OpenAIとNode.jsを使ってスライド作成を自動化する

git clone https://github.com/griddbnet/Blogs.git --branch slides

cd apps
npm install 

OPENAI_API_KEY=sk-proj-secret
VITE_APP_URL=http://localhost:3000

npm run start:build

# installs nvm (Node Version Manager)
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash

# download and install Node.js
nvm install 16

# verifies the right Node.js version is in the environment
node -v # should print `v16.20.2`

# verifies the right NPM version is in the environment
npm -v # should print `8.19.4``

sudo systemctl status gridstore

sudo systemctl start gridstore

You are a data scientist assistant. When given data and a query, write the proper code and create the proper visualization.

const dataScienceAssistantId = "asst_FOqRnMVXw0WShTSGw70NZJAX"

[
 {
  "Year": 2020,
  "Quarter": "Q1",
  "Distribution channel": "Online Sales",
  "Revenue ($M)": 2.10,
  "Costs ($M)": 1.905643,
  "Customer count": 190,
  "Time": "2020 Q1",
  "Product Category": "Engine Parts",
  "Region": "North America",
  "Units Sold": 900,
  "Average Sale Price ($)": 2333,
  "Discounts Given ($)": 14000,
  "Returns ($)": 4500,
  "Customer Satisfaction Rating": 8.2,
  "Salesperson": "SP120",
  "Marketing Spend ($)": 18000
 },
 {
  "Year": 2020,
  "Quarter": "Q1",
  "Distribution channel": "Direct Sales",
  "Revenue ($M)": 2.15,
  "Costs ($M)": 2.004112,
  "Customer count": 200,
  "Time": "2020 Q1",
  "Product Category": "Brakes",
  "Region": "Europe",
  "Units Sold": 1000,
  "Average Sale Price ($)": 2150,
  "Discounts Given ($)": 12000,
  "Returns ($)": 5000,
  "Customer Satisfaction Rating": 8.0,
  "Salesperson": "SP121",
  "Marketing Spend ($)": 19000
 },
 ...
 {
  "Year": 2024,
  "Quarter": "Q2",
  "Distribution channel": "Direct Sales",
  "Revenue ($M)": 3.15,
  "Costs ($M)": 2.525112,
  "Customer count": 390,
  "Time": "2024 Q2",
  "Product Category": "Brakes",
  "Region": "Europe",
  "Units Sold": 1500,
  "Average Sale Price ($)": 2095,
  "Discounts Given ($)": 22000,
  "Returns ($)": 17000,
  "Customer Satisfaction Rating": 9.1,
  "Salesperson": "SP144",
  "Marketing Spend ($)": 38000
 }
]

const analyzeDataPrompt = "Calculate the profit (revenue minus cost) by quarter and year, and visualize as a line plot across the distribution channels, where the colors of the lines are green, light red, and light blue"

 const thread = await openai.beta.threads.create({
  messages: [
   {
    "role": "user",
    "content": analyzeDataPrompt,
    "attachments": [
     {
      file_id: fileId,
      tools: [{ type: "code_interpreter" }]
     }
    ]
   }
  ]
 });

const insightPrompt = `Give me two medium-length sentences (~20-30 words per sentence) of the most important insights from the plot you just created, and save each sentence as an item in one array. Give me a raw array, no formatting, no commentary. These will be used for a slide deck, and they should be about the 'so what' behind the data.`

const titlePrompt = "Given the plot and bullet point you created, come up with a very brief title only for a slide. It should reflect just the main insights you came up with."

//...
if (bulletPointsSummary.status === "completed") {
                    const message = await openai.beta.threads.messages.list(thread.id)
                    const dataVisTitle = message.data[0].content[0].text.value

                    presentationOptions = {
                        title: slideTitle,
                        subtitle: slideSubtitle,
                        dataVisTitle: dataVisTitle,
                        chartImagePath: path.join(__dirname, "public", `${filename}`),
                        keyInsights: "Key Insights:",
                        bulletPoints: bulletPoints,
                        outputFilename: path.join(__dirname, 'public', pptxFilename)
                    };

                    try {
                        createPresentation(presentationOptions)
                    } catch (error) {
                        console.log(error)
                    }
}
//...

app.get('/create/:fileId', async (req, res) => {
 const fileId = req.params.fileId

 try {
  const result = await aiAssistant(fileId)

  if (result.status === "completed") {
   const {
    title: titlePptx,
    subtitle: subtitlePptx,
    dataVisTitle: dataVisTitlePptx,
    chartImage: chartImagePptx,
    bulletPoints: bulletPointsPptx,
    outputFilename: pptxFile
   } = result.data

   const saveDataStatus = await saveData({ titlePptx, subtitlePptx, dataVisTitlePptx, chartImagePptx, bulletPointsPptx, pptxFile })

   res.json({
    save: saveDataStatus,
    data: result.data,
    pptx: result.pptx
   })
  } else {
   res.status(500).json({
    error: 'Task not completed', status: result.status
   })
  }

 } catch (error) {
  console.error('Error in AI Assistant:', error)
  res.status(500).json({ error: 'Error in AI Assistant', details: error.message })
 }
})