app.py

import gradio as gr
import numpy as np
import tensorflow as tf
import cv2

# Load your trained model
#model = tf.keras.models.load_model('path_to_your_model.h5')

def predict_gender(image):
    # Convert image to format expected by your model & preprocess
    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    img = cv2.resize(img, (224, 224))  # Example size
    img = img / 255.0  # Normalizing
    img = np.expand_dims(img, axis=0)

    prediction = model.predict(img)

    # Assuming binary classification with a single output neuron
    return "Male" if prediction[0] < 0.5 else "Female"

# Define Gradio interface
iface = gr.Interface(
    fn=predict_gender,
    inputs=gr.inputs.Image(type="webcam", label="Capture an Image from Webcam"),
    outputs=gr.outputs.Label(),
    live=True
)

iface.launch()