app.py

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

# Download and extract dataset
gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
file_id = gdrive_url.split('/d/')[1].split('/view')[0]
direct_download_url = f'https://drive.google.com/uc?id={file_id}'
local_zip_file = 'file.zip'
gdown.download(direct_download_url, local_zip_file, quiet=False)
extracted_path = 'extracted_files'
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.")
os.remove(local_zip_file)
data_dir = pathlib.Path(extracted_path) / 'Pest_Dataset'

# Data loading and preprocessing
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

# Data augmentation
data_augmentation = keras.Sequential(
    [
        layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
        layers.RandomRotation(0.1),
        layers.RandomZoom(0.1),
        layers.RandomBrightness(0.2),
        layers.RandomContrast(0.2),
    ]
)

# Model definition
num_classes = len(class_names)
model = Sequential([
    data_augmentation,
    layers.Rescaling(1./255),
    layers.Conv2D(16, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    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.Dense(num_classes, activation='softmax', name="outputs")
])

optimizer = keras.optimizers.Adam(learning_rate=0.001)
lr_scheduler = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3)
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

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

model.summary()

# Train the model
epochs = 15
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."
}

# 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]
    top_3_indices = prediction.argsort()[-3:][::-1]
    results = {}
    for i in top_3_indices:
        class_name = class_names[i]
        results[class_name] = f"{float(prediction[i]):.2f} - {category_descriptions[class_name]}"
    return results

# Gradio interface setup
image = gr.Image()
label = gr.Label(num_top_classes=3)
custom_css = """
body {background-color: #f5f5f5;}
.gradio-container {border: 1px solid #ccc; border-radius: 10px; padding: 20px;}
"""

gr.Interface(
    fn=predict_image,
    inputs=image,
    outputs=label,
    title="Agricultural Pest Image Classification",
    description="Identify 12 types of agricultural pests from images. This model was trained on a dataset from Kaggle.",
    css=custom_css
).launch(debug=True)