Build an AI-powered cooking assistant with Flask. Learn backend design, SQLAlchemy, and RESTful APIs for recipes and reviews. Use OpenAI to generate recipes, extract from HTML with prompt engineering, add Text-to-Speech, and automate workflows with Python.
Build a solid foundation for your AI cooking assistant with a modular Flask app. Learn the app factory pattern, Blueprints, and SQLAlchemy models for recipes, ingredients, and reviews. Set up SQLite, add CLI tools for DB management, and create a clean, scalable, production-ready backend.
Building the Recipe API: Retrieval, Search, and Filter
4 lessons
Course 3
Recipe Generation & Extraction with AI
5 lessons
Course 4
Final Additions to our Smart Cooking API
3 lessons
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22 practices
Build essential API endpoints to make your app interactive and data-driven. Retrieve single, random, or popular recipes, browse with pagination, and filter by ingredients. Add serialization, error handling, and a script to remove duplicates, turning your backend into a dynamic, queryable service.
Add intelligence to your app using OpenAI’s LLMs. Learn to generate structured recipes from ingredients with prompt engineering, render dynamic prompts, call the API, and process outputs. Build a script to extract recipes from messy HTML and store them cleanly. Your app will now auto-generate and parse recipes.
Add final touches to your app for better interactivity. Use gTTS for Text-to-Speech recipe playback, let users submit reviews and ratings, and list all ingredients. Build a script to export recipes to CSV. These features make your assistant ready for real users seeking culinary inspiration.
