This code generator creates FastAPI app from an openapi file.

企业级 FastAPI 项目生成器。基于 Cookiecutter，一键创建包含 JWT 认证、RBAC 权限、三层架构的生产就绪 FastAPI 项目。Enterprise FastAPI project generator. One-click creation of production-ready FastAPI projects with JWT auth, RBAC, and clean architecture via Cookiecutter.

Create a Video Membership app using FastAPI & NoSQL

In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

A solution template for creating a Single Page App (SPA) with React and FastAPI following the principles of Clean Architecture.

A FastAPI CLI & Streamlit App wrapper for Excel files... create APIs from Excel data files within seconds

Cross-platform tool for creating custom game icons, borders, and covers for the iiSU Network. Desktop app (Python/PySide6), Android app (Kotlin), and a community Workshop server (FastAPI) for uploading, browsing, and sharing game artwork.

🚀 SuperMCP - Create multiple isolated MCP servers using a single connector. Build powerful Model Context Protocol integrations for databases (PostgreSQL, MSSQL) with FastAPI backend, React dashboard, and token-based auth. Perfect for multi-tenant apps and AI assistants.

A Web app stack written in FastAPI, Qdrant, and React for creating AI projects

A cookiecutter template for creating a custom Web app that combines SvelteKit and FastAPI

This repository will take you through creating a FastAPI StableDiffusion app (including Dockerfile) all the way to adding a new feature using industry standard branch development!

Chatbot app for creating charts with React, FastAPI, and OpenAI

FastAPI Chat App with WebSockets is an open-source real-time chat application built on the FastAPI framework. This project provides a robust foundation for creating modern and secure chat applications with features such as WebSocket communication, user authentication, private messaging, and more.

Pricefy app is used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

A command line app for creating Backends with FastAPI, inspired in Artisan from Laravel and manage.py from Django.

Create a serverless API using FastAPI, deployed on AWS App Runner and powered by Neon Postgres

Create you first ChatGPT App using MCP, Apps SDK, and Python (FastMCP and FastAPI)

AI Mobile App created with React Native and FastAPI, implements ChatGPT to identify what is in the picture taken, and provides the average price of the food item.

Python web app that allows users to upload their own photos and create unique composite images. Built with FastAPI, Wand, and Canvas API.

Python FastAPI implementation for creating a basic CRUD API with JWT authentication. It also provides API access based on role (User|Admin). This app also includes the implementation of two factor authentication, which allows users to login with a one-time pin rather than a password.

Diabetes Predictor App used to predict whether a person has diabetes or not based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

SMS Spam Classifier app is used to predict if the given input message is spam or not created using python's scikit-learn, fastapi, pandas, nltk and joblib packages.

Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt pip3 install -r api/requirements.txt Install Tensorflow Serving (Setup instructions) Setup for ReactJS Install Nodejs (Setup instructions) Install NPM (Setup instructions) Install dependencies cd frontend npm install --from-lock-json npm audit fix Copy .env.example as .env. Change API url in .env. Setup for React-Native app Initial setup for React-Native app(Setup instructions) Install dependencies cd mobile-app yarn install cd ios && pod install && cd ../ Copy .env.example as .env. Change API url in .env. Training the Model Download the data from kaggle. Only keep folders related to Potatoes. Run Jupyter Notebook in Browser. jupyter notebook Open training/potato-disease-training.ipynb in Jupyter Notebook. In cell #2, update the path to dataset. Run all the Cells one by one. Copy the model generated and save it with the version number in the models folder. Running the API Using FastAPI Get inside api folder cd api Run the FastAPI Server using uvicorn uvicorn main:app --reload --host 0.0.0.0 Your API is now running at 0.0.0.0:8000 Using FastAPI & TF Serve Get inside api folder cd api Copy the models.config.example as models.config and update the paths in file. Run the TF Serve (Update config file path below) docker run -t --rm -p 8501:8501 -v C:/Code/potato-disease-classification:/potato-disease-classification tensorflow/serving --rest_api_port=8501 --model_config_file=/potato-disease-classification/models.config Run the FastAPI Server using uvicorn For this you can directly run it from your main.py or main-tf-serving.py using pycharm run option (as shown in the video tutorial) OR you can run it from command prompt as shown below, uvicorn main-tf-serving:app --reload --host 0.0.0.0 Your API is now running at 0.0.0.0:8000 Running the Frontend Get inside api folder cd frontend Copy the .env.example as .env and update REACT_APP_API_URL to API URL if needed. Run the frontend npm run start Running the app Get inside mobile-app folder cd mobile-app Copy the .env.example as .env and update URL to API URL if needed. Run the app (android/iOS) npm run android or npm run ios Creating the TF Lite Model Run Jupyter Notebook in Browser. jupyter notebook Open training/tf-lite-converter.ipynb in Jupyter Notebook. In cell #2, update the path to dataset. Run all the Cells one by one. Model would be saved in tf-lite-models folder. Deploying the TF Lite on GCP Create a GCP account. Create a Project on GCP (Keep note of the project id). Create a GCP bucket. Upload the tf-lite model generate in the bucket in the path models/potato-model.tflite. Install Google Cloud SDK (Setup instructions). Authenticate with Google Cloud SDK. gcloud auth login Run the deployment script. cd gcp gcloud functions deploy predict_lite --runtime python38 --trigger-http --memory 512 --project project_id Your model is now deployed. Use Postman to test the GCF using the Trigger URL. Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions Deploying the TF Model (.h5) on GCP Create a GCP account. Create a Project on GCP (Keep note of the project id). Create a GCP bucket. Upload the tf .h5 model generate in the bucket in the path models/potato-model.h5. Install Google Cloud SDK (Setup instructions). Authenticate with Google Cloud SDK. gcloud auth login Run the deployment script. cd gcp gcloud functions deploy predict --runtime python38 --trigger-http --memory 512 --project project_id Your model is now deployed. Use Postman to test the GCF using the Trigger URL. Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions

Create FastAPI App - Production-Grade Boilerplate Generator

backend api for a basic social media app built using python fastAPI that allows users ro create an account, create ,update and delete posts while also liking them

An example to create a fastapi app which serves shortest route and k_shortest_routes using osmnx

An app created for those who simply want to track earnings/losses made while trading cryptocurrencies! Python, Anaconda, FastAPI, SQLite, SQLAlchemy, Alembic, Jinja2, HTML, CSS.

Rainfall Predictor App used to predict tomorrow's rainfall based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

A simple Task Management application built with FastAPI, HTMX, and Jinja2 templates. This app allows users to create, update, toggle, and delete todo items. It also incorporates modern styling for a simple, clean and visually appealing user interface.

Todo App is a simple, efficient, and lightweight task management application built using the FastAPI framework. It allows users to create, read, update, and delete tasks in a to-do list, making it easy to manage daily tasks or project to-do items.