This is a simple application that uses a pre-trained model to classify images as either "Smoking" or "Not Smoking". The application provides a graphical user interface (GUI) that allows users to select an image and scan it for smoking detection.

• Python 3.x
• tkinter
• PIL
• numpy
• tensorflow
• tensorflow_hub

1. Clone the repository or download the code files.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the application using python smoking_detector.py.
4. The application window will open with a title, image label, select image button, scan button, and result label.
5. Click the Select Image button to choose an image file (supports PNG, JPG, JPEG, and GIF formats).
6. After selecting an image, it will be displayed in the image label.
7. Click the SCAN button to initiate the smoking detection process.
8. The result label will display the prediction result, indicating whether the image contains smoking or not.

• The code uses the tkinter library to create the GUI elements.
• The PIL library is used for image processing and displaying images in the GUI.
• The numpy library is used for array manipulation.
• The tensorflow and tensorflow_hub libraries are used for loading the pre-trained smoking detection model.
• The application is implemented as a class called SmokingDetector.
• The class constructor sets up the GUI elements and loads the pre-trained model.
• The select_image method is responsible for opening a file dialog to select an image file and displaying the selected image in the GUI.
• The predict_image method processes the selected image and performs smoking detection using the pre-trained model. The result is displayed in the GUI.
• The run method starts the main event loop of the GUI.
• The __main__ block creates an instance of the SmokingDetector class and runs the application.

This project is licensed under the MIT License.