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"node dist/index.js" + }, + "keywords": [], + "author": "", + "license": "ISC", + "description": "", + "dependencies": { + "@google/generative-ai": "^0.24.1", + "@types/cors": "^2.8.19", + "@types/express": "^5.0.6", + "@types/multer": "^2.0.0", + "@types/node": "^24.10.1", + "axios": "^1.13.2", + "cors": "^2.8.5", + "dotenv": "^17.2.3", + "express": "^5.2.1", + "mongoose": "^9.1.1", + "multer": "^2.0.2", + "ts-node-dev": "^2.0.0", + "typescript": "^5.9.3" + } +} diff --git a/Backend/src/config/db.ts b/Backend/src/config/db.ts new file mode 100644 index 00000000..67b1ddef --- /dev/null +++ b/Backend/src/config/db.ts @@ -0,0 +1,14 @@ +import mongoose from "mongoose"; +import dotenv from 'dotenv'; + +dotenv.config(); + +export const connectDB = async(): Promise => { + try{ + await mongoose.connect(process.env.MONGO_URL || ""); + console.log('mongoDB is connected'); + }catch(err){ + console.error('mongoConnection Failed'); + process.exit(1); + } +} \ No newline at end of file diff --git a/Backend/src/controllers/analyzeController.ts b/Backend/src/controllers/analyzeController.ts new file mode 100644 index 00000000..1ffa2b7c --- /dev/null +++ b/Backend/src/controllers/analyzeController.ts @@ -0,0 +1,55 @@ +import { Request, Response } from "express"; +import crypto from "crypto"; +import { analyzeWithGemini } from "../services/geminiService"; +import { getNutritionData } from "../services/edamamService"; +import Food from "../models/Food"; + +export const analyzeImage = async ( + req: Request, + res: Response +): Promise => { + try { + if (!req.file) { + res.status(400).json({ message: "No image uploaded" }); + return; + } + + const imageBuffer = req.file.buffer; + const mimeType = req.file.mimetype; + + const imageHash = crypto + .createHash("sha256") + .update(imageBuffer) + .digest("hex"); + + const cachedFood = await Food.findOne({ imageHash }); + if (cachedFood) { + res.json(cachedFood); + return; + } + + const geminiResult = await analyzeWithGemini(imageBuffer, mimeType); + + const nutritionData = await getNutritionData( + geminiResult.quantity, + geminiResult.foodName + ); + + const newFood = new Food({ + imageHash, + foodName: geminiResult.foodName, + quantity: geminiResult.quantity, + nutrition: nutritionData, + }); + + await newFood.save(); + + res.json(newFood); + } catch (error) { + console.error("Analysis Error:", error); + res.status(500).json({ + message: "Failed to analyze image", + error: (error as Error).message, + }); + } +}; diff --git a/Backend/src/index.ts b/Backend/src/index.ts new file mode 100644 index 00000000..dabbe1b2 --- /dev/null +++ b/Backend/src/index.ts @@ -0,0 +1,27 @@ +import express from "express"; +import cors from "cors"; +import dotenv from "dotenv"; +import { connectDB } from "./config/db"; +import analyzeRoute from "./routes/analyzeRoute"; + +dotenv.config(); +connectDB(); + +const app = express(); +const port = process.env.PORT || 8080; + +app.use(cors({ + origin: process.env.VITE_URL, + credentials: true +})); + +app.use(express.json()); +app.get("/", (_, res) => { + res.send("Hello from FoodSnap AI Backend!"); +}); + +app.use("/api/v1", analyzeRoute); + +app.listen(port, () => { + console.log(`Server is running on port ${port}`); +}); diff --git a/Backend/src/middlewares/uploadMiddleware.ts b/Backend/src/middlewares/uploadMiddleware.ts new file mode 100644 index 00000000..a0b06bd2 --- /dev/null +++ b/Backend/src/middlewares/uploadMiddleware.ts @@ -0,0 +1,17 @@ +import multer from 'multer'; + +const storage = multer.memoryStorage(); + +export const upload = multer({ + storage: storage, + limits: { + fileSize: 5 * 1024 * 1024, + }, + fileFilter: (_, file, cb) => { + if (file.mimetype.startsWith('image/')) { + cb(null, true); + } else { + cb(new Error('Only images are allowed')); + } + }, +}); diff --git a/Backend/src/models/Food.ts b/Backend/src/models/Food.ts new file mode 100644 index 00000000..9c3e69bc --- /dev/null +++ b/Backend/src/models/Food.ts @@ -0,0 +1,44 @@ +import mongoose, { Document, Schema } from "mongoose"; + +export interface IFood extends Document { + imageHash: string; + foodName: string; + quantity: string; + nutrition: { + Calories: { quantity: number; unit: string }; + Protein: { quantity: number; unit: string }; + Fat: { quantity: number; unit: string }; + Carbohydrates: { quantity: number; unit: string }; + }; + createdAt: Date; + updatedAt: Date; +} + +const FoodSchema = new Schema( + { + imageHash: { + type: String, + required: true, + unique: true, + trim: true, + }, + foodName: { + type: String, + required: true, + trim: true, + }, + quantity: { + type: String, + required: true, + }, + nutrition: { + type: