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]})")