Flower Image Classifier App: classify

Overview

The Flower Image Classifier App allows users to identify different species of flowers by either selecting an image from their device's gallery or by capturing a new photo. The app leverages a machine learning model trained using PyTorch and optimized for mobile deployment via TensorFlow Lite.

Technologies Used

• PyTorch: For building and training the image classification model.
• TensorFlow: For converting the PyTorch model to TensorFlow Lite format.
• Flask: For creating the backend service to handle image uploads and inference.
• React Native & Expo: For building the cross-platform mobile app.
• ngrok: To expose the local Flask server to the internet, making it accessible from the React Native app.

Getting Started

Requirements

• Python 3.8+
• Node.js and npm
• Expo CLI
• Flask
• PyTorch and TensorFlow
• ngrok

Installation

1. Backend Setup:

   • DO THIS BACKEND FIRST
   • LINK TO BACKEND REPOSITORY: https://github.com/akaul823/hostedModel/tree/main

2. FRONTEND: For the frontend, we will copy and paste that URL from above to line 112 at the fetch request. Now we can start the app.

   npm install
   npx expo start --tunnel

   After this, a barcode and set of options will show up. You can use your phone's camera to scan the barcode, and the app will load on your phone through Expo Go.

Usage

Scan the QR code with the Expo Go app on your mobile device.

Demo

Click the image above to watch the demo video.

System Architecture

1. Model Architecture: The image classification model was built using PyTorch's pre-trained image classifier model and fine-tuned for flower species identification.

2. Conversion to TFLite: After training, the model was converted into the TFLite format using TensorFlow's converter.

3. Backend Server: The Flask server accepts captured photos and image uploads from the React Native app, runs inference using the TFLite model; a number is returned, which is then mapped onto a list of labels and then sends the predicted flower species back to the app.

4. React Native App: Built using React Native and Expo, the app provides an intuitive interface for the user to either capture a new photo or choose an existing one. Once the image is selected, it's sent to the Flask server for prediction.

Contributing

If you'd like to contribute, please fork the repository and make changes as you'd like. Pull requests are warmly welcomed.

Acknowledgements

• Big thanks to PyTorch and TensorFlow teams for making model development and conversion seamless.
• React Native and Expo for an intuitive app development experience.
• Flask for being a lightweight and powerful backend tool.
• Flatiron School for guiding me through this project

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