Update app.py

app.py

@@ -4,13 +4,22 @@ import numpy as np
 import os
 import PIL
 import tensorflow as tf
+
 from tensorflow import keras
-from tensorflow.keras import layers, Sequential
+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'
 
@@ -41,37 +50,88 @@ 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 = data_dir / 'Pest_Dataset'
+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]))
+
 
-# Load 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=(180, 180),
-    batch_size=32)
+  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=(180, 180),
-    batch_size=32)
+  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)
 
-data_augmentation = Sequential([
-    layers.RandomFlip("horizontal"),
+
+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")
+
+
+data_augmentation = keras.Sequential(
+  [
+    layers.RandomFlip("horizontal",
+                      input_shape=(img_height,
+                                  img_width,
+                                  3)),
     layers.RandomRotation(0.1),
     layers.RandomZoom(0.1),
-])
+  ]
+)
 
-num_classes = len(class_names)
+
+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),
@@ -84,22 +144,25 @@ model = Sequential([
   layers.Dropout(0.2),
   layers.Flatten(),
   layers.Dense(128, activation='relu'),
-  layers.Dense(num_classes, name="outputs")
+  layers.Dense(num_classes, activation='softmax', name="outputs")  # Use softmax here
 ])
 
 model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),  # Change from_logits to False
               metrics=['accuracy'])
 
 model.summary()
 
+
 epochs = 15
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
 )
 
+
+
 # Define category descriptions
 category_descriptions = {
     "Ants": "Ants are small insects known for their complex social structures and teamwork.",
@@ -150,3 +213,4 @@ 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)
+