import os
import time
import shutil
import pathlib
import itertools
from PIL import Image

# Import data handling tools
import cv2
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('darkgrid')
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, precision_score, recall_score, f1_score

# Import deep learning libraries
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam, Adamax
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization, GlobalAveragePooling2D
from tensorflow.keras import regularizers
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.applications import DenseNet121,InceptionV3
from tensorflow.keras.models import Model

# Ignore warnings
import warnings
warnings.filterwarnings("ignore")

print('Modules loaded')

# Generate data paths with labels
data_dir = '/kaggle/input/lung-and-colon-cancer-histopathological-images/lung_colon_image_set'
filepaths = []
labels = []
folds = os.listdir(data_dir)

# Generate paths and labels
for fold in folds:
    foldpath = os.path.join(data_dir, fold)
    flist = os.listdir(foldpath)
    for f in flist:
        f_path = os.path.join(foldpath, f)
        filelist = os.listdir(f_path)
        for file in filelist:
            fpath = os.path.join(f_path, file)
            filepaths.append(fpath)
            if f == 'colon_aca':
                labels.append('Colon Adenocarcinoma')
            elif f == 'colon_n':
                labels.append('Colon Benign Tissue')
            elif f == 'lung_aca':
                labels.append('Lung Adenocarcinoma')
            elif f == 'lung_n':
                labels.append('Lung Benign Tissue')
            elif f == 'lung_scc':
                labels.append('Lung Squamous Cell Carcinoma')

# Concatenate data paths with labels into a DataFrame
df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})

# Split dataset into train, validation, and test sets
train_df, temp_df = train_test_split(df, train_size=0.8, stratify=df['labels'], random_state=42)
valid_df, test_df = train_test_split(temp_df, train_size=0.5, stratify=temp_df['labels'], random_state=42)

# Define image size, channels, and batch size
batch_size = 64
img_size = (224, 224)
channels = 3
img_shape = (img_size[0], img_size[1], channels)

# Create ImageDataGenerator for training and validation
train_datagen = ImageDataGenerator()
valid_datagen = ImageDataGenerator()

train_gen = train_datagen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',
                                              target_size=img_size, class_mode='categorical',
                                              batch_size=batch_size, shuffle=True)

valid_gen = valid_datagen.flow_from_dataframe(valid_df, x_col='filepaths', y_col='labels',
                                              target_size=img_size, class_mode='categorical',
                                              batch_size=batch_size, shuffle=True)

test_gen = valid_datagen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',
                                             target_size=img_size, class_mode='categorical',
                                             batch_size=batch_size, shuffle=False)

# Get class names
num_classes = len(train_gen.class_indices)

# Define the model
#base_model = DenseNet121(input_shape=img_shape, include_top=False, weights='imagenet')
base_model = InceptionV3(input_shape=img_shape, include_top=False, weights='imagenet')

base_model.trainable = True

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(256, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

model_DenseNet = Model(inputs=base_model.input, outputs=predictions)

# Compile the model
model_DenseNet.compile(optimizer=Adamax(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# Define callbacks
callbacks = [
    ModelCheckpoint(filepath='best_model.keras', monitor='val_loss', save_best_only=True, verbose=1),
    EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True, verbose=1),
    ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=1e-6, verbose=1)
]

# Helper function to calculate metrics
def calculate_metrics(generator, model):
    preds = model.predict(generator)
    y_true = generator.classes
    y_pred = np.argmax(preds, axis=1)
    
    precision = precision_score(y_true, y_pred, average='weighted')
    recall = recall_score(y_true, y_pred, average='weighted')
    f1 = f1_score(y_true, y_pred, average='weighted')
    
    return precision, recall, f1

# Train the model and calculate metrics for each epoch
class MetricsCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        # Training metrics
        train_precision, train_recall, train_f1 = calculate_metrics(train_gen, self.model)
        print(f'Epoch {epoch+1} Training Precision: {train_precision:.4f}, Recall: {train_recall:.4f}, F1 Score: {train_f1:.4f}')
        
