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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
import os
# 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")
# Check if model is saved
model_path = "mnist_cnn_model.h5"
if st.button("Train Model"):
model = create_model()
with st.spinner("Training..."):
st.text("Training now:")
history = model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=10, batch_size=64)
# Save the model
model.save(model_path)
# 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)
# Store the test labels globally for later use
st.session_state['true_labels'] = true_labels
# 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: {st.session_state['true_labels'][index]}", use_column_width=True)
# Reload the model if needed
if not os.path.exists(model_path):
st.error("Train the model first.")
return
model = keras.models.load_model(model_path)
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"):
test_index_prediction(index)
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