| import streamlit as st | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| import numpy as np | |
| from PIL import Image | |
| st.write("Loading model...") | |
| module = hub.load("https://tfhub.dev/google/progan-128/1") | |
| def generate_random_face(): | |
| latent_vector = np.random.normal(size=[1,512]).astype(np.float32) | |
| image = module.signatures['default'](tf.constant(latent_vector))['default'] | |
| image = np.uint8(image.numpy()[0]*255) | |
| return Image.fromarray(image) | |
| st.title("Random Face Generator") | |
| st.write("Click the button below to generate a random AI generated face") | |
| if st.button("Generate Random Face"): | |
| generated_image = generate_random_face() | |
| st.image(generated_image, caption="Generated Face", use_container_width=True) | |
| image_array = np.array(generated_image) | |
| txt_filename = "generated_face.txt" | |
| np.savetxt(txt_filename,image_array.flatten(), fmt="%d") | |
| st.download_button("Download Image as TXT", txt_filename, txt_filename, "text/plain") | |
| st.sidebar.write("App Version: 1.0") | |
| st.sidebar.write("Model: ProGAN-128 from Tensorflow Hub") |