Update app.py

app.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow import keras
+from tensorflow.keras import layers
+from keras.datasets import mnist
+
+# Function to display image, latent representation, and reconstructed image
+def display_reconstruction(index, autoencoder, encoder, x_test):
+    original = x_test[index]
+    latent_repr = encoder.predict(np.expand_dims(original, 0))[0]
+    reconstructed = autoencoder.predict(np.expand_dims(original, 0))[0]
+
+    fig, axs = plt.subplots(1, 3, figsize=(12, 4))
+
+    # Display original image
+    axs[0].imshow(np.reshape(original, (28, 28)), cmap='gray')
+    axs[0].set_title('Original Image')
+
+    # Display latent representation as a bar chart
+    axs[1].bar(range(len(latent_repr)), latent_repr)
+    axs[1].set_title('Latent Representation')
+
+    # Display reconstructed image
+    axs[2].imshow(np.reshape(reconstructed, (28, 28)), cmap='gray')
+    axs[2].set_title('Reconstructed Image')
+
+    for ax in axs:
+        ax.axis('off')
+
+    st.pyplot(fig)
+
+# Main Streamlit app
+st.title("Autoencoder Training and Visualization")
+
+# Button to trigger training
+if st.button("Train Autoencoder"):
+    # Load and preprocess data
+    (x_train, _), (x_test, _) = mnist.load_data()
+
+    x_train = x_train.astype('float32') / 255.0
+    x_test = x_test.astype('float32') / 255.0
+
+    x_train = np.reshape(x_train, (-1, 784))  # Flatten to (None, 784)
+    x_test = np.reshape(x_test, (-1, 784))
+
+    # Define autoencoder architecture
+    input_img = keras.Input(shape=(784,))
+
+    encoded = layers.Dense(128, activation='relu')(input_img)
+    encoded = layers.Dense(64, activation='relu')(encoded)
+    latent_vector = layers.Dense(32, activation='relu')(encoded)
+
+    decoded = layers.Dense(64, activation='relu')(latent_vector)
+    decoded = layers.Dense(128, activation='relu')(decoded)
+    decoded = layers.Dense(784, activation='sigmoid')(decoded)
+
+    autoencoder = keras.Model(input_img, decoded)
+    autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
+
+    # Train the autoencoder and display progress
+    with st.spinner("Training in progress..."):
+        autoencoder.fit(x_train, x_train, epochs=5, batch_size=128, validation_data=(x_test, x_test))
+
+    # Create encoder model
+    encoder = keras.Model(input_img, latent_vector)
+
+    # Input for image index to display
+    test_index = st.number_input("Enter an index (0-9999) to view an image from the test set:", min_value=0, max_value=9999)
+
+    # Button to display the reconstruction
+    if st.button("Display Reconstruction"):
+        display_reconstruction(test_index, autoencoder, encoder, x_test)