Object, + required: true, + }, + }, + { + timestamps: true, + } +); + +export default mongoose.model("Food", FoodSchema); diff --git a/Backend/src/routes/analyzeRoute.ts b/Backend/src/routes/analyzeRoute.ts new file mode 100644 index 00000000..4d408ea3 --- /dev/null +++ b/Backend/src/routes/analyzeRoute.ts @@ -0,0 +1,9 @@ +import express from 'express'; +import { analyzeImage } from '../controllers/analyzeController'; +import { upload } from '../middlewares/uploadMiddleware'; + +const router = express.Router(); + +router.post('/analyze', upload.single('image'), analyzeImage); + +export default router; diff --git a/Backend/src/services/edamamService.ts b/Backend/src/services/edamamService.ts new file mode 100644 index 00000000..86827a85 --- /dev/null +++ b/Backend/src/services/edamamService.ts @@ -0,0 +1,69 @@ +import axios from 'axios'; +import dotenv from 'dotenv'; + +dotenv.config(); + +const EDAMAM_APP_ID = process.env.EDAMAM_APP_ID; +const EDAMAM_APP_KEY = process.env.EDAMAM_APP_KEY; + +export interface NutritionData { + Calories: { quantity: number; unit: string }; + Protein: { quantity: number; unit: string }; + Fat: { quantity: number; unit: string }; + Carbohydrates: { quantity: number; unit: string }; +} + +export const getNutritionData = async (quantity: string, foodName: string): Promise => { + try { + const query = `${quantity} ${foodName}`; + + const response = await axios.get("https://api.edamam.com/api/nutrition-data", { + params: { + app_id: EDAMAM_APP_ID, + app_key: EDAMAM_APP_KEY, + ingr: query, + }, + }); + + const data = response.data; + + let finalNutrition: NutritionData = { + Calories: { quantity: 0, unit: 'kcal' }, + Protein: { quantity: 0, unit: 'g' }, + Fat: { quantity: 0, unit: 'g' }, + Carbohydrates: { quantity: 0, unit: 'g' } + }; + + const hasTopLevelData = data.totalNutrients && Object.keys(data.totalNutrients).length > 0; + + if (hasTopLevelData) { + if (data.totalNutrients.ENERC_KCAL) { + finalNutrition.Calories = { quantity: data.totalNutrients.ENERC_KCAL.quantity, unit: data.totalNutrients.ENERC_KCAL.unit }; + } + if (data.totalNutrients.PROCNT) { + finalNutrition.Protein = { quantity: data.totalNutrients.PROCNT.quantity, unit: data.totalNutrients.PROCNT.unit }; + } + if (data.totalNutrients.FAT) { + finalNutrition.Fat = { quantity: data.totalNutrients.FAT.quantity, unit: data.totalNutrients.FAT.unit }; + } + if (data.totalNutrients.CHOCDF) { + finalNutrition.Carbohydrates = { quantity: data.totalNutrients.CHOCDF.quantity, unit: data.totalNutrients.CHOCDF.unit }; + } + + } else if (data.ingredients && data.ingredients.length > 0) { + const parsed = data.ingredients[0].parsed; + if (parsed && parsed.length > 0 && parsed[0].nutrients) { + const n = parsed[0].nutrients; + + if (n.ENERC_KCAL) finalNutrition.Calories = { quantity: n.ENERC_KCAL.quantity, unit: n.ENERC_KCAL.unit }; + if (n.PROCNT) finalNutrition.Protein = { quantity: n.PROCNT.quantity, unit: n.PROCNT.unit }; + if (n.FAT) finalNutrition.Fat = { quantity: n.FAT.quantity, unit: n.FAT.unit }; + if (n.CHOCDF) finalNutrition.Carbohydrates = { quantity: n.CHOCDF.quantity, unit: n.CHOCDF.unit }; + } + } + + return finalNutrition; + } catch (error) { + throw new Error('Failed to fetch nutrition data from Edamam'); + } +}; diff --git a/Backend/src/services/geminiService.ts b/Backend/src/services/geminiService.ts new file mode 100644 index 00000000..793464cd --- /dev/null +++ b/Backend/src/services/geminiService.ts @@ -0,0 +1,44 @@ +import { GoogleGenerativeAI } from "@google/generative-ai"; +import dotenv from "dotenv"; + +dotenv.config(); + +const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY || ""); +const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" }); + +interface GeminiResponse { + foodName: string; + quantity: string; +} + +export const analyzeWithGemini = async ( + imageBuffer: Buffer, + mimeType: string +): Promise => { + try { + const prompt = ` + Analyze this food image. + Identify the food item and estimate the portion size/quantity. + Return ONLY a valid JSON object in the following format, with no markdown code blocks: + { + "foodName": "name of food", + "quantity": "estimated quantity (e.g., 100gm, 1kg)" + } + `; + + const imagePart = { + inlineData: { + data: imageBuffer.toString("base64"), + mimeType: mimeType, + }, + }; + + const result = await model.generateContent([prompt, imagePart]); + const response = await result.response; + const text = response.text(); + const jsonResponse: GeminiResponse = JSON.parse(text); + return jsonResponse; + } catch (error) { + throw new Error("Failed to analyze image with Gemini"); + } +}; diff --git a/Backend/tsconfig.json b/Backend/tsconfig.json new file mode 