Chuck Norris World App - A sample app that shows how to add authentication to a ReactJS app

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

ReactJS + Nodejs + React Router V4 user authentication, based on the code from tutorial: https://vladimirponomarev.com/blog/authentication-in-react-apps-jwt, and the

Creating a blog application for Firebase and ReactJs integration tutorial. It also has a Authentication service thatprovided by Firebase.

Firebase authentication system using phone OTP, HTML sections using React+Vite, a login page with Firebase OAuth, a message board application with Firestore, and a ReactJS tutorial website.

Discover how to build and deploy a Full Stack Food Ordering App using Next.js! In this tutorial, we dive into React.js, Tailwind CSS, Clerk for authentication, and HyGraph for enhanced functionality. Perfect for developers looking to create robust web applications. Don't miss out, watch now! #nextjs #ReactJs #TailwindCSS #FullStackTutorial#nextjs

reactjs authentication tutorial

https://auth0.com/blog/reactjs-authentication-tutorial/

Trying out https://auth0.com/blog/reactjs-authentication-tutorial/

A project based on a React authentication tutorial using Auth0 | https://auth0.com/blog/reactjs-authentication-tutorial/

CS course scheduler from CS397 React Tutorial. Deployed using Firebase and built from ReactJs. Includes elements of authentication, version control, code modularization, and databases.

Full-stack application focussed on JWT authentication, using ReactJs in frontend and NodeJs in the backend, with GraphQL, Apollo Client, and Postgres as database complimenting the project. (With reference to Ben Awad's youtube tutorial.)

this is a tic-tac-toe similar to facebooks tutorial for ReactJs. This is made on ruby-hyperloop which is an isomorphic web framework. I added user authentication with Devise and used psql as database for scoring and saving the highscores.