        # Validation metrics
        val_precision, val_recall, val_f1 = calculate_metrics(valid_gen, self.model)
        print(f'Epoch {epoch+1} Validation Precision: {val_precision:.4f}, Recall: {val_recall:.4f}, F1 Score: {val_f1:.4f}')

# Measure training time
start_time = time.time()

# Train the model with the custom metrics callback
history = model_DenseNet.fit(train_gen, validation_data=valid_gen, epochs=20, callbacks=[MetricsCallback()] + callbacks)

end_time = time.time()
training_time = end_time - start_time
print(f'Total Training Time: {training_time:.2f} seconds')

# Plot training history (accuracy and loss)
plt.figure(figsize=(12, 5))

# Plot accuracy
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Val Accuracy')
plt.title('Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

# Plot loss
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Val Loss')
plt.title('Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()

# Measure testing time
start_time = time.time()

# Evaluate on the test set
test_loss, test_acc = model_DenseNet.evaluate(test_gen)

end_time = time.time()
testing_time = end_time - start_time

print(f'Test Accuracy: {test_acc:.4f}')
print(f'Total Testing Time: {testing_time:.2f} seconds')

# Final metrics on the test set
test_precision, test_recall, test_f1 = calculate_metrics(test_gen, model_DenseNet)
print(f'Test Precision: {test_precision:.4f}, Recall: {test_recall:.4f}, F1 Score: {test_f1:.4f}')

Modules loaded
Found 20000 validated image filenames belonging to 5 classes.
Found 2500 validated image filenames belonging to 5 classes.
Found 2500 validated image filenames belonging to 5 classes.
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
87910968/87910968 ━━━━━━━━━━━━━━━━━━━━ 4s 0us/step
Epoch 1/20


WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1727877360.012647      68 service.cc:145] XLA service 0x7edf98003240 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1727877360.012710      68 service.cc:153]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1727877397.680112      68 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'loop_multiply_fusion_18', 12 bytes spill stores, 24 bytes spill loads

I0000 00:00:1727877397.744461      68 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.


313/313 ━━━━━━━━━━━━━━━━━━━━ 104s 315ms/step
Epoch 1 Training Precision: 0.2040, Recall: 0.2037, F1 Score: 0.2032
40/40 ━━━━━━━━━━━━━━━━━━━━ 14s 343ms/step
Epoch 1 Validation Precision: 0.2062, Recall: 0.2076, F1 Score: 0.2058

Epoch 1: val_loss improved from inf to 0.12445, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 445s 1s/step - accuracy: 0.9367 - loss: 0.1787 - val_accuracy: 0.9520 - val_loss: 0.1244 - learning_rate: 0.0010
Epoch 2/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 91s 291ms/step
Epoch 2 Training Precision: 0.1986, Recall: 0.1986, F1 Score: 0.1983
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 288ms/step
Epoch 2 Validation Precision: 0.2071, Recall: 0.2068, F1 Score: 0.2063

Epoch 2: val_loss did not improve from 0.12445
313/313 ━━━━━━━━━━━━━━━━━━━━ 213s 676ms/step - accuracy: 0.9966 - loss: 0.0111 - val_accuracy: 0.9644 - val_loss: 0.1606 - learning_rate: 0.0010
Epoch 3/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 298ms/step
Epoch 3 Training Precision: 0.1979, Recall: 0.1979, F1 Score: 0.1979
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 290ms/step
Epoch 3 Validation Precision: 0.2032, Recall: 0.2032, F1 Score: 0.2032

Epoch 3: val_loss improved from 0.12445 to 0.00737, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 220s 698ms/step - accuracy: 0.9971 - loss: 0.0106 - val_accuracy: 0.9984 - val_loss: 0.0074 - learning_rate: 0.0010
Epoch 4/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 94s 300ms/step
Epoch 4 Training Precision: 0.1982, Recall: 0.1981, F1 Score: 0.1982
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 285ms/step
Epoch 4 Validation Precision: 0.2028, Recall: 0.2028, F1 Score: 0.2028

