Create app.file

app.file

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+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}")
+
+# Path to the dataset directory
+data_dir = pathlib.Path('extracted_files/Pest_Dataset')
+data_dir = pathlib.Path(data_dir)
+
+# Verify if the path exists
+assert data_dir.exists(), f"Path {data_dir} does not exist."
+
+# Load the dataset
+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)
+
+# Plot some images from the training dataset
+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")
+
+# Define data augmentation
+data_augmentation = keras.Sequential(
+  [
+    layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+  ]
+)
+
+# Plot augmented images
+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")
+
+# Define the model
+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.Dropout(0.2),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              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
+)
+
+# Define the Gradio interface
+def predict_image(img):
+    img = np.array(img)
+    img_resized = tf.image.resize(img, (img_height, img_width))
+    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)