Base FastAPI Project to create general api.

Create a new FastAPI project in one command

CLI to create Fastapi projects easily.

PDF summarizer that leverages GPT AI to generate summaries from uploaded PDF files. The application uses FastAPI for the backend and Streamlit for the frontend. The project was created with the assistance of AI language models.

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

Example project created by fastapi-mvc from fastapi-mvc/copier-project template

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

This is a simple boilerplate MVC like project to create quickly application with great components (Fastapi, Prisma, HTMX...)

This project was built as a result of a deepening of the studies discussed on the blog farlley.com with a greater focus on Domain Driven Design (DDD) architecture. In this work you will find a simple template for creating microservices, as well as a use case (which will still be implemented according to the Roadmap found in this same document) applying the theories and studies presented in the books Implementing Domain-Driven Design - Vaughn Vernon, Domain-Driven Design: Attacking the Complexities at the Heart of Software - Eric Evans, and Building Microservices: Designing Fine-Grained Systems - Sam Newman, the bibliography consulted will be added to the end of this document in accordance with the application roadmap. The template was written for the python language in its version 3.9.0, using the FastApi framework.

Cookiecutter template to create a new FastAPI project

This project provides a FastAPI application to create and update GitHub gists using the GitHub API. It includes SQLite for persistence and is designed to run in a GitHub Codespace.

Skeleton cookiecutter based structure for easily creating base structure for fastapi and database projects.

This project is a collection of Python-based REST APIs built using FastAPI and other relevant Python libraries. The goal of this repository is to practice and experiment with creating, testing, and deploying APIs, with a focus on building real-world, scalable web services.

This project is a powerful and user-friendly solution for removing backgrounds from photos. It utilizes the "rembg" Python library combined with FastAPI to build a robust API that can seamlessly remove backgrounds from images. The front-end is developed using React to create an intuitive user interface for uploading and processing images.

A quickstart tool for creating a FastAPI project with Jinja2, TailwindCSS, Flowbite, HTMX, and AlpineJS.

A small api project with fastapi to try creating a full async http request lifecycle with python

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.

✍️ A simple and professional Blog API built with FastAPI, SQLite, and Pydantic. This project demonstrates how to create, list, and delete blog posts while applying data validation, path & query parameters, and database persistence.

This project combines Azure AI Search, Azure OpenAI Service, LangChain, React.JS, and Python FastAPI to create an intelligent system for managing Jira issues. It features advanced AI search for seamless document retrieval, a user-friendly React.JS front-end, and a robust Python FastAPI back-end.

This project provides a production-grade API to create and manage tokens on the Solana blockchain using the Pump.fun platform. Built with FastAPI, it offers endpoints for creating tokens, buying tokens, and performing both actions together.

fastapi-sqlmodel-crud is a project based on FastAPI+SQLModel, which is used to quickly build Create, Read, Update, Delete common API interfaces.

A template to quickly bootstrap FastAPI projects, to create a backend in Python.

Python project using FastAPI and Deta to create an API that returns the grammatical gender of German nouns.

This project is a deliberately vulnerable FastAPI application created to demonstrate common web vulnerabilities and how to perform remediation to mitigate them.

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

This project provides a simple structure for creating a FastAPI-based web application for managing books, along with Alembic for database migrations. It is designed with beginners in mind and follows a structured folder organization.

This project allows movie enthusiasts to create and share their personal movie-watching experiences. Users can showcase their watched movies, write reviews, and explore the viewing histories of others. Built with the FastAPI framework and utilizing MongoDB for data storage.

This is a beginner-friendly project built using FastAPI and SQLite that demonstrates how to perform full CRUD operations (Create, Read, Update, Delete) on user data. The project follows a modular structure with routers, models, and schemas separated for clean and scalable code. It uses SQLAlchemy ORM for interacting with the SQLite database.

A template project combining Tauri, Python (TensorFlow), FastAPI, and Svelte to create lightweight desktop applications with integrated machine learning capabilities.

A FastAPI project AI based to create a voice and whatsapp agent in the transportation domain