Create app.py

app.py

@@ -0,0 +1,20 @@
+import streamlit as st
+import tensorflow as tf 
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('HumanFaceGenerator.h5')
+
+# Create a button
+button_clicked = st.button("Generate")
+
+# Check if the button is clicked
+if button_clicked:
+    # Generate an image
+    seed = tf.random.normal((1, 100))
+    pred = model.predict(seed)
+    pred = pred * 0.5 + 0.5  # Normalize the pixel values
+    pred = np.squeeze(pred)  # Remove singleton dimensions if any
+    
+    # Display the generated image
+    st.image(pred, caption='Generated Image', use_column_width=True)