y# Dishcovery: AI-Powered Recipe Suggestion App 🍽️

Dishcovery is an innovative recipe suggestion platform where users can upload images of ingredients (fruits, vegetables, etc.) along with dietary preferences to generate personalized recipes. Powered by cutting-edge AI and robust backend systems, Dishcovery creates detailed recipes and saves them for users to browse, share, and enjoy.

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Features

• 📷 Ingredient Recognition: Upload photos of ingredients for analysis using AI vision models (Llama Vision from meta-llama/Llama-Vision-Free).
• 🧑‍🍳 Personalized Recipes: Generate recipes tailored to dietary preferences and restrictions.
• 📊 Detailed Recipe Information: Includes cooking steps, ingredients, calories, cooking time, servings, and dietary suitability.
• 🖼️ Dish Gallery: A global showcase of dishes generated by users, viewable in a card-based layout.
• 🛠️ Dynamic Dish View: Click on a dish card to see detailed information about that dish.
• 🔁 Caching with Redis: Optimized performance for dish gallery and recipe fetching.
• 🔒 User Authentication: Login functionality for users (future plans include a profile page to manage dishes).
• 🔗 Shareable Content: Dynamic thumbnails and metadata (planned).
• 🚀 Scalable Architecture: Optimized for high performance with caching and rate limiting.

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Tech Stack

Frontend

• Framework: Next.js 14 (App Router)
• Styling: Tailwind CSS and Shadcn Components
• Global State Management: Custom React hooks and Zustand

Backend

• Database: PostgreSQL with Drizzle ORM
• Caching: Redis via Upstash
• AI Vision Models: Llama Vision from meta-llama/Llama-Vision-Free (by Together.ai)
• Authentication: NextAuth.js

Package Manager

• Bun: For ultra-fast builds and dependency management (Why Bun?).

Deployment

• Frontend & Server: Vercel
• Database: Cloud-hosted PostgreSQL
• Caching: Upstash Redis for serverless caching

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How It Works

1. Upload: Users upload an image of ingredients and specify dietary preferences.
2. AI Analysis: The image is analyzed by AI, and a recipe prompt is generated.
3. Recipe Creation: AI returns a detailed recipe, which is saved to the PostgreSQL database.
4. Gallery Display: Recipes are cached in Redis for efficient loading and displayed in a gallery.
5. Interactive View: Click on any dish in the gallery to view its full details.

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Setup Instructions

Clone this repository

bun create itsOmSarraf/dishcovery-nextjs
cd dishcovery-nextjs

Install dependencies

bun install

Configure environment variables

• Copy the .env.example

Run the development server

bun dev

Access the app

Visit http://localhost:3000.

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Contributing

We welcome contributions to make Dishcovery even better! Here's how you can contribute:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add your message here"

4. Push to the branch:

   git push origin feature-name

5. Open a Pull Request.

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Roadmap

• 🚧 User Profile Pages: Allow users to view and manage their generated dishes.
• 📤 Social Sharing: Add dynamic thumbnails and metadata for sharing.
• 🕒 Rate Limiting: Implement per-user rate limiting for uploads.
• 🌐 Global Caching: Optimize gallery performance with Redis caching.
• 🔍 Search & Filter: Add functionality to search and filter dishes by cuisine, time, or dietary restrictions.

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License

This project is open-source and available under the MIT License.