Create 7_mnist.py

pages/7_mnist.py

@@ -0,0 +1,121 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+
+def load_and_preprocess_mnist():
+    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+    
+    x_train = x_train.astype('float32') / 255.0
+    x_test = x_test.astype('float32') / 255.0
+    
+    x_train = x_train.reshape((-1, 28, 28, 1))
+    x_test = x_test.reshape((-1, 28, 28, 1))
+    
+    y_train = keras.utils.to_categorical(y_train, 10)
+    y_test = keras.utils.to_categorical(y_test, 10)
+    
+    return (x_train, y_train), (x_test, y_test)
+
+def create_mnist_model():
+    model = keras.Sequential([
+        keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),
+        keras.layers.MaxPooling2D(pool_size=(2, 2)),
+        keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu'),
+        keras.layers.MaxPooling2D(pool_size=(2, 2)),
+        keras.layers.Flatten(),
+        keras.layers.Dropout(0.5),
+        keras.layers.Dense(64, activation='relu'),
+        keras.layers.Dense(10, activation='softmax')
+    ])
+    
+    model.compile(optimizer='adam',
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+    
+    return model
+
+def train_model(model, x_train, y_train, epochs, batch_size):
+    history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+    return history
+
+def plot_training_history(history):
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
+    
+    ax1.plot(history.history['accuracy'], label='Training Accuracy')
+    ax1.plot(history.history['val_accuracy'], label='Validation Accuracy')
+    ax1.set_title('Model Accuracy')
+    ax1.set_xlabel('Epoch')
+    ax1.set_ylabel('Accuracy')
+    ax1.legend()
+    
+    ax2.plot(history.history['loss'], label='Training Loss')
+    ax2.plot(history.history['val_loss'], label='Validation Loss')
+    ax2.set_title('Model Loss')
+    ax2.set_xlabel('Epoch')
+    ax2.set_ylabel('Loss')
+    ax2.legend()
+    
+    return fig
+
+def main():
+    st.title("MNIST Digit Classification with Keras and Streamlit")
+    
+    # Load and preprocess data
+    (x_train, y_train), (x_test, y_test) = load_and_preprocess_mnist()
+    
+    # Create model
+    if 'model' not in st.session_state:
+        st.session_state.model = create_mnist_model()
+    
+    # Sidebar for training parameters
+    st.sidebar.header("Training Parameters")
+    epochs = st.sidebar.slider("Number of Epochs", min_value=1, max_value=50, value=10)
+    batch_size = st.sidebar.selectbox("Batch Size", options=[32, 64, 128, 256], index=2)
+    
+    # Train model button
+    if st.sidebar.button("Train Model"):
+        with st.spinner("Training in progress..."):
+            history = train_model(st.session_state.model, x_train, y_train, epochs, batch_size)
+        st.success("Training completed!")
+        
+        # Plot training history
+        st.subheader("Training History")
+        fig = plot_training_history(history)
+        st.pyplot(fig)
+        
+        # Evaluate model
+        test_loss, test_acc = st.session_state.model.evaluate(x_test, y_test)
+        st.write(f"Test accuracy: {test_acc:.4f}")
+        
+        # Set a flag to indicate the model has been trained
+        st.session_state.model_trained = True
+    
+    # Test on random image
+    st.subheader("Test on Random Image")
+    if st.button("Select Random Image"):
+        if not hasattr(st.session_state, 'model_trained'):
+            st.warning("Please train the model first before testing.")
+        else:
+            # Select a random image from the test set
+            idx = np.random.randint(0, x_test.shape[0])
+            image = x_test[idx]
+            true_label = np.argmax(y_test[idx])
+            
+            # Make prediction
+            prediction = st.session_state.model.predict(image[np.newaxis, ...])[0]
+            predicted_label = np.argmax(prediction)
+            
+            # Display image and prediction
+            fig, ax = plt.subplots()
+            ax.imshow(image.reshape(28, 28), cmap='gray')
+            ax.axis('off')
+            st.pyplot(fig)
+            
+            st.write(f"True Label: {true_label}")
+            st.write(f"Predicted Label: {predicted_label}")
+            st.write(f"Confidence: {prediction[predicted_label]:.4f}")
+
+if __name__ == "__main__":
+    main()