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4 courses
87 practices
11 hours
Design and build a smart, AI-powered cooking API with Django by modeling a scalable database, creating rich retrieval and search endpoints, generating structured recipes with AI, and adding features like reviews, text-to-speech, and data export for a polished backend experience.
Cooking App Structure & Database Modeling with Django
4 lessons
21 practices
Set up the core Django project and a dedicated recipes app, then model the domain your API will rely on (recipes, ingredients, and ratings). You’ll configure settings (SQLite, CORS, static files), register models in the admin, and add a couple of CLI scripts to seed data and safely wipe/reset tables for local development.
Building the API: Retrieval, Search, and Filter with Django
4 lessons
23 practices
Implement the first set of read-focused API endpoints for your recipe database. You’ll add consistent JSON serialization, pagination, and robust validation/error handling, then expand into recipe discovery features like filtering by ingredients, returning steps as an array, and selecting random or top-rated recipes.
Add an LLM layer to generate and extract structured recipes. You’ll create a lightweight prompt templating system, integrate OpenAI calls behind a small wrapper, and expose an endpoint that generates recipes from ingredients. You’ll also build a CLI workflow to extract a clean recipe from messy HTML and store it into your Django database.
Final Additions to our Smart Cooking API with Django
3 lessons
18 practices
Round out the backend with interactive features and utilities: generate Text-to-Speech audio for recipe playback, let users submit ratings, list available ingredients, and export stored recipes to CSV for portability. You’ll focus on practical API finishing touches and simple scripts that improve usability and maintainability.
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