import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dense, Dropout, Flatten, BatchNormalization
from tensorflow.keras.models import Model

from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

import pandas as pd
import random
import cv2

from PIL import Image
import gdown
import zipfile

import pathlib

# Ensure that these imports are at the beginning of your script to avoid any NameError issues.


# Define the Google Drive shareable link
gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'

# Extract the file ID from the URL
file_id = gdrive_url.split('/d/')[1].split('/view')[0]
direct_download_url = f'https://drive.google.com/uc?id={file_id}'

# Define the local filename to save the ZIP file
local_zip_file = 'file.zip'

# Download the ZIP file
gdown.download(direct_download_url, local_zip_file, quiet=False)

# Directory to extract files
extracted_path = 'extracted_files'

# Verify if the downloaded file is a ZIP file and extract it
try:
    with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:
        zip_ref.extractall(extracted_path)
    print("Extraction successful!")
except zipfile.BadZipFile:
    print("Error: The downloaded file is not a valid ZIP file.")

# Optionally, you can delete the ZIP file after extraction
os.remove(local_zip_file)

# Convert the extracted directory path to a pathlib.Path object
data_dir = pathlib.Path(extracted_path)

# Print the directory structure to debug
for root, dirs, files in os.walk(extracted_path):
    level = root.replace(extracted_path, '').count(os.sep)
    indent = ' ' * 4 * (level)
    print(f"{indent}{os.path.basename(root)}/")
    subindent = ' ' * 4 * (level + 1)
    for f in files:
        print(f"{subindent}{f}")

# Path to the dataset directory
data_dir = pathlib.Path('extracted_files/Pest_Dataset')
data_dir = pathlib.Path(data_dir)

# Read images and labels into a DataFrame
image_paths = list(data_dir.glob('*/*.jpg'))
image_labels = [str(path.parent.name) for path in image_paths]
image_df = pd.DataFrame({'Filepath': image_paths, 'Label': image_labels})

# Display distribution of labels
label_counts = image_df['Label'].value_counts()
plt.figure(figsize=(10, 6))
sns.barplot(x=label_counts.index, y=label_counts.values, alpha=0.8, palette='rocket')
plt.title('Distribution of Labels in Image Dataset', fontsize=16)
plt.xlabel('Label', fontsize=14)
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=45)
plt.show()

# Display 16 pictures of the dataset with their labels
random_index = np.random.randint(0, len(image_df), 16)
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(10, 10),
                         subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
    ax.imshow(plt.imread(image_df.Filepath[random_index[i]]))
    ax.set_title(image_df.Label[random_index[i]])
plt.tight_layout()
plt.show()

# Function to return a random image path from a given directory
def random_sample(directory):
    images = [os.path.join(directory, img) for img in os.listdir(directory) if img.endswith(('.jpg', '.jpeg', '.png'))]
    return random.choice(images)

# Function to compute the Error Level Analysis (ELA) of an image
def compute_ela_cv(path, quality):
    temp_filename = 'temp.jpg'
    orig = cv2.imread(path)
    cv2.imwrite(temp_filename, orig, [int(cv2.IMWRITE_JPEG_QUALITY), quality])
    compressed = cv2.imread(temp_filename)
    ela_image = cv2.absdiff(orig, compressed)
    ela_image = np.clip(ela_image * 10, 0, 255).astype(np.uint8)
    return ela_image

# View random sample from the dataset
p = random_sample('extracted_files/Pest_Dataset/beetle')
orig = cv2.imread(p)
orig = cv2.cvtColor(orig, cv2.COLOR_BGR2RGB) / 255.0
init_val = 100
columns = 3
rows = 3

fig = plt.figure(figsize=(15, 10))
for i in range(1, columns*rows + 1):
    quality = init_val - (i-1) * 8
    img = compute_ela_cv(path=p, quality=quality)
    if i == 1:
        img = orig.copy()
    ax = fig.add_subplot(rows, columns, i)
    ax.title.set_text(f'q: {quality}')
    plt.imshow(img)
plt.show()

