implement feature of Personalized Recipe and Meal Recommendation using AI

backend/routes/recommendations.js

@@ -0,0 +1,113 @@
+const express = require('express');
+const router = express.Router();
+const { GoogleGenerativeAI } = require('@google/generative-ai');
+const Meal = require('../models/Meal');
+const logger = require('../utils/logger');
+const APIKeyManager = require('../utils/apiKeyManager');
+const { recommendationPrompt } = require('../utils/prompts');
+
+const apiKeyManager = new APIKeyManager();
+
+const DAILY_TARGETS = {
+    calories: 2000,
+    protein: 50,
+    carbs: 250,
+    fat: 65,
+};
+
+router.get('/recommendations', async (req, res) => {
+    const requestId = Date.now().toString();
+
+    try {
+        logger.info(`Recommendation request started: ${requestId}`);
+
+        const now = new Date();
+        const startOfDay = new Date(now.getFullYear(), now.getMonth(), now.getDate());
+
+        const todaysMeals = await Meal.find({
+            createdAt: { $gte: startOfDay },
+        }).lean();
+
+        const dailyTotals = todaysMeals.reduce(
+            (acc, meal) => {
+                acc.calories += meal.calories || 0;
+                acc.protein += meal.macronutrients?.protein || 0;
+                acc.carbs += meal.macronutrients?.carbs || 0;
+                acc.fat += meal.macronutrients?.fat || 0;
+                return acc;
+            },
+            { calories: 0, protein: 0, carbs: 0, fat: 0 }
+        );
+
+        const remaining = {
+            calories: Math.max(0, DAILY_TARGETS.calories - dailyTotals.calories),
+            protein: Math.max(0, DAILY_TARGETS.protein - dailyTotals.protein),
+            carbs: Math.max(0, DAILY_TARGETS.carbs - dailyTotals.carbs),
+            fat: Math.max(0, DAILY_TARGETS.fat - dailyTotals.fat),
+        };
+
+        const recentFoods = todaysMeals
+            .map(meal => meal.foodName)
+            .filter(Boolean);
+
+        const prompt = recommendationPrompt(
+            remaining.calories,
+            remaining.protein,
+            remaining.carbs,
+            remaining.fat,
+            recentFoods
+        );
+
+        const apiKey = await apiKeyManager.getValidKey();
+        const genAI = new GoogleGenerativeAI(apiKey);
+        const model = genAI.getGenerativeModel({ model: 'gemini-flash-latest' });
+
+        const result = await model.generateContent([prompt]);
+        const response = await result.response;
+        let text = response.text();
+
+        text = text
+            .replace(/```json/g, '')
+            .replace(/```/g, '')
+            .trim();
+
+        let recipes;
+        try {
+            recipes = JSON.parse(text);
+            logger.info(`Recommendations generated successfully: ${requestId}`);
+        } catch (parseError) {
+            logger.error(`Failed to parse recommendation response: ${requestId}`, parseError);
+            return res.status(500).json({
+                error: 'Failed to parse AI recommendations',
+                requestId,
+            });
+        }
+
+        if (!Array.isArray(recipes)) {
+            recipes = [recipes];
+        }
+
+        res.json({
+            recipes,
+            dailyTotals,
+            remaining,
+            mealsLogged: todaysMeals.length,
+        });
+    } catch (error) {
+        if (error.message === 'All API keys have exceeded rate limits') {
+            logger.error(`API rate limit exceeded: ${requestId}`, error);
+            return res.status(429).json({
+                error: 'Service temporarily unavailable. Please try again later.',
+                requestId,
+            });
+        }
+
+        logger.error(`Recommendation generation failed: ${requestId}`, error);
+        res.status(500).json({
+            error: 'Failed to generate recommendations',
+            requestId,
+        });
+    }
+});
+
+module.exports = router;

backend/server.js

@@ -3,24 +3,17 @@ require('dotenv').config();
 const express = require('express');
 const cors = require('cors');
 const cookieParser = require('cookie-parser');
-
-const app = express();
-app.use(cookieParser());
 const helmet = require('helmet');
 const rateLimit = require('express-rate-limit');
 const multer = require('multer');
 const path = require('path');
-const chatRoute = require('./routes/chat.route');
 const fs = require('fs');
-const chatRoute = require('./routes/chat.route');
-
-const fs = require('fs');
-
 const logger = require('./utils/logger');
-
 const authRoutes = require('./routes/authRoutes');
+const chatRoute = require('./routes/chat.route');
+
+const app = express();
 
