Building an AI Cooking Helper with FastAPI | CodeSignal Learn

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Building an AI Cooking Helper with FastAPI

Software Engineering

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4 courses

90 practices

11 hours

Build a full AI-powered cooking assistant with FastAPI. Model recipes and ingredients, create robust APIs, integrate OpenAI for recipe generation, add text-to-speech and reviews, and export data for a complete smart cooking experience.

Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

API Integration, Design, & Development

INTERMEDIATE

Data Management and Database Querying

INTERMEDIATE

Server-Side Programming

Tools you'll use

FastAPI

Flask

OpenAI

Python

Course 1

Cooking App Structure & Database Modeling with FastAPI

4 lessons

22 practices

Build a solid foundation for your AI cooking assistant with a modular FastAPI app. Learn how to wire APIRouter, Pydantic schemas, and SQLAlchemy models for recipes, ingredients, and reviews. Set up SQLite, enable CORS, serve static files, and create a clean, scalable, production-ready backend.

Course 2

Building the API: Retrieval, Search, and Filter with FastAPI

4 lessons

25 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 keep responses typed with Pydantic models.

Course 3

AI Recipe Generation & Extraction with FastAPI

5 lessons

25 practices

Add intelligence to your app using OpenAI’s LLMs. 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.

Course 4

Final Additions to our Smart Cooking API with FastAPI

3 lessons

18 practices

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.

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