100644 index 00000000..dcbb8829 --- /dev/null +++ b/Backend/tsconfig.json @@ -0,0 +1,62 @@ +{ + "compilerOptions": { + /* Basic Options */ + "target": "es2022" /* Specify ECMAScript target version: 'ES3' (default), 'ES5', 'ES2015', 'ES2016', 'ES2017','ES2018' or 'ESNEXT'. */, + "module": "commonjs" /* Specify module code generation: 'none', 'commonjs', 'amd', 'system', 'umd', 'es2015', or 'ESNext'. */, + "lib": [ + "esnext", + "dom" + ] /* Specify library files to be included in the compilation. */, + // "allowJs": true, /* Allow javascript files to be compiled. */ + // "checkJs": true, /* Report errors in .js files. */ + "jsx": "react-jsx" /* Specify JSX code generation: 'preserve', 'react-native', or 'react'. */, + // "declaration": true, /* Generates corresponding '.d.ts' file. */ + // "declarationMap": true, /* Generates a sourcemap for each corresponding '.d.ts' file. */ + // "sourceMap": true, /* Generates corresponding '.map' file. */ + // "outFile": "./", /* Concatenate and emit output to single file. */ + "outDir": "./dist/" /* Redirect output structure to the directory. */, + "rootDir": "./src" /* Specify the root directory of input files. Use to control the output directory structure with --outDir. */, + // "composite": true, /* Enable project compilation */ + // "removeComments": true, /* Do not emit comments to output. */ + "noEmit": false /* Do not emit outputs. */, + // "importHelpers": true /* Import emit helpers from 'tslib'. */, + // "downlevelIteration": true, /* Provide full support for iterables in 'for-of', spread, and destructuring when targeting 'ES5' or 'ES3'. */ + // "isolatedModules": true, /* Transpile each file as a separate module (similar to 'ts.transpileModule'). */ + /* Strict Type-Checking Options */ + "strict": true /* Enable all strict type-checking options. */, + // "noImplicitAny": true, /* Raise error on expressions and declarations with an implied 'any' type. */ + // "strictNullChecks": true, /* Enable strict null checks. */ + // "strictFunctionTypes": true, /* Enable strict checking of function types. */ + // "strictBindCallApply": true, /* Enable strict 'bind', 'call', and 'apply' methods on functions. */ + // "strictPropertyInitialization": true, /* Enable strict checking of property initialization in classes. */ + // "noImplicitThis": true, /* Raise error on 'this' expressions with an implied 'any' type. */ + // "alwaysStrict": true, /* Parse in strict mode and emit "use strict" for each source file. */ + /* Additional Checks */ + "noUnusedLocals": true /* Report errors on unused locals. */, + "noUnusedParameters": true /* Report errors on unused parameters. */, + // "noImplicitReturns": true, /* Report error when not all code paths in function return a value. */ + // "noFallthroughCasesInSwitch": true, /* Report errors for fallthrough cases in switch statement. */ + /* Module Resolution Options */ + "moduleResolution": "node" /* Specify module resolution strategy: 'node' (Node.js) or 'classic' (TypeScript pre-1.6). */, + // "baseUrl": "./", /* Base directory to resolve non-absolute module names. */ + // "paths": {}, /* A series of entries which re-map imports to lookup locations relative to the 'baseUrl'. */ + // "rootDirs": [], /* List of root folders whose combined content represents the structure of the project at runtime. */ + // "typeRoots": [], /* List of folders to include type definitions from. */ + // "types": [], /* Type declaration files to be included in compilation. */ + // "allowSyntheticDefaultImports": true /* Allow default imports from modules with no default export. This does not affect code emit, just typechecking. */, + "esModuleInterop": true /* Enables emit interoperability between CommonJS and ES Modules via creation of namespace objects for all imports. Implies 'allowSyntheticDefaultImports'. */, + // "preserveSymlinks": true, /* Do not resolve the real path of symlinks. */ + /* Source Map Options */ + // "sourceRoot": "", /* Specify the location where debugger should locate TypeScript files instead of source locations. */ + // "mapRoot": "", /* Specify the location where debugger should locate map files instead of generated locations. */ + // "inlineSourceMap": true, /* Emit a single file with source maps instead of having a separate file. */ + // "inlineSources": true, /* Emit the source alongside the sourcemaps within a single file; requires '--inlineSourceMap' or '--sourceMap' to be set. */ + /* Experimental Options */ + // "experimentalDecorators": true, /* Enables experimental support for ES7 decorators. */ + // "emitDecoratorMetadata": true, /* Enables experimental support for emitting type metadata for decorators. */ + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true + }, + "exclude": ["node_modules"] +} diff --git a/Frontend/.gitignore b/Frontend/.gitignore new file mode 100644 index 00000000..7ceb59f8 --- /dev/null +++ b/Frontend/.gitignore @@ -0,0 +1,25 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? +.env diff --git a/Frontend/README.md b/Frontend/README.md new file mode 100644 index 00000000..d2e77611 --- /dev/null +++ b/Frontend/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/Frontend/eslint.config.js b/Frontend/eslint.config.js new file mode 100644 index 00000000..5e6b472f --- /dev/null +++ b/Frontend/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/Frontend/index.html b/Frontend/index.html new file mode 100644 index 00000000..d35e0e9a --- /dev/null +++ b/Frontend/index.html @@ -0,0 +1,27 @@ + + + + + + + + + + FoodSnap AI + + +
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+ } + } + } +} diff --git a/Frontend/package.json b/Frontend/package.json new file mode 100644 index 00000000..9fc09626 --- /dev/null +++ b/Frontend/package.json @@ -0,0 +1,35 @@ +{ + "name": "foodsnapai", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "@tailwindcss/vite": "^4.1.17", + "axios": "^1.13.2", + "react": "^19.2.0", + "react-dom": "^19.2.0" + }, + "devDependencies": { + "@eslint/js": "^9.39.1", + "@types/node": "^24.10.1", + "@types/react": "^19.2.5", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "autoprefixer": "^10.4.22", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "postcss": "^8.5.6", + "tailwindcss": "^4.1.17", + "typescript": "~5.9.3", + "typescript-eslint": "^8.46.4", + "vite": "^7.2.4" + } +} diff 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a/Frontend/public/favicon/favicon-32x32.png b/Frontend/public/favicon/favicon-32x32.png new file mode 100644 index 00000000..e0a6e6c8 Binary files /dev/null and b/Frontend/public/favicon/favicon-32x32.png differ diff --git a/Frontend/public/favicon/favicon.ico b/Frontend/public/favicon/favicon.ico new file mode 100644 index 00000000..cf5148f7 Binary files /dev/null and b/Frontend/public/favicon/favicon.ico differ diff --git a/Frontend/public/favicon/site.webmanifest b/Frontend/public/favicon/site.webmanifest new file mode 100644 index 00000000..45dc8a20 --- /dev/null +++ b/Frontend/public/favicon/site.webmanifest @@ -0,0 +1 @@ +{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"} \ No newline at end of file diff --git a/Frontend/src/App.tsx b/Frontend/src/App.tsx new file mode 100644 index 00000000..6ec744d1 --- /dev/null +++ b/Frontend/src/App.tsx @@ -0,0 +1,9 @@ +import Tracker from "./pages/Tracker" + +function App() { + return( + + ) +} + +export default App diff --git a/Frontend/src/config/axios.ts b/Frontend/src/config/axios.ts new file mode 100644 index 00000000..1b8e2184 --- /dev/null +++ b/Frontend/src/config/axios.ts @@ -0,0 +1,7 @@ +import axios from 'axios' + +const instance = axios.create({ + baseURL: import.meta.env.VITE_API_URL, +}); + +export default instance; diff --git a/Frontend/src/index.css b/Frontend/src/index.css new file mode 100644 index 00000000..f1d8c73c --- /dev/null +++ b/Frontend/src/index.css @@ -0,0 +1 @@ +@import "tailwindcss"; diff --git a/Frontend/src/main.tsx b/Frontend/src/main.tsx new file mode 100644 index 00000000..1e008043 --- /dev/null +++ b/Frontend/src/main.tsx @@ -0,0 +1,10 @@ +import { StrictMode } from "react"; +import { createRoot } from "react-dom/client"; +import "./index.css"; +import App from "./App.tsx"; + +createRoot(document.getElementById("root")!).render( + + + +); diff --git a/Frontend/src/pages/Tracker.tsx b/Frontend/src/pages/Tracker.tsx new file mode 100644 index 00000000..e9fd49c7 --- /dev/null +++ b/Frontend/src/pages/Tracker.tsx @@ -0,0 +1,224 @@ +import React, { useState, useRef } from "react"; +import { analyzeImage, type AnalyzeResponse } from "../services/analyzeService"; + +const Tracker = () => { + const [image, setImage] = useState(null); + const [file, setFile] = useState(null); + const [data, setData] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const fileInputRef = useRef(null); + + const handleImageUpload = (e: React.ChangeEvent) => { + const selectedFile = e.target.files?.[0]; + if (selectedFile) { + setFile(selectedFile); + setData(null); + setError(null); + const reader = new FileReader(); + reader.onloadend = () => { + setImage(reader.result as string); + }; + reader.readAsDataURL(selectedFile); + } + }; + + const handleRemoveImage = () => { + setImage(null); + setFile(null); + setData(null); + setError(null); + if (fileInputRef.current) { + fileInputRef.current.value = ""; + } + }; + + const handleAnalyze = async () => { + if (!file) return; + + setLoading(true); + setError(null); + try { + const result = await analyzeImage(file); + setData(result); + } catch (err) { + setError("Failed to analyze image. Please try again."); + console.error(err); + } finally { + setLoading(false); + } + }; + + return ( +
