Update app.py

app.py

@@ -1,16 +1,20 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
 import os
-import zipfile
-import gdown
-import pathlib
+import PIL
 import tensorflow as tf
-from tensorflow.keras.preprocessing import image_dataset_from_directory
 
+from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization, Rescaling
-from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler
-import gradio as gr
-import numpy as np
+
+
+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'
@@ -40,127 +44,127 @@ except zipfile.BadZipFile:
 os.remove(local_zip_file)
 
 # Convert the extracted directory path to a pathlib.Path object
-data_dir = pathlib.Path('extracted_files/Pest_Dataset')
-
-
-
-# Set image dimensions and batch size
-img_height, img_width = 180, 180
-batch_size = 32
-
-# Create training and validation datasets
-train_ds = 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 = image_dataset_from_directory(
-    data_dir,
-    validation_split=0.2,
-    subset="validation",
-    seed=123,
-    image_size=(img_height, img_width),
-    batch_size=batch_size
-)
+data_dir = pathlib.Path(extracted_path)
 
-class_names = train_ds.class_names
-print(class_names)
+# 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]))
 
 
+bees = list(data_dir.glob('bees/*'))
+print(bees[0])
+PIL.Image.open(str(bees[0]))
 
-data_augmentation = tf.keras.Sequential(
-    [
-        layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
-        layers.RandomRotation(0.2),
-        layers.RandomZoom(0.2),
-        layers.RandomContrast(0.2),
-        layers.RandomBrightness(0.2),
-    ]
-)
 
+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)
 
-num_classes = len(class_names)
-model = Sequential()
 
-model.add(data_augmentation)
-model.add(Rescaling(1./255))
+class_names = train_ds.class_names
+print(class_names)
 
-model.add(Conv2D(32, 3, padding='same', activation='relu'))
-model.add(BatchNormalization())
-model.add(MaxPooling2D())
 
-model.add(Conv2D(64, 3, padding='same', activation='relu'))
-model.add(BatchNormalization())
-model.add(MaxPooling2D())
+import matplotlib.pyplot as plt
 
-model.add(Conv2D(128, 3, padding='same', activation='relu'))
-model.add(BatchNormalization())
-model.add(MaxPooling2D())
+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")
 
-model.add(Conv2D(256, 3, padding='same', activation='relu'))
-model.add(BatchNormalization())
-model.add(MaxPooling2D())
 
-model.add(Conv2D(512, 3, padding='same', activation='relu'))
-model.add(BatchNormalization())
-model.add(MaxPooling2D())
+data_augmentation = keras.Sequential(
+  [
+    layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+  ]
+)
 
-model.add(Dropout(0.5))
-model.add(Flatten())
 
-model.add(Dense(256, activation='relu'))
-model.add(Dropout(0.5))
+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")
 
-model.add(Dense(num_classes, activation='softmax', name="outputs"))
 
-model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+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', name="outputs")  # Use softmax here
+])
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),  # Change from_logits to False
               metrics=['accuracy'])
 
 model.summary()
 
 
-
-# Implement early stopping
-early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
-
-# Learning rate scheduler
-def scheduler(epoch, lr):
-    if epoch < 10:
-        return lr
-    else:
-        return lr * tf.math.exp(-0.1)
-
-lr_scheduler = LearningRateScheduler(scheduler)
-
-# Train the model
-epochs = 30
+epochs = 15
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs,
-    callbacks=[early_stopping, lr_scheduler]
+  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, (img_height, img_width))
+    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]
-    return {predicted_label: f"{float(prediction[predicted_class]):.2f}"}
+    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
 
 image = gr.Image()
 label = gr.Label(num_top_classes=1)
@@ -184,5 +188,3 @@ gr.Interface(
     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)
-
-