import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image

# Load the trained model
model = load_model('gender_classification_model.h5')

# Define the prediction function
def predict_gender(img):
    img = img.resize((150, 150))
    img = np.array(img)
    img = np.expand_dims(img, axis=0)
    img = img / 255.0
    prediction = model.predict(img)
    return "Male" if prediction[0] > 0.5 else "Female"

# Create the Gradio interface
iface = gr.Interface(fn=predict_gender, inputs=gr.inputs.Image(type='pil'), outputs="text")
iface.launch()