Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import classification_report
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.datasets import mnist
+import streamlit as st
+
+# Load the MNIST dataset
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+
+# Preprocess the data
+train_images = train_images.reshape((60000, 28, 28, 1)).astype("float32") / 255
+test_images = test_images.reshape((10000, 28, 28, 1)).astype("float32") / 255
+
+# Convert labels to categorical format
+train_labels = keras.utils.to_categorical(train_labels, 10)
+test_labels = keras.utils.to_categorical(test_labels, 10)
+
+# Define the CNN model
+def create_model():
+    model = keras.Sequential([
+        layers.Conv2D(32, (3, 3), activation="relu", input_shape=(28, 28, 1)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation="relu"),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation="relu"),
+        layers.Flatten(),
+        layers.Dense(64, activation="relu"),
+        layers.Dense(10, activation="softmax")
+    ])
+    
+    model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+    return model
+
+# Streamlit UI
+st.title("CNN for MNIST Classification")
+
+if st.button("Train Model"):
+    model = create_model()
+    with st.spinner("Training..."):
+        history = model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=10, batch_size=64)
+        
+    # Plot training loss and accuracy
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
+
+    ax1.plot(history.history["loss"], label="Train Loss")
+    ax1.plot(history.history["val_loss"], label="Val Loss")
+    ax1.set_title("Training and Validation Loss")
+    ax1.set_xlabel("Epoch")
+    ax1.set_ylabel("Loss")
+    ax1.legend()
+
+    ax2.plot(history.history["accuracy"], label="Train Accuracy")
+    ax2.plot(history.history["val_accuracy"], label="Val Accuracy")
+    ax2.set_title("Training and Validation Accuracy")
+    ax2.set_xlabel("Epoch")
+    ax2.set_ylabel("Accuracy")
+    ax2.legend()
+
+    st.pyplot(fig)
+
+    # Evaluate the model on test data
+    test_preds = np.argmax(model.predict(test_images), axis=1)
+    true_labels = np.argmax(test_labels, axis=1)
+
+    # Classification report
+    report = classification_report(true_labels, test_preds, digits=4)
+    st.text("Classification Report:")
+    st.text(report)
+
+# Testing with a specific index
+index = st.number_input("Enter an index (0-9999) to test:", min_value=0, max_value=9999, step=1)
+
+def test_index_prediction(index):
+    image = test_images[index].reshape(28, 28)
+    st.image(image, caption=f"True Label: {true_labels[index]}", use_column_width=True)
+
+    prediction = model.predict(test_images[index].reshape(1, 28, 28, 1))
+    predicted_class = np.argmax(prediction)
+    st.write(f"Predicted Class: {predicted_class}")
+
+if st.button("Test Index"):
+    if 'model' in locals() and model:
+        test_index_prediction(index)
+    else:
+        st.error("Train the model first.")
+