Epoch 4: val_loss improved from 0.00737 to 0.00456, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 218s 690ms/step - accuracy: 0.9976 - loss: 0.0076 - val_accuracy: 0.9984 - val_loss: 0.0046 - learning_rate: 0.0010
Epoch 5/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 95s 304ms/step
Epoch 5 Training Precision: 0.1999, Recall: 0.1998, F1 Score: 0.1999
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 293ms/step
Epoch 5 Validation Precision: 0.2080, Recall: 0.2080, F1 Score: 0.2080

Epoch 5: val_loss did not improve from 0.00456
313/313 ━━━━━━━━━━━━━━━━━━━━ 218s 690ms/step - accuracy: 0.9975 - loss: 0.0071 - val_accuracy: 0.9944 - val_loss: 0.0137 - learning_rate: 0.0010
Epoch 6/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 95s 303ms/step
Epoch 6 Training Precision: 0.1996, Recall: 0.1996, F1 Score: 0.1996
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step
Epoch 6 Validation Precision: 0.1988, Recall: 0.1988, F1 Score: 0.1988

Epoch 6: val_loss improved from 0.00456 to 0.00263, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 219s 692ms/step - accuracy: 0.9972 - loss: 0.0099 - val_accuracy: 0.9988 - val_loss: 0.0026 - learning_rate: 0.0010
Epoch 7/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 94s 299ms/step
Epoch 7 Training Precision: 0.1975, Recall: 0.1975, F1 Score: 0.1975
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 286ms/step
Epoch 7 Validation Precision: 0.2052, Recall: 0.2052, F1 Score: 0.2052

Epoch 7: val_loss did not improve from 0.00263
313/313 ━━━━━━━━━━━━━━━━━━━━ 215s 681ms/step - accuracy: 0.9996 - loss: 0.0026 - val_accuracy: 0.9988 - val_loss: 0.0077 - learning_rate: 0.0010
Epoch 8/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 96s 308ms/step
Epoch 8 Training Precision: 0.2022, Recall: 0.2021, F1 Score: 0.2022
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 291ms/step
Epoch 8 Validation Precision: 0.2024, Recall: 0.2024, F1 Score: 0.2024

Epoch 8: val_loss did not improve from 0.00263
313/313 ━━━━━━━━━━━━━━━━━━━━ 218s 690ms/step - accuracy: 0.9996 - loss: 0.0013 - val_accuracy: 0.9988 - val_loss: 0.0033 - learning_rate: 0.0010
Epoch 9/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 296ms/step
Epoch 9 Training Precision: 0.1976, Recall: 0.1976, F1 Score: 0.1976
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 271ms/step
Epoch 9 Validation Precision: 0.1872, Recall: 0.1872, F1 Score: 0.1872

Epoch 9: val_loss did not improve from 0.00263

Epoch 9: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
313/313 ━━━━━━━━━━━━━━━━━━━━ 214s 676ms/step - accuracy: 0.9996 - loss: 0.0018 - val_accuracy: 0.9964 - val_loss: 0.0136 - learning_rate: 0.0010
Epoch 10/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 297ms/step
Epoch 10 Training Precision: 0.2029, Recall: 0.2029, F1 Score: 0.2029
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 283ms/step
Epoch 10 Validation Precision: 0.1996, Recall: 0.1996, F1 Score: 0.1996

Epoch 10: val_loss improved from 0.00263 to 0.00206, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 213s 676ms/step - accuracy: 0.9987 - loss: 0.0034 - val_accuracy: 0.9992 - val_loss: 0.0021 - learning_rate: 2.0000e-04
Epoch 11/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 297ms/step
Epoch 11 Training Precision: 0.1991, Recall: 0.1991, F1 Score: 0.1991
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 276ms/step
Epoch 11 Validation Precision: 0.1908, Recall: 0.1908, F1 Score: 0.1908