+ {/* Background Gradients */} +
+
+
+
+ +
+ {/* Header */} +
+

+ Nutrition AI +

+

+ Your personal food analyst. Snap your meal to get instant + nutrition details. +

+
+ + {/* Main Card */} +
+
+ {/* Upload Area */} +
+ !image && fileInputRef.current?.click() + }> + + + {image ? ( +
+ Food preview +
+ +
+
+ ) : ( +
+
+ + + + + +
+
+

+ Upload your Meal +

+

+ JPG, PNG, WEBP supported +

+
+
+ )} +
+ + {/* Error Message */} + {error && ( +
+ {error} +
+ )} + + {/* Action Button */} +
+ +
+
+
+ + {/* Nutrition Grid */} + {data && ( +
+ {[ + { label: "Food Name", value: data.foodName }, + { label: "Total Quantity", value: data.quantity }, + { label: "Calories", value: Math.round(data.nutrition?.Calories?.quantity || 0), unit: data.nutrition?.Calories?.unit || "kcal" }, + { label: "Protein", value: Math.round(data.nutrition?.Protein?.quantity || 0), unit: data.nutrition?.Protein?.unit || "g" }, + { label: "Fat", value: Math.round(data.nutrition?.Fat?.quantity || 0), unit: data.nutrition?.Fat?.unit || "g" }, + { label: "Carbs", value: Math.round(data.nutrition?.Carbohydrates?.quantity || 0), unit: data.nutrition?.Carbohydrates?.unit || "g" }, + ].map((item, index) => ( +
+

{item.label}

+

+ {item.value} {item.unit} +

+
+ ))} +
+ )} +
+
+ ); +}; + +export default Tracker; diff --git a/Frontend/src/services/analyzeService.ts b/Frontend/src/services/analyzeService.ts new file mode 100644 index 00000000..151779ba --- /dev/null +++ b/Frontend/src/services/analyzeService.ts @@ -0,0 +1,24 @@ +import instance from "../config/axios"; + +export interface NutritionInfo { + Calories: { quantity: number; unit: string }; + Protein: { quantity: number; unit: string }; + Fat: { quantity: number; unit: string }; + Carbohydrates: { quantity: number; unit: string }; +} + +export interface AnalyzeResponse { + imageHash: string; + foodName: string; + quantity: string; + nutrition: NutritionInfo; +} + +export const analyzeImage = async (imageFile: File): Promise => { + const formData = new FormData(); + formData.append('image', imageFile); + + const response = await instance.post('/api/v1/analyze', formData); + + return response.data; +}; diff --git a/Frontend/tsconfig.app.json b/Frontend/tsconfig.app.json new file mode 100644 index 00000000..a9b5a59c --- /dev/null +++ b/Frontend/tsconfig.app.json @@ -0,0 +1,28 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "ES2022", + "useDefineForClassFields": true, + "lib": ["ES2022", "DOM", "DOM.Iterable"], + "module": "ESNext", + "types": ["vite/client"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src"] +} diff --git a/Frontend/tsconfig.json b/Frontend/tsconfig.json new file mode 100644 index 00000000..1ffef600 --- /dev/null +++ b/Frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/Frontend/tsconfig.node.json b/Frontend/tsconfig.node.json new file mode 100644 index 00000000..8a67f62f --- /dev/null +++ b/Frontend/tsconfig.node.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2023", + "lib": ["ES2023"], + "module": "ESNext", + "types": ["node"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/Frontend/vite.config.ts b/Frontend/vite.config.ts new file mode 100644 index 00000000..4f39ea52 --- /dev/null +++ b/Frontend/vite.config.ts @@ -0,0 +1,8 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; +import tailwindcss from "@tailwindcss/vite"; + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react(), tailwindcss()], +}); diff --git a/README.md b/README.md index b58f55ca..b8535a73 100644 --- a/README.md +++ b/README.md @@ -1,194 +1,117 @@ -# FoodSnap AI - Hackathon Project +# FoodSnap AI 🍎📸 -🎉 **Welcome to the FoodSnap AI Hackathon!** 🎉 +**FoodSnap AI** is a smart nutrition tracking application that leverages artificial intelligence to analyze food images, identify dishes, and estimate their nutritional content. -Get ready to combine your coding skills with the fascinating world of artificial intelligence and nutrition! We're excited to see what innovative solutions you come up with. +## 🚩 Problem Statement -**The Challenge:** -Your mission, should you choose to accept it, is to develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image. +Tracking daily nutritional intake is often a tedious and error-prone process. Users struggle to accurately estimate portion sizes and identify the caloric content of complex dishes. This friction leads to inconsistent tracking, lack of awareness about actual intake, and ultimately, a loss of motivation to maintain