app.py

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

# Define the root directory
model = tf.keras.models.load_model("gender_resnet50.h5")

def predict_gender(image):
    image = image.resize((224, 224))
    image = tf.keras.utils.img_to_array(image)
    image = image / 255.0
    pred_arr = np.expand_dims(image, axis=0)
    result = model.predict(pred_arr)
    prob = result[0]
    text_res = "Male" if prob >= 0.5 else "Female"
    return text_res

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

# Launch the Gradio interface
interface.launch(share=True)