuth } = require(\"google-auth-library\"); const MODEL_NAME = \"models/text-bison-001\"; const API_KEY = \"YOUR API KEY\"; const client = new TextServiceClient({ authClient: new GoogleAuth().fromAPIKey(API_KEY), }); const promptString = \"The student was so excited for the big science fair tomorrow, but they had forgotten to do their project. They had been so busy with soccer practice and their other extracurricular activities that they had completely lost track of time. They knew that they would have to stay up late to finish their project, but they were determined to do it. They worked for hours, and finally, just before midnight, they were finished. They were so tired, but they were also relieved. They knew that they had done their best, and they were confident that they would do well in the science fair.nnPlease turn this paragraph into a bulleted list.\"; client.generateText({ // required, which model to use to generate the result model: MODEL_NAME, // optional, 0.0 always uses the highest-probability result temperature: 0.7, // optional, how many candidate results to generate candidateCount: 1, // optional, number of most probable tokens to consider for generation top_k: 40, // optional, for nucleus sampling decoding strategy top_p: 0.95, // optional, maximum number of output tokens to generate max_output_tokens: 1024, prompt: { text: promptString, }, }).then(result =\\\u003e { console.log(JSON.stringify(result, null, 2)); }); \ncontent_copy done Copy to clipboard"]]