Epoch 11: val_loss improved from 0.00206 to 0.00126, saving model to best_model.keras
313/313 ━━━━━━━━━━━━━━━━━━━━ 214s 678ms/step - accuracy: 1.0000 - loss: 2.5278e-04 - val_accuracy: 0.9992 - val_loss: 0.0013 - learning_rate: 2.0000e-04
Epoch 12/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 96s 305ms/step
Epoch 12 Training Precision: 0.1983, Recall: 0.1983, F1 Score: 0.1983
40/40 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step
Epoch 12 Validation Precision: 0.1808, Recall: 0.1808, F1 Score: 0.1808

Epoch 12: val_loss did not improve from 0.00126
313/313 ━━━━━━━━━━━━━━━━━━━━ 218s 691ms/step - accuracy: 0.9999 - loss: 2.0833e-04 - val_accuracy: 0.9992 - val_loss: 0.0019 - learning_rate: 2.0000e-04
Epoch 13/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 92s 293ms/step
Epoch 13 Training Precision: 0.1990, Recall: 0.1990, F1 Score: 0.1990
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 285ms/step
Epoch 13 Validation Precision: 0.2028, Recall: 0.2028, F1 Score: 0.2028

Epoch 13: val_loss did not improve from 0.00126
313/313 ━━━━━━━━━━━━━━━━━━━━ 217s 686ms/step - accuracy: 1.0000 - loss: 1.3029e-04 - val_accuracy: 0.9992 - val_loss: 0.0019 - learning_rate: 2.0000e-04
Epoch 14/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 296ms/step
Epoch 14 Training Precision: 0.1993, Recall: 0.1993, F1 Score: 0.1993
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 281ms/step
Epoch 14 Validation Precision: 0.2096, Recall: 0.2096, F1 Score: 0.2096

Epoch 14: val_loss did not improve from 0.00126

Epoch 14: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
313/313 ━━━━━━━━━━━━━━━━━━━━ 215s 679ms/step - accuracy: 1.0000 - loss: 4.2404e-05 - val_accuracy: 0.9992 - val_loss: 0.0020 - learning_rate: 2.0000e-04
Epoch 15/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 91s 291ms/step
Epoch 15 Training Precision: 0.2021, Recall: 0.2021, F1 Score: 0.2021
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 284ms/step
Epoch 15 Validation Precision: 0.1928, Recall: 0.1928, F1 Score: 0.1928

Epoch 15: val_loss did not improve from 0.00126
313/313 ━━━━━━━━━━━━━━━━━━━━ 211s 670ms/step - accuracy: 1.0000 - loss: 5.6256e-05 - val_accuracy: 0.9992 - val_loss: 0.0019 - learning_rate: 4.0000e-05
Epoch 16/20
313/313 ━━━━━━━━━━━━━━━━━━━━ 93s 298ms/step
Epoch 16 Training Precision: 0.1956, Recall: 0.1956, F1 Score: 0.1956
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 277ms/step
Epoch 16 Validation Precision: 0.2080, Recall: 0.2080, F1 Score: 0.2080

Epoch 16: val_loss did not improve from 0.00126
313/313 ━━━━━━━━━━━━━━━━━━━━ 213s 673ms/step - accuracy: 1.0000 - loss: 3.8977e-05 - val_accuracy: 0.9992 - val_loss: 0.0019 - learning_rate: 4.0000e-05
Epoch 16: early stopping
Restoring model weights from the end of the best epoch: 11.
Total Training Time: 3681.97 seconds

40/40 ━━━━━━━━━━━━━━━━━━━━ 26s 647ms/step - accuracy: 0.9998 - loss: 3.0504e-04
Test Accuracy: 0.9996
Total Testing Time: 26.87 seconds
40/40 ━━━━━━━━━━━━━━━━━━━━ 11s 285ms/step
Test Precision: 0.9996, Recall: 0.9996, F1 Score: 0.9996