Update app.py

app.py

@@ -4,7 +4,14 @@ import matplotlib.pyplot as plt
 from tensorflow import keras
 from tensorflow.keras import layers
 from keras.datasets import mnist
-import cv2
+
+
+# Streamlit interface
+st.title("Autoencoder Visualization")
+st.subheader("Please train the autoencoder on MNIST train dataset")
+st.subheader("Test and observe decoding below")
+st.subheader("Developed by Dr. Hemprasad Yashwant Patil, SENSE, VIT Vellore")
+
 
 # Function to display image, latent representation, and reconstructed image
 def display_reconstruction(index, autoencoder, encoder, x_test):
@@ -86,8 +93,3 @@ if st.session_state.autoencoder:
     if st.button("Display Reconstruction"):
         display_reconstruction(test_index, st.session_state.autoencoder, st.session_state.encoder, st.session_state.x_test)
 
-architecture_image = cv2.imread("image1.png")
-
-architecture_image = cv2.cvtColor(architecture_image, cv2.COLOR_BGR2RGB)  # Convert to RGB for proper display
-
-st.image(architecture_image, caption="Autoencoder Architecture")