-// Validate required environment variables
 const requiredEnvVars = ['GEMINI_API_KEY', 'MONGO_URI', 'JWT_SECRET'];
 for (const envVar of requiredEnvVars) {
   if (!process.env[envVar]) {
@@ -31,25 +24,21 @@ for (const envVar of requiredEnvVars) {
 
 const PORT = process.env.PORT || 5000;
 
-// Security middleware
 app.use(helmet());
 
-// Rate limiting
 const limiter = rateLimit({
-  windowMs: 15 * 60 * 1000, // 15 minutes
-  max: 100, // limit each IP to 100 requests per windowMs
+  windowMs: 15 * 60 * 1000,
+  max: 100,
   message: 'Too many requests from this IP',
 });
 app.use(limiter);
 
-// Stricter rate limit for upload endpoint
 const uploadLimiter = rateLimit({
   windowMs: 15 * 60 * 1000,
-  max: 10, // 10 uploads per 15 minutes
+  max: 10,
   message: 'Upload rate limit exceeded',
 });
 
-// CORS configuration
 const allowedOrigins = (
   process.env.CORS_ALLOWED_ORIGINS ||
   'http://localhost:3000,http://localhost:5173'
@@ -60,7 +49,6 @@ const allowedOrigins = (
 
 const corsOptions = {
   origin: (origin, callback) => {
-    // Allow requests with no origin (like mobile apps or curl requests)
     if (!origin) return callback(null, true);
 
     if (
@@ -89,7 +77,7 @@ app.use(cookieParser());
 app.use('/uploads', express.static(path.join(__dirname, 'uploads')));
 
 app.use('/api/auth', authRoutes);
-// Ensure uploads directory exists
+
 const uploadsDir = path.join(__dirname, 'uploads');
 try {
   if (!fs.existsSync(uploadsDir)) {
@@ -100,15 +88,16 @@ try {
   process.exit(1);
 }
 
-// Routes
 const analyzeRoutes = require('./routes/analyze');
+const recommendationsRoutes = require('./routes/recommendations');
+
 app.use('/api', uploadLimiter, analyzeRoutes);
+app.use('/api', recommendationsRoutes);
 
 app.get('/', (req, res) => {
   res.send('NutriLens Backend is running');
 });
 
-// Global Error Handler
 app.use((err, req, res, _next) => {
   if (err instanceof multer.MulterError) {
     if (err.code === 'LIMIT_FILE_SIZE') {
@@ -129,7 +118,6 @@ app.use((err, req, res, _next) => {
   res.status(500).json({ error: 'Internal server error' });
 });
 
-// Database Connection and Server Start
 const connectDB = require('./config/db');
 
 const startServer = async () => {

backend/utils/prompts.js

@@ -1,11 +1,10 @@
-// Text chat prompt
 const chatPrompt = message => `You are NutriBot, a helpful nutrition assistant.
 User Message: "${message}"
 
 Respond in valid JSON format ONLY:
 {
   "text": "Your helpful response text here...",
-  "report": { // Optional: include ONLY if user describes a specific meal to analyze
+  "report": {
      "carbs": 0,
      "protein": 0,
      "fats": 0
@@ -21,7 +20,6 @@ Rules:
 - Don't force a report if not applicable.
 `;
 
-// Image analysis prompt
 const analysisPrompt =
   quantity => `Analyze this food image thoroughly. Identify the food item(s).
 
@@ -71,13 +69,53 @@ Return ONLY valid JSON in the following format (all numeric values MUST be numbe
 }
 Notes:
 - All gram values should be in grams (g)
-- Vitamins and minerals in milligrams (mg) or appropriate units (mcg for certain vitamins if standard, but preferably normalize to mg or specify unit if implicit constraints allow - however schema implies Number so stick to standard numerical values, e.g. mg for Sodium/Potassium/Calcium/Iron/Magnesium/Zinc. Vitamin A/D/B12/C usually mg or mcg. Provide best estimate in standard units.)
+- Vitamins and minerals in milligrams (mg) or appropriate units
 - Percentages should be whole numbers (0-100)
 - healthScore should be 0-100
 - Be accurate with portion size estimation based on the User Context provided.
 - Provide NON-ZERO estimates for micronutrients if reasonable trace amounts exist. Do not just zero them out unless completely absent.`;
 
