[AI] Create a Object Recognition Web App with Python & React

Python and Fast API form the backend foundation for our AI object recognition app, delivering image upload endpoints with fast, asynchronous handling and automatic documentation.

Install python on macOS using brew, the official installer, or pyenv, then verify with python3 to confirm the installed version, such as 3.10.0.

Install Python on Windows by downloading from the official Python website or using Microsoft Store; add Python to path, optionally disable the path length limit, then verify with Python --version.

Install and run a fastapi API server using python, set up a virtual environment, and create a basic get route with automatic swagger docs for testing.

Explore a complex items route in the object recognition web app by defining a get route with item IDs, returning items and handling 404 errors, then test in the browser.

Install uvicorn with pip and run uvicorn main:app --reload to enable an asynchronous ASGI server for scalable Python apps. Uvicorn offers production readiness, easy port config (8080), and browser docs.

Demonstrate using a requirements.txt to install all packages with pip, ensuring consistent versions for a team, including fast API, TensorFlow, numpy, OpenCV Python, and uvicorn.

Build modular, reusable components with React to create interactive, dynamic web interfaces. Pair with TypeScript to catch errors early and clarify code, strengthening the front end application.

Install Node.js to run JavaScript runtimes for your AI object recognition web app; download from nodejs.org for Windows or Mac, use the latest LTS 20, then verify with node --version.

Learn to scaffold a React front end with Vite, configure a TypeScript project, install dependencies, and run the dev server to view your app on localhost.

Create and style an image control component in a tsx front end by building ImageControl and importing index.css, then render it in App.tsx with container and inner container classes.

Set up image and selected file state with React useState, initializing to null. Create prediction, loading, and error states to manage backend requests and display results in the front end.

Recreate a basic front end that uploads and renders an image, then display the first prediction's category and accuracy in a box, with a prompt when no image is uploaded.

Demonstrate uploading an image via a file input that accepts all image types, handling onChange, and toggling a disabled or loading state until the image is identified.

Explore how TensorFlow powers an SSD MobileNet v2 object detector trained on the Coco dataset for real-time recognition in web apps.

Download the TensorFlow SSD MobileNet v2 Coco model from GitHub and extract the zip. Copy the folder into the server backend and pass the integrated model into the application.

Delete unused code, import the dataset, create the TensorFlow environment, and load the SSD MobileNet v2 coco saved model for the prediction model using the serving default signature.

Implement a run_inference function that prepares an image, converts it to a TensorFlow tensor, and runs object detection to return detections with bounding boxes, class labels, and confidence.

Define a fast api post route predict to accept an uploaded image, convert it to rgb numpy, run inference, and return a json response of detections for the frontend.

Add a label map linking object IDs to names from COCO dataset for SSD mobile, import the Python label_map module, and integrate it into the main app in VS Code.

Loop through detections, filter by score at least 0.5, map class IDs with the label map, collect boxes, categories, and scores, then return a JSON response for the front end.

Test the predict route by running uvicorn and uploading test images to verify model predictions with high accuracy, such as 98–99% for boats and cats.

Develop a TypeScript React hook to upload images via Axios, manage loading and error states, and return the backend prediction for use across components.

Define and export types for an object recognition app, including a prediction type with category and score, plus a use image upload result type with array, null, loading, and error.

Learn how to implement a post request with Axios in a try-catch block, handling multipart form data, server response, and prediction state in a React hook.

Use the image control hook to manage image uploads, predictions, loading, and error states within a React TSX component, wiring the hook and supplying the server key later.

Create a keys folder with api keys.txt, export the key, and import it into the app. Send it to /predict at 127.0.0.1:800, and use environment variables or logs for security.

Implement a handle image change function to process an HTML input change event, set the selected file and create an object URL, then implement handle upload to the server.

Start the app with npm run dev, install axios to fix the network error, upload an image to test identify image, and configure cross-origin requests to accept http calls.

Configure cross-origin resource sharing in the fast API server using CORSMiddleware, setting allow origins, credentials, methods, and headers, and define localhost URLs for react and angular front-ends.

Upload images on the front end to run object recognition with Python and React, displaying labels and a fixed percentage. Explore adding more images and refining back-end and front-end accuracy.

Split the front end into reusable, smaller components in a React-based object recognition web app, creating image and prediction components with props to streamline code and improve readability.

Pass data from parent to child using props to render components dynamically. Convert to functional components, import types, and pass category, score, and image url to fix errors.

Learn how image clarity, background noise, and kernel-based filtering can lead to misclassifications in object recognition, and practice with clearer images and larger models like v2 and v3.

Discover how object recognition apps identify hazards, aid inventory management, and describe environments in real time for visually impaired users, while noting limits in low light and privacy concerns.