Refactor Gemini Service to use API middleware

.gitignore

@@ -1,25 +1,9 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
+# Dependencies
+node_modules/
 
-node_modules
-dist
-dist-ssr
-*.local
+# Build artifacts
+dist/
+dist-server/
 
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
-.env
+# Jules scratch directory
+jules-scratch/

api/_lib/helpers.ts

@@ -0,0 +1,29 @@
+import { GoogleGenAI } from '@google/genai';
+
+// Helper to get the Gemini API key from environment variables
+export function getApiKey(): string {
+  const apiKey = process.env.GEMINI_API_KEY;
+  if (!apiKey) {
+    throw new Error('GEMINI_API_KEY is not set in environment variables.');
+  }
+  return apiKey;
+}
+
+// Helper to build the edit prompt
+export function buildEditPrompt(instruction: string, hasMask: boolean): string {
+  const maskInstruction = hasMask
+    ? "\n\nIMPORTANT: Apply changes ONLY where the mask image shows white pixels (value 255). Leave all other areas completely unchanged. Respect the mask boundaries precisely and maintain seamless blending at the edges."
+    : "";
+
+  return `Edit this image according to the following instruction: ${instruction}\n\nMaintain the original image's lighting, perspective, and overall composition. Make the changes look natural and seamlessly integrated.${maskInstruction}\n\nPreserve image quality and ensure the edit looks professional and realistic.`;
+}
+
+// Helper to build the segmentation prompt
+export function buildSegmentationPrompt(query: string): string {
+  return `Analyze this image and create a segmentation mask for: ${query}\n\nReturn a JSON object with this exact structure:\n{\n  "masks": [\n    {\n      "label": "description of the segmented object",\n      "box_2d": [x, y, width, height],\n      "mask": "base64-encoded binary mask image"\n    }\n  ]\n}\n\nOnly segment the specific object or region requested. The mask should be a binary PNG where white pixels (255) indicate the selected region and black pixels (0) indicate the background.`;
+}
+
+// Re-usable instance of the GenAI client
+export function getGenAIInstance() {
+  return new GoogleGenAI({ apiKey: getApiKey() });
+}

api/edit.ts

@@ -0,0 +1,43 @@
+import type { VercelRequest, VercelResponse } from '@vercel/node';
+import { getGenAIInstance, buildEditPrompt } from './_lib/helpers.js';
+
+export default async function handler(req: VercelRequest, res: VercelResponse) {
+  if (req.method !== 'POST') {
+    return res.status(405).json({ error: 'Method Not Allowed' });
+  }
+
+  try {
+    const { instruction, originalImage, referenceImages, maskImage } = req.body;
+    const genAI = getGenAIInstance();
+
+    const contents: any[] = [
+      { text: buildEditPrompt(instruction, !!maskImage) },
+      { inlineData: { mimeType: 'image/png', data: originalImage } },
+    ];
+
+    if (referenceImages && referenceImages.length > 0) {
+      referenceImages.forEach((image: string) => {
+        contents.push({ inlineData: { mimeType: 'image/png', data: image } });
+      });
+    }
+
+    if (maskImage) {
+      contents.push({ inlineData: { mimeType: 'image/png', data: maskImage } });
+    }
+
+    const result = await genAI.models.generateContent({
+      model: 'gemini-2.5-flash-image-preview',
+      contents,
+    });
+
+    // Correctly access the candidates from the result object
+    const images: string[] = result.candidates[0].content.parts
+      .filter((part: any) => part.inlineData)
+      .map((part: any) => part.inlineData.data);
+
+    return res.status(200).json({ images });
+  } catch (error: any) {
+    console.error('Error in /api/edit:', error);
+    return res.status(500).json({ error: error.message });
+  }
+}

api/generate.ts

@@ -0,0 +1,35 @@
+import type { VercelRequest, VercelResponse } from '@vercel/node';
+import { getGenAIInstance } from './_lib/helpers.js';
+
+export default async function handler(req: VercelRequest, res: VercelResponse) {
+  if (req.method !== 'POST') {
+    return res.status(405).json({ error: 'Method Not Allowed' });
+  }
+
+  try {
+    const { prompt, referenceImages } = req.body;
+    const genAI = getGenAIInstance();
+
+    const contents: any[] = [{ text: prompt }];
+    if (referenceImages && referenceImages.length > 0) {
+      referenceImages.forEach((image: string) => {
+        contents.push({ inlineData: { mimeType: 'image/png', data: image } });
+      });
+    }
+
+    const result = await genAI.models.generateContent({
+      model: 'gemini-2.5-flash-image-preview',
+      contents,
+    });
+
+    // Correctly access the candidates from the result object
+    const images: string[] = result.candidates[0].content.parts
+      .filter((part: any) => part.inlineData)
+      .map((part: any) => part.inlineData.data);
+
+    return res.status(200).json({ images });
+  } catch (error: any) {
+    console.error('Error in /api/generate:', error);
+    return res.status(500).json({ error: error.message });
+  }
+}

api/segment.ts

@@ -0,0 +1,37 @@
+import type { VercelRequest, VercelResponse } from '@vercel/node';
+import { getGenAIInstance, buildSegmentationPrompt } from './_lib/helpers.js';
+
+export default async function handler(req: VercelRequest, res: VercelResponse) {
+  if (req.method !== 'POST') {
+    return res.status(405).json({ error: 'Method Not Allowed' });
+  }
+
+  try {
+    const { image, query } = req.body;
+    const genAI = getGenAIInstance();
+
+    const contents = [
+      { text: buildSegmentationPrompt(query) },
+      { inlineData: { mimeType: 'image/png', data: image } },
+    ];
+
+    const result = await genAI.models.generateContent({
+      model: 'gemini-2.5-flash-image-preview',
+      contents,
+    });
+
+    // Correctly access the candidates from the result object
+    const responseText = result.candidates[0].content.parts[0].text;
+
+    try {
+      const jsonResponse = JSON.parse(responseText);
+      return res.status(200).json(jsonResponse);
+    } catch (e) {
+      return res.status(200).send(responseText);
+    }
+
+  } catch (error: any) {
+    console.error('Error in /api/segment:', error);
+    return res.status(500).json({ error: error.message });
+  }
+}