# Read images and labels into a DataFrame
image_paths = list(data_dir.glob('*/*.jpg'))
image_labels = [str(path.parent.name) for path in image_paths]
image_df = pd.DataFrame({'Filepath': [str(path) for path in image_paths], 'Label': image_labels})

# Separate into train and test data
train_df, test_df = train_test_split(image_df, test_size=0.2, shuffle=True, random_state=42)

train_generator = ImageDataGenerator(
    preprocessing_function=tf.keras.applications.efficientnet_v2.preprocess_input,
    validation_split=0.2
)

test_generator = ImageDataGenerator(
    preprocessing_function=tf.keras.applications.efficientnet_v2.preprocess_input
)

# Split the data into three categories
train_images = train_generator.flow_from_dataframe(
    dataframe=train_df,
    x_col='Filepath',
    y_col='Label',
    target_size=(224, 224),
    color_mode='rgb',
    class_mode='categorical',
    batch_size=32,
    shuffle=True,
    seed=42,
    subset='training'
)

val_images = train_generator.flow_from_dataframe(
    dataframe=train_df,
    x_col='Filepath',
    y_col='Label',
    target_size=(224, 224),
    color_mode='rgb',
    class_mode='categorical',
    batch_size=32,
    shuffle=True,
    seed=42,
    subset='validation'
)

test_images = test_generator.flow_from_dataframe(
    dataframe=test_df,
    x_col='Filepath',
    y_col='Label',
    target_size=(224, 224),
    color_mode='rgb',
    class_mode='categorical',
    batch_size=32,
    shuffle=False
)


# Data Augmentation Step
augment = tf.keras.Sequential([
    layers.experimental.preprocessing.Resizing(224, 224),
    layers.experimental.preprocessing.Rescaling(1./255),
    layers.experimental.preprocessing.RandomFlip("horizontal"),
    layers.experimental.preprocessing.RandomRotation(0.1),
    layers.experimental.preprocessing.RandomZoom(0.1),
    layers.experimental.preprocessing.RandomContrast(0.1),
])

# Load the pretrained model
pretrained_model = tf.keras.applications.efficientnet_v2.EfficientNetV2L(
    input_shape=(224, 224, 3),
    include_top=False,
    weights='imagenet',
    pooling='max'
)

pretrained_model.trainable = False

# Create checkpoint callback
checkpoint_path = "pests_cats_classification_model_checkpoint"
checkpoint_callback = ModelCheckpoint(checkpoint_path,
                                      save_weights_only=True,
                                      monitor="val_accuracy",
                                      save_best_only=True)

# Setup EarlyStopping callback to stop training if model's val_loss doesn't improve for 5 epochs
early_stopping = EarlyStopping(monitor="val_loss", patience=5, restore_best_weights=True)

inputs = pretrained_model.input
x = augment(inputs)

# Add new classification layers
x = Flatten()(pretrained_model.output)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
x = BatchNormalization()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)

outputs = Dense(12, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

model.compile(
    optimizer=Adam(0.00001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Train the model
history = model.fit(
    train_images,
    steps_per_epoch=len(train_images),
    validation_data=val_images,
    validation_steps=len(val_images),
    epochs=20,  # Change epochs to 20
    callbacks=[
        early_stopping,
        checkpoint_callback,
    ]
)

results = model.evaluate(test_images, verbose=0)

print("    Test Loss: {:.5f}".format(results[0]))
print("Test Accuracy: {:.2f}%".format(results[1] * 100))

accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(accuracy))
plt.plot(epochs, accuracy, 'b', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')

plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')

plt.title('Training and validation loss')
plt.legend()
plt.show()

# Predict the label of the test_images
pred = model.predict(test_images)
pred = np.argmax(pred, axis=1)

# Map the label
labels = (train_images.class_indices)
labels = dict((v, k) for k, v in labels.items())
pred = [labels[k] for k in pred]

