backend

backend/index_vertexai.js

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+import 'dotenv/config'; // Load environment variables from .env file
+import {GoogleAuth} from 'google-auth-library';
+import {GoogleGenAI} from '@google/genai';
+import * as fs from 'fs'; // Import the file system module
+
+const inputImagePath = 'strawberry.jpg';
+const msg1Text1 = {text: `Do you remember a pineapple image? show the pineapple image`};
+
+const outputImagePath = 'strawberry_output.png';
+const project = 'gen-lang-client-0531051429';
+const location = 'global'; // It's best practice to use a specific region
+const model = 'gemini-2.0-flash-preview-image-generation';
+
+// --- Authentication using Service Account Key ---
+const auth = new GoogleAuth({
+  keyFilename: './gen-lang-client-0531051429-7455e402c041.json', // <-- Path to your downloaded key file
+  scopes: ['https://www.googleapis.com/auth/cloud-platform'],
+});
+
+// Initialize Vertex with your Cloud project and location
+const ai = new GoogleGenAI({
+  vertexai: true,
+  project: project,
+  location: location,
+  googleAuth: auth,
+});
+// Define the log file path
+const logFile = 'app.log';
+
+// Set up generation config
+const generationConfig = {
+  maxOutputTokens: 8192,
+  temperature: 1,
+  topP: 0.95,
+  responseModalities: ["TEXT", "IMAGE"],
+  safetySettings: [
+    {
+      category: 'HARM_CATEGORY_HATE_SPEECH',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_DANGEROUS_CONTENT',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_HARASSMENT',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_IMAGE_HATE',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_IMAGE_HARASSMENT',
+      threshold: 'OFF',
+    },
+    {
+      category: 'HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT',
+      threshold: 'OFF',
+    }
+  ],
+};
+
+const imageBuffer = fs.readFileSync(inputImagePath);
+const imageBase64 = imageBuffer.toString('base64');
+
+const msg1Image1 = {
+  inlineData: {
+    mimeType: 'image/jpeg',
+    data: imageBase64
+  }
+};
+
+
+const chat = ai.chats.create({
+  model: model,
+  config: generationConfig
+});
+
+/**
+ * Appends a message to the log file.
+ * @param {string} message The message to log.
+ */
+function writeToLog(message) {
+  fs.appendFile(logFile, message + '\n', (err) => {
+    if (err) {
+      console.error('Error writing to log file:', err);
+    }
+  });
+}
+
+async function sendMessage(message) {
+  const response = await chat.sendMessageStream({
+    message: message
+  });
+  console.log(JSON.stringify(response).substring(0, 1000));
+  process.stdout.write('stream result: ');
+  for await (const chunk of response) {
+    const part = chunk.candidates?.[0]?.content?.parts?.[0];
+
+    console.log(JSON.stringify(chunk).substring(0, 200));
+    if (chunk.text) {
+      process.stdout.write(chunk.text);
+    } else if (part && part.inlineData.data !== undefined && JSON.stringify(part).length > 100000) {
+      // The image data is in base64 format.
+      // Define the file path and name.
+      // Write the base64 data to a file. [2, 4, 10]
+
+      return part.inlineData.data
+    } else {
+      writeToLog(JSON.stringify(chunk));
+    }
+  }
+}
+
+
+export default async function generateContent(inputImageBase64, textDescription) {
+  return await sendMessage([
+    {
+      inlineData: {
+        mimeType: 'image/jpeg',
+        data: inputImageBase64.split(',', 2)[1]
+      }
+    },
+    {
+      text: textDescription
+    }
+  ]);
+}
+
+/*
+export default async function generateContent() {
+  await sendMessage([
+    msg1Image1,
+    msg1Text1
+  ]);
+}
+
+generateContent()
+*/