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| import streamlit as st | |
| import tensorflow as tf | |
| from tensorflow.keras import datasets, layers, models | |
| from tensorflow.keras.preprocessing.image import ImageDataGenerator | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image | |
| # Define the CNN model | |
| def create_cnn_model(): | |
| model = models.Sequential() | |
| model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
| model.add(layers.Flatten()) | |
| model.add(layers.Dense(64, activation='relu')) | |
| model.add(layers.Dropout(0.5)) | |
| model.add(layers.Dense(10, activation='softmax')) | |
| return model | |
| # Streamlit app | |
| st.title("CIFAR-10 Image Classification with CNN") | |
| # Load CIFAR-10 data | |
| (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() | |
| train_images, test_images = train_images / 255.0, test_images / 255.0 | |
| # Display sample images | |
| st.subheader("Sample Training Images") | |
| fig, ax = plt.subplots(1, 5, figsize=(15, 3)) | |
| for i in range(5): | |
| ax[i].imshow(train_images[i]) | |
| ax[i].axis('off') | |
| st.pyplot(fig) | |
| # Model creation | |
| model = create_cnn_model() | |
| # Compile the model | |
| model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) | |
| # Data augmentation | |
| datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True) | |
| datagen.fit(train_images) | |
| # Training parameters | |
| batch_size = st.slider("Batch Size", 32, 128, 64, 32) | |
| epochs = st.slider("Epochs", 10, 50, 20, 10) | |
| # Train button | |
| if st.button("Train Model"): | |
| with st.spinner("Training the model..."): | |
| history = model.fit(datagen.flow(train_images, train_labels, batch_size=batch_size), | |
| steps_per_epoch=len(train_images) / batch_size, | |
| epochs=epochs, | |
| validation_data=(test_images, test_labels)) | |
| st.success("Model training completed!") | |
| # Display training curves | |
| st.subheader("Training and Validation Accuracy") | |
| fig, ax = plt.subplots() | |
| ax.plot(history.history['accuracy'], label='Training Accuracy') | |
| ax.plot(history.history['val_accuracy'], label='Validation Accuracy') | |
| ax.set_xlabel('Epoch') | |
| ax.set_ylabel('Accuracy') | |
| ax.legend() | |
| st.pyplot(fig) | |
| st.subheader("Training and Validation Loss") | |
| fig, ax = plt.subplots() | |
| ax.plot(history.history['loss'], label='Training Loss') | |
| ax.plot(history.history['val_loss'], label='Validation Loss') | |
| ax.set_xlabel('Epoch') | |
| ax.set_ylabel('Loss') | |
| ax.legend() | |
| st.pyplot(fig) | |
| # Prediction on uploaded image | |
| st.subheader("Make Predictions") | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Preprocess the uploaded image | |
| image = Image.open(uploaded_file) | |
| image = image.resize((32, 32)) | |
| image_array = np.array(image) / 255.0 | |
| st.image(image, caption='Uploaded Image', use_column_width=True) | |
| if st.button("Predict"): | |
| prediction = model.predict(np.expand_dims(image_array, axis=0)) | |
| predicted_class = np.argmax(prediction) | |
| st.write(f"Predicted Class: {predicted_class} ({class_names[predicted_class]})") | |