Flower Image Classification

This is a simple project that uses a convolutional neural network (CNN) to classify images of flowers into different categories. The project consists of two Python files:

• model_trainer.py: This file contains the code to train the CNN model using the flower images.
• app.py: This file contains a GUI application that allows the user to select an image of a flower and get a prediction of the flower's category.

Dataset

The dataset used in this project contains images of five different types of flowers:

• Daisy
• Dandelion
• Rose
• Sunflower
• Tulip

The images are stored in the flowers directory, which is organized into subdirectories for each flower type.

Training the Model

To train the model, simply run the model_trainer.py file. The script will load the images from the flowers directory, preprocess them, and train the CNN model using TensorFlow. The trained model will be saved to a file named flower_classifier.

Running the Application

To run the GUI application, simply run the app.py file. The application will open a window that allows the user to select an image of a flower. Once the user selects an image, the application will preprocess the image, make a prediction using the trained CNN model, and display the predicted flower category.

Requirements

This project requires the following Python packages:

• TensorFlow
• Pillow
• tkinter

To install these packages, you can use pip:

pip install tensorflow pillow tkinter

Conclusion

This is a simple project that demonstrates how to use a CNN to classify images of flowers. The project can be easily extended to include other types of images or to improve the accuracy of the model.