This is a Python-based application that uses the KivyMD framework to predict the age and gender of a person from an image. The app integrates a pre-trained machine learning model for age and gender prediction.

1. Make sure to install the kivy (necessary libraries)

pip install kivy kivymd

2. Create a KivyMD app: You'll need to create a KivyMD app that captures user input (e.g., an image for age and gender prediction) and displays the results.

3. Add UI elements: In the KivyMD app, add the necessary UI elements like buttons, labels, and file pickers to interact with the user and get input.

4. Load the model: Integrate your age and gender prediction model into the app. Depending on your model format (TensorFlow, PyTorch, etc.), you'll need to load the model and prepare it for inference.

5. Perform inference: When the user provides an image through the UI, use your model to perform age and gender prediction on that image.

6. Display the results: Show the predicted age and gender to the user, either in labels or as pop-up dialogs.

As I don't have access to your specific model code, I can provide you with an outline of how to do these steps in KivyMD. However, please keep in mind that the implementation details might differ based on your specific model and how you've trained it.

Here's a basic code outline to get you started: refer to main.py

• Load an image from the device's storage.
• Predict the age and gender of the person in the selected image.
• Display the predicted age and gender on the screen.

• Python 3.6 or higher
• Kivy and KivyMD installed (use pip install kivy kivymd to install the dependencies)

1. Clone the repository:

git clone https://github.com/your-username/age_gender_prediction_app.git

2. Change into the project directory:

cd age_gender_prediction_app

1. Run the app:

python main.py

2. The application window will open, and you will see an image placeholder and a "Browse Image" button.

3. Click on the "Browse Image" button to select an image from your device's storage.

4. After selecting the image, the app will process it using the pre-trained model to predict the age and gender of the person.

5. The predicted age and gender will be displayed on the screen below the image.

If you want to use your own pre-trained model, follow these steps:

1. Replace the example prediction code in main.py with your own model inference code. Modify the predict_age_gender() function to load your model and perform the prediction.

2. Ensure your model takes an image as input and outputs the predicted age and gender.

• Enhance the UI for a more visually appealing experience.
• Support real-time image capture using the device's camera.
• Add more advanced features for facial analysis (e.g., emotion recognition).
• Optimize the app's performance and memory usage.

Contributions are welcome! If you have any ideas for improvements or find any issues, please open an issue or a pull request.

If you have any questions or need further assistance, you can reach out here 👉 email and please dont forget to star 😅