Update app.py

app.py

@@ -9,11 +9,15 @@ 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'
 
@@ -53,69 +57,105 @@ for root, dirs, files in os.walk(extracted_path):
     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
+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
-)
+  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
-)
+  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")
 
 data_augmentation = keras.Sequential(
-    [
-        layers.RandomFlip("horizontal",
-                          input_shape=(img_height,
-                                      img_width,
-                                      3)),
-        layers.RandomRotation(0.1),
-        layers.RandomZoom(0.1),
-    ]
+  [
+    layers.RandomFlip("horizontal",
+                      input_shape=(img_height,
+                                  img_width,
+                                  3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+  ]
 )
 
+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),
-    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, name="outputs")
+  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, name="outputs")
 ])
 
+
 model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
-epochs = 20
+
+model.summary()
+
+epochs = 50
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
 )
 
+results = model.evaluate(val_ds, verbose=0)
+
+print("Validation Loss: {:.5f}".format(results[0]))
+print("Validation Accuracy: {:.2f}%".format(results[1] * 100))
+
 import gradio as gr
 import numpy as np
 import tensorflow as tf
@@ -125,16 +165,34 @@ def predict_image(img):
     img_resized = tf.image.resize(img, (180, 180))
     img_4d = tf.expand_dims(img_resized, axis=0)
     prediction = model.predict(img_4d)[0]
-    probabilities = tf.nn.softmax(prediction)
-    return {class_names[i]: float(probabilities[i]) * 100 for i in range(len(class_names))}
+    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
+
+
+
 
 image = gr.Image()
 label = gr.Label(num_top_classes=12)
 
+# 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."
+    description="Upload an image of a pest to classify it into one of the predefined categories.",
+    css=custom_css
 ).launch(debug=True)
+
+
+
+