healthy dietary habits. -## Table of Contents +## 💡 Solution -1. [Project Overview](#project-overview) -2. [Why This Project?](#why-this-project) -3. [Scope & Implementation Freedom](#project-scope--implementation-freedom) -4. [Key Objectives / Potential Features](#key-objectives--potential-features) -5. [Helpful Resources & APIs](#helpful-resources--apis) - * [Food Image Datasets](#food-image-datasets) - * [Nutrition Information Databases & APIs](#nutrition-information-databases--apis) - * [Machine Learning / AI Tools](#machine-learning--ai-tools) -6. [Getting Started: Git & Submission Workflow](#getting-started-git--submission-workflow) - * [1. Fork the Repository](#1-fork-the-repository) - * [2. Clone Your Forked Repository](#2-clone-your-forked-repository) - * [3. Create a New Branch](#3-create-a-new-branch) - * [4. Develop Your Project](#4-develop-your-project) - * [5. Push Your Branch to Your Fork](#5-push-your-branch-to-your-fork) - * [6. Submit Your Project (Create a Pull Request)](#6-submit-your-project-create-a-pull-request) -7. [What to Include in Your Submission](#what-to-include-in-your-submission) -8. [Judging Criteria (General Pointers)](#judging-criteria-general-pointers) +**FoodSnap AI** eliminates the guesswork from nutrition tracking. By leveraging advanced computer vision and Generative AI (Google Gemini), the application instantly analyzes food photos to identify dishes and estimate portion sizes. It then cross-references this data with a reliable nutrition database (Edamam) to provide an accurate breakdown of calories, proteins, fats, and carbohydrates. This seamless, "snap-and-track" experience empowers users to make informed dietary choices with minimal effort. +## 🚀 Features ---- - -## Project Overview - -**Project Name:** FoodSnap AI - -**The Challenge:** -Develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image. - -## Why This Project? +- **AI-Powered Analysis**: Uses Google's Gemini AI to identify food items and estimate portion sizes from images. +- **Detailed Nutrition Data**: Integrates with the Edamam API to retrieve accurate calorie, protein, fat, and carbohydrate information. +- **History & Tracking**: Keeps a record of your analyzed meals (stored via MongoDB). +- **Responsive Design**: Built with React and Tailwind CSS for a modern, mobile-friendly interface. -Understanding calorie intake is crucial for health and fitness. Manually logging food can be tedious. FoodSnap AI aims to simplify this process, making nutritional awareness more accessible to everyone. Imagine snapping a photo of your meal and instantly getting its nutritional information! +## 🛠️ Tech Stack -## Project Scope & Implementation Freedom +### Frontend -You have complete freedom in how you bring FoodSnap AI to life! +- **React** (v19) +- **Vite** +- **Tailwind CSS** (v4) +- **Axios** +- **TypeScript** -* **Platform:** Mobile App (iOS, Android, cross-platform), Web App, Website, or even a Command Line Interface (CLI) tool. -* **Technology Stack:** Use any programming languages, frameworks, libraries, or APIs you prefer. Python, JavaScript, Java, Swift, Kotlin, Ruby, Go – the choice is yours! -* **Approach:** You can use pre-trained machine learning models, train your own, leverage existing food recognition APIs, or come up with a completely novel approach. +### Backend -## Key Objectives / Potential Features +- **Node.js** +- **Express** +- **MongoDB** (with Mongoose) +- **Google Generative AI (Gemini)** SDK +- **Edamam API** +- **TypeScript** -1. **Image Input:** The system must accept an image of food as input. -2. **Food Identification (Implicit or Explicit):** The system needs to identify the food item(s) in the image. This could be a direct output or an internal step. -3. **Calorie Estimation:** Based on the identified food, estimate its total calories. -4. **Macronutrient Breakdown (Bonus):** If possible, also estimate protein, carbohydrates, and fats. -5. **Portion Size Consideration (Advanced Bonus):** Accurately estimating portion size from an image is challenging but would be a significant enhancement. -6. **User Interface (for non-CLI):** If you're building an app or website, make it user-friendly and intuitive. -7. **Multiple Food Items (Advanced Bonus):** Can your solution handle an image with multiple food items on a plate? - -## Helpful Resources & APIs - -To get you started, here are some resources that might be useful. You are **not limited** to these and are encouraged to explore! +--- -### Food Image Datasets -*(For training/inspiration if you go the custom ML route)* +## ⚙️ Setup & Installation -* **Food-101:** [https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) (101 food categories, 