aaxyat/tomatosorter repository overview

⁠The Ripe Tomato 🍅

This is a machine learning based web application that helps to predict whether a tomato is ripe or unripe. It utilizes a model trained on Keras and provides an easy-to-use interface for uploading a tomato image and receiving the prediction.

⁠Table of Contents

1. Getting Started⁠
2. Prerequisites⁠
3. Installation⁠
4. Contributors⁠

⁠Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

⁠Prerequisites

Ensure that you have the following installed on your local machine:

• Python 3.7+
• Flask
• Tensorflow 2.x
• Keras

⁠Installation

⁠Running with Docker

# Pull the Docker image:
docker pull aaxyat/tomatosorter

# Run the application using Docker:
docker run -d -p 5000:5000 aaxyat/tomatosorter

After running the above command, visit http://localhost:5000⁠ in your web browser.

⁠Direct Installation

Clone the repo:

git clone https://github.com/NonvegPanda/TheRipeTomato.git
cd TheRipeTomato

Create a new Python virtual environment:

python -m venv env

Activate the virtual environment:

On Windows:

.\env\Scripts\activate

On MacOS/Linux:

source env/bin/activate

Next, install the dependencies:

pip install -r requirements.txt

Before running the project, please ensure that long paths are enabled on Windows. To enable long paths, follow the steps below:

1. Open the Local Group Policy Editor (press Win + R, type gpedit.msc and hit Enter).
2. Navigate to: Computer Configuration > Administrative Templates > System > Filesystem > NTFS.
3. Double click the Enable NTFS long paths option and enable it.

If the Local Group Policy Editor is not available in your version of Windows, try this method:

1. Open the Registry Editor (press Win + R, type regedit and hit Enter).
2. Navigate to: HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem
3. Right-click the value LongPathsEnabled and select Modify.
4. Change Value data to 1 and click OK.

Start the Flask development server:

python app.py

Navigate to http://localhost:5000⁠ in your browser.

⁠Contributors

• Yubhaskar Baral⁠
• Ayush Bhattarai⁠

Your contributions are always welcome! Feel free to improve existing code, documentation or implement new features. Please open an issue to propose your changes if they are substantial. 😃

Made with ♥ by Yubhaskar Baral and Ayush Bhattarai