Create 9_Cifar_10.py

pages/9_Cifar_10.py

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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]})")