101,000 images) -* **UECFood-100 / UECFood-256:** [http://foodcam.mobi/dataset.html](http://foodcam.mobi/dataset.html) (Japanese food primarily, good for object detection) -* **Recipe1M+:** [http://pic2recipe.csail.mit.edu/](http://pic2recipe.csail.mit.edu/) (Images and recipes) -* **Google Images / Flickr:** Can be used for scraping specific food images (be mindful of terms of service). +Follow these steps to get the project running locally. -### Nutrition Information Databases & APIs -*(For calorie/macro lookup)* +### 1. Prerequisites -* **USDA FoodData Central API:** [https://fdc.nal.usda.gov/api-guide.html](https://fdc.nal.usda.gov/api-guide.html) (Comprehensive US food composition database) -* **Edamam Food Database API:** [https://developer.edamam.com/food-database-api](https://developer.edamam.com/food-database-api) (Offers free tier for recipe analysis and food database lookup) -* **Spoonacular API:** [https://spoonacular.com/food-api](https://spoonacular.com/food-api) (Nutrition, recipes, food products, free tier available) -* **MyFitnessPal / CalorieKing / FatSecret:** While direct API access might be limited or paid, these websites are excellent sources for manual data collection or understanding how nutritional information is presented. Web scraping *could* be an option, but always respect `robots.txt` and terms of service. -* **Open Food Facts:** [https://world.openfoodfacts.org/](https://world.openfoodfacts.org/) (A collaborative, free, and open database of food products from around the world. They have an API.) +Ensure you have the following installed: -### Machine Learning / AI Tools +- [Node.js](https://nodejs.org/) (v16+) +- [MongoDB](https://www.mongodb.com/try/download/community) (running locally or a cloud Atlas URI) -* **TensorFlow / Keras:** For building and training custom models. -* **PyTorch:** Another popular deep learning framework. -* **OpenCV:** For image processing tasks. -* **Pre-trained Image Recognition Models:** (e.g., MobileNet, ResNet, InceptionV3 available via TensorFlow Hub, PyTorch Hub, etc.) These can often be fine-tuned for food recognition. -* **Cloud AI Services:** Google Cloud Vision AI, AWS Rekognition, Azure Computer Vision (these often have free tiers for experimentation and can perform object/food recognition out-of-the-box). +You will also need API keys for: -## Getting Started: Git & Submission Workflow +- [Google Gemini API](https://ai.google.dev/) +- [Edamam Nutrition Analysis API](https://www.edamam.com/) -We will be using GitHub for version control and submission. Please follow these steps carefully. +### 2. Clone the Repository -**This Repository (Main Project):** `https://github.com/WeCode-Community-Dev/foodsnap-ai` +```bash +git clone https://github.com/Rahul-R79/foodsnap-ai.git +cd foodsnap-ai +``` -### 1. Fork the Repository -* Go to the main project repository: `https://github.com/WeCode-Community-Dev/foodsnap-ai` -* In the top-right corner of the page, click the "**Fork**" button. -* This will create a copy of the repository under your own GitHub account (e.g., `https://github.com/YOUR_USERNAME/foodsnap-ai`). This is *your* personal remote copy. +### 3. Backend Setup -### 2. Clone Your Forked Repository -* On your GitHub page for *your forked repository* (`https://github.com/YOUR_USERNAME/foodsnap-ai`), click the green "**Code**" button. -* Copy the HTTPS or SSH URL. -* Open your terminal or Git client and run: +1. Navigate to the backend directory: ```bash - git clone https://github.com/YOUR_USERNAME/foodsnap-ai.git - cd foodsnap-ai + cd Backend ``` - (Replace `YOUR_USERNAME` with your actual GitHub username.) - -### 3. Create a New Branch -* It's crucial to work on a new branch rather than directly on `main` or `master`. -* Choose a descriptive branch name, for example, `feature/your-team-name` or `solution-john-doe`. -* In your terminal, inside the `foodsnap-ai` directory, run: +2. Install dependencies: ```bash - git checkout -b feature/your-team-name + npm install ``` - (e.g., `git checkout -b feature/awesome-coders` or `git checkout -b solution-jane-doe`) -* You are now on your new branch. Verify by running `git branch`. - -### 4. Develop Your Project -* Start coding! Add your files, write your logic, and build your FoodSnap AI solution. -* Commit your changes frequently with clear commit messages: +3. Create a `.env` file in the `Backend` directory and add the following variables: + ```env + MONGO_URL=your_mongodb_connection_string + GEMINI_API_KEY=your_gemini_api_key + EDAMAM_APP_ID=your_edamam_app_id + EDAMAM_APP_KEY=your_edamam_app_key + PORT=8080 + ``` +4. Start the backend server: ```bash - # Stage all new and modified files - git add . - # Or stage