+const recommendationPrompt = (remainingCalories, remainingProtein, remainingCarbs, remainingFat, recentFoods) => {
+  const foodContext = recentFoods && recentFoods.length > 0
+    ? `The user has recently eaten: ${recentFoods.join(', ')}. Suggest different foods to add variety.`
+    : 'The user has not logged any meals today yet. Suggest balanced meals for the day.';
+
+  return `You are a professional nutritionist AI. ${foodContext}
+
+The user's remaining daily nutritional targets are:
+- Calories: ${remainingCalories} kcal
+- Protein: ${remainingProtein} g
+- Carbs: ${remainingCarbs} g
+- Fat: ${remainingFat} g
+
+Suggest exactly 3 healthy, practical recipes that collectively help the user meet their remaining nutritional goals. Each recipe should be simple to prepare at home.
+
+Return ONLY valid JSON as an array of exactly 3 objects in the following format (all numeric values MUST be numbers, not strings):
+[
+  {
+    "title": "Recipe Name",
+    "prepTime": "15 mins",
+    "calories": 0,
+    "protein": 0,
+    "carbs": 0,
+    "fat": 0,
+    "ingredients": ["ingredient 1 with quantity", "ingredient 2 with quantity"],
+    "instructions": ["Step 1 description", "Step 2 description"],
+    "tags": ["high-protein", "low-carb"]
+  }
+]
+
+Rules:
+- Each recipe should target roughly one-third of the remaining daily macros.
+- Tags should be relevant descriptors like "high-protein", "low-carb", "low-fat", "quick", "vegan", "gluten-free", etc.
+- Keep instructions concise but actionable (3-6 steps per recipe).
+- Include 4-8 ingredients per recipe with specific quantities.
+- Prep time should be realistic.
+- All calorie values in kcal, macros in grams.`;
+};
+
 module.exports = {
   chatPrompt,
   analysisPrompt,
+  recommendationPrompt,
 };

frontend/src/App.tsx

@@ -5,6 +5,7 @@ import { Navbar } from './components/Navbar';
 import { HistorySidebar } from './components/HistorySidebar';
 import { Home } from './pages/Home';
 import { Analysis } from './pages/Analysis';
+import { Recipes } from './pages/Recipes';
 import { getHistory, clearHistory } from './api';
 import type { MealData } from './api';
 import Footer from './components/Footer';
@@ -29,7 +30,6 @@ const App: React.FC = () => {
 
     fetchHistory();
 
-    // Listen for history updates from other components
     const handleHistoryUpdate = () => {
       fetchHistory();
     };
@@ -51,15 +51,14 @@ const App: React.FC = () => {
 
   return (
     <div className='min-h-screen flex flex-col transition-colors duration-300'>
-      {/* Navbar */}
       <Navbar showHistory={showHistory} setShowHistory={setShowHistory} />
 
-      {/* Main Content */}
       <main className='flex-1 max-w-4xl mx-auto px-4 pt-32'>
         <AnimatePresence mode='wait'>
           <Routes location={location} key={location.pathname}>
             <Route path='/' element={<Home />} />
             <Route path='/analysis' element={<Analysis />} />
+            <Route path='/recipes' element={<Recipes />} />
           </Routes>
         </AnimatePresence>
 
@@ -75,7 +74,6 @@ const App: React.FC = () => {
         />
       </main>
 
-      {/* Footer */}
       <Footer />
       <Chatbot />
       <ScrollToTop />

frontend/src/api.ts

@@ -54,10 +54,9 @@ export interface MealData {
     benefits: string[];
     concerns: string[];
   };
-  // flattened properties for backward compatibility or direct access if needed
-  fat?: number; // legacy
-  protein?: number; // legacy
-  carbs?: number; // legacy
+  fat?: number;
+  protein?: number;
+  carbs?: number;
   analysis: string;
   recommendation: string;
   imagePath: string;
@@ -73,6 +72,18 @@ export interface HistoryResponse {
   };
 }
 
+export interface Recipe {
+  title: string;
+  prepTime: string;
+  calories: number;
+  protein: number;
+  carbs: number;
+  fat: number;
+  ingredients: string[];
+  instructions: string[];
+  tags: string[];
+}
+
 import { compressImage } from './utils/imageOptimizer';
 
 export const analyzeImage = async (file: File, quantity?: string): Promise<MealData> => {
@@ -134,3 +145,8 @@ export const sendChatMessage = async (
   const response = await axios.post(`${API_URL}/chat`, { message });
   return response.data;
 };
+
+export const getRecommendations = async (): Promise<Recipe[]> => {
+  const response = await axios.get(`${API_URL}/recommendations`);
+  return response.data.recipes;
+};