# Display the result
print(f'The first 5 predictions: {pred[:5]}')

# Display 25 random pictures from the dataset with their labels
random_index = np.random.randint(0, len(test_df) - 1, 15)
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(25, 15),
                         subplot_kw={'xticks': [], 'yticks': []})

for i, ax in enumerate(axes.flat):
    ax.imshow(plt.imread(test_df.Filepath.iloc[random_index[i]]))
    if test_df.Label.iloc[random_index[i]] == pred[random_index[i]]:
        color = "green"
    else:
        color = "red"
    ax.set_title(f"True: {test_df.Label.iloc[random_index[i]]}\nPredicted: {pred[random_index[i]]}", color=color)
plt.show()
plt.tight_layout()

y_test = list(test_df.Label)
print(classification_report(y_test, pred))

report = classification_report(y_test, pred, output_dict=True)
df = pd.DataFrame(report).transpose()
df

# Define function to get image array
def get_img_array(img_path, size):
    img = tf.keras.preprocessing.image.load_img(img_path, target_size=size)
    array = tf.keras.preprocessing.image.img_to_array(img)
    array = np.expand_dims(array, axis=0)
    return array

# Define function to make Grad-CAM heatmap
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, classifier_layer_names=None):
    grad_model = tf.keras.models.Model(
        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
    )

    with tf.GradientTape() as tape:
        conv_outputs, predictions = grad_model(img_array)
        loss = predictions[:, np.argmax(predictions[0])]

    output = conv_outputs[0]
    grads = tape.gradient(loss, conv_outputs)[0]

    gate_f = tf.cast(output > 0, "float32")
    gate_r = tf.cast(grads > 0, "float32")
    guided_grads = grads * gate_f * gate_r

    weights = tf.reduce_mean(guided_grads, axis=(0, 1))

    cam = np.zeros(output.shape[0:2], dtype=np.float32)

    for i, w in enumerate(weights):
        cam += w * output[:, :, i]

    cam = cv2.resize(cam.numpy(), (img_array.shape[2], img_array.shape[1]))
    cam = np.maximum(cam, 0)
    heatmap = cam / cam.max()

    return heatmap

# Define function to save and display Grad-CAM
def save_and_display_gradcam(img_path, heatmap, cam_path="cam.jpg", alpha=0.4):
    img = tf.keras.preprocessing.image.load_img(img_path)
    img = tf.keras.preprocessing.image.img_to_array(img)

    heatmap = np.uint8(255 * heatmap)

    jet = cm.get_cmap("jet")

    jet_colors = jet(np.arange(256))[:, :3]
    jet_heatmap = jet_colors[heatmap]

    jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
    jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)

    superimposed_img = jet_heatmap * alpha + img
    superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)

    superimposed_img.save(cam_path)

    return cam_path

# Display the part of the pictures used by the neural network to classify the pictures
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(15, 10),
                         subplot_kw={'xticks': [], 'yticks': []})

for i, ax in enumerate(axes.flat):
    img_path = test_df.Filepath.iloc[random_index[i]]
    img_array = tf.keras.applications.efficientnet_v2.preprocess_input(get_img_array(img_path, size=(224, 224)))
    heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name="top_conv")
    cam_path = save_and_display_gradcam(img_path, heatmap)
    ax.imshow(plt.imread(cam_path))
    ax.set_title(f"True: {test_df.Label.iloc[random_index[i]]}\nPredicted: {pred[random_index[i]]}")
plt.tight_layout()
plt.show()

# Define Gradio interface
def predict_image(img):
    img = np.array(img)
    img_resized = tf.image.resize(img, (224, 224))
    img_4d = tf.expand_dims(img_resized, axis=0)
    prediction = model.predict(img_4d)[0]
    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}

image = gr.Image()
label = gr.Label(num_top_classes=1)

# Define custom CSS for background image
custom_css = """
body {
    background-image: url('extracted_files/Pest_Dataset/bees/bees (444).jpg');
    background-size: cover;
    background-repeat: no-repeat;
    background-attachment: fixed;
    color: white;
}
"""

gr.Interface(
    fn=predict_image,
    inputs=image,
    outputs=label,
    title="Welcome to Agricultural Pest Image Classification",
    description="The image data set used was obtained from Kaggle and has a collection of 12 different types of agricultural pests: Ants, Bees, Beetles, Caterpillars, Earthworms, Earwigs, Grasshoppers, Moths, Slugs, Snails, Wasps, and Weevils",
    css=custom_css
).launch(debug=True)