specific files - # git add path/to/your/file.py path/to/another/file.js - - # Commit your changes - git commit -m "feat: Implement image upload functionality" - # Example commit types: feat, fix, docs, style, refactor, test, chore + npm run dev ``` + The server should start on `http://localhost:8080`. + +### 4. Frontend Setup -### 5. Push Your Branch to Your Fork -* When you're ready to save your progress to *your remote fork on GitHub*, push your branch: +1. Open a new terminal and navigate to the frontend directory: ```bash - git push origin feature/your-team-name + cd Frontend ``` - (Replace `feature/your-team-name` with your actual branch name.) -* If it's the first time pushing this branch, Git might suggest a command like `git push --set-upstream origin feature/your-team-name`. Use that command. - -### 6. Submit Your Project (Create a Pull Request) -* Once your project is complete (or at a submittable stage), go to *your* forked repository on GitHub (`https://github.com/YOUR_USERNAME/foodsnap-ai`). -* You should see a prompt saying "`feature/your-team-name` had recent pushes". Click the "**Compare & pull request**" button. -* If you don't see the prompt, go to the "**Pull requests**" tab and click "**New pull request**". -* **Crucially, ensure the settings are:** - * **Base repository:** `WeCode-Community-Dev/foodsnap-ai` - * **Base branch:** `main` (or `master`, whichever is the default for this repository) - * **Head repository:** `YOUR_USERNAME/foodsnap-ai` - * **Compare branch:** `feature/your-team-name` (your development branch) -* Write a clear title and a detailed description for your Pull Request (PR). Include: - * A brief overview of your solution. - * Technologies used. - * How to run/test your project (setup, commands, etc.). - * Any known issues or limitations. - * A link to a live demo if applicable (e.g., Heroku, Netlify, GitHub Pages). - * Screenshots or a short video showcasing your project can be very helpful! -* Click "**Create pull request**". - -## What to Include in Your Submission -*(In your project's directory, pushed to your branch and included in the PR)* - -* **Source Code:** All the code for your project. -* **`README.md` (Your Project's README):** This is very important! Your project's `README.md` (different from this main hackathon `README.md`) should include: - * Project Title & Team Name/Members (if applicable). - * A brief description of your FoodSnap AI solution. - * What features you implemented. - * Tech stack used. - * **Clear instructions on how to set up and run *your specific project* locally.** This includes dependencies, environment variables, build steps, and run commands. - * Any API keys or environment variables needed (explain how to get them, but **DO NOT COMMIT ACTUAL KEYS** to the repository). Use a `.env.example` file to show what's needed. - * Link to a live demo (if any). -* **(Optional but Recommended)** A short demo video or presentation slides (you can link these in your PR description or in your project's README). - -## Judging Criteria (General Pointers) - -While specific criteria might be announced, generally projects are evaluated on: - -* **Functionality:** Does it work as intended? Does it achieve the core goal of calorie estimation from an image? -* **Accuracy:** How close are the calorie/macro estimations? (We understand this is complex!) -* **Innovation & Creativity:** Did you come up with a unique approach or an interesting feature? -* **Technical Implementation:** Quality of code, choice of technology, and complexity handled. -* **User Experience (UX/UI):** If applicable, is the application easy and pleasant to use? -* **Presentation/Demo:** How well you explain and showcase your project. -* **Adherence to Submission Guidelines:** Including a good project `README.md` and following the Git workflow. - +2. Install dependencies: + ```bash + npm install + ``` +3. Create a `.env` file in the `Frontend` directory (if not already present, though Vite uses `.env` files for environment variables) or ensure your `src/config/axios.ts` points to the correct backend URL. Typically, for Vite: + ```env + VITE_API_URL=http://localhost:8080 + ``` +4. Start the frontend development server: + ```bash + npm run dev + ``` + The app will usually be available at `http://localhost:5173`. --- -Good luck, innovators! We can't wait to see your FoodSnap AI creations. Remember to have fun, learn, and collaborate! +## 📖 Usage + +1. Open the application in your browser. +2. Upload or take a photo of your food. +3. Wait for the AI to analyze the image. +4. View the identified food name, estimated quantity, and nutritional breakdown. + -**Happy Hacking!** -The WeCode Community Dev Team