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 PIL import Image
import gdown
import zipfile
import pathlib

# 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}")

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

bees = list(data_dir.glob('bees/*'))
print(bees[0])
PIL.Image.open(str(bees[0]))

img_height, img_width = 180, 180
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size
)

class_names = train_ds.class_names
print(class_names)

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[labels[i]])
    plt.axis("off")

data_augmentation = keras.Sequential(
  [
    layers.RandomFlip("horizontal",
                      input_shape=(img_height,
                                  img_width,
                                  3)),
    layers.RandomRotation(0.1),
    layers.RandomZoom(0.1),
    layers.RandomContrast(0.1),
    layers.RandomBrightness(0.1)
  ]
)

plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
  for i in range(9):
    augmented_images = data_augmentation(images)
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(augmented_images[0].numpy().astype("uint8"))
    plt.axis("off")

num_classes = len(class_names)
model = Sequential([
  data_augmentation,
  layers.Rescaling(1./255),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(128, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Dropout(0.5),
  layers.Flatten(),
  layers.Dense(256, activation='relu'),
  layers.Dropout(0.5),
  layers.Dense(num_classes, activation='softmax', name="outputs")
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['accuracy'])

model.summary()

# Learning rate scheduler
lr_scheduler = keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))

# Early stopping
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

epochs = 20
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs,
  callbacks=[lr_scheduler, early_stopping]
)

# Define category descriptions
category_descriptions = {
    "Ants": "Ants are small insects known for their complex social structures and teamwork.",
    "Bees": "Bees are flying insects known for their role in pollination and producing honey.",
    "Beetles": "Beetles are a group of insects with hard exoskeletons and wings. They are the largest order of insects.",
    "Caterpillars": "Caterpillars are the larval stage of butterflies and moths, known for their voracious appetite.",
    "Earthworms": "Earthworms are segmented worms that are crucial for soil health and nutrient cycling.",
    "Earwigs": "Earwigs are insects with pincers on their abdomen and are known for their nocturnal activity.",
    "Grasshoppers": "Grasshoppers are insects known for their powerful hind legs, which they use for jumping.",
    "Moths": "Moths are nocturnal insects related to butterflies, known for their attraction to light.",
    "Slugs": "Slugs are soft-bodied mollusks that are similar to snails but lack a shell.",
    "Snails": "Snails are mollusks with a coiled shell, known for their slow movement and slimy trail.",
    "Wasps": "Wasps are stinging insects that can be solitary or social, and some species are important pollinators.",
    "Weevils": "Weevils are a type of beetle with a long snout, known for being pests to crops and stored grains."
}

# Define the prediction function
def predict_image(img):
    img = np.array(img)
    img_resized = tf.image.resize(img, (180, 180))
    img_4d = tf.expand_dims(img_resized, axis=0)
    prediction = model.predict(img_4d)[0]
    predicted_class = np.argmax(prediction)
    predicted_label = class_names[predicted_class]
    predicted_description = category_descriptions[predicted_label]
    return {predicted_label: f"{float(prediction[predicted_class]):.2f} - {predicted_description}"}

# Set up Gradio interface
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)