Delete app.py

app.py

@@ -1,199 +0,0 @@
-
-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)
-
-
-
-
-import matplotlib.pyplot as plt
-
-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")
-
-
-
-
-
-
-
-
-num_classes = 12
-
-model = Sequential([
-  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
-  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.Flatten(),
-  layers.Dense(128, activation='relu'),
-  layers.Dense(num_classes,activation='softmax')
-])
-
-
-
-
-
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-              metrics=['accuracy'])
-
-
-
-
-epochs=10
-history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
-)
-
-
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-
-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]
-    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
-
-image = gr.Image()
-label = gr.Label(num_top_classes=5)
-
-# 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="Pest Classification",
-    description="Upload an image of a pest to classify it into one of the predefined categories.",
-    css=custom_css
-).launch(debug=True)