app.py

import gradio as gr
import numpy as np
import tensorflow as tf
from PIL import Image
import io

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

# Define the prediction function
def predict_gender(img_data):
    # Convert input image to PIL Image
    img = Image.open(io.BytesIO(img_data))
    
    # Resize and preprocess the image
    img = img.resize((150, 150))
    img = np.array(img)
    img = np.expand_dims(img, axis=0)
    img = img / 255.0
    
    # Predict gender
    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.Image(), outputs="text")
iface.launch()