Update app.py

app.py

@@ -1,202 +1,29 @@
-# %%
-import os
-os.environ['KMP_DUPLICATE_LIB_OK']= 'True'
-
+import gradio as gr
 import tensorflow as tf
-tf.__version__
-
-# %%
-
-import matplotlib.pyplot as plt
 import numpy as np
-
-
-# %%
-# Set the paths to your dataset directories
-train_dir = r'pokemon/train'
-val_dir = r'pokemon/val'
-
-# Ensure the paths are correctly formatted
-train_dir = os.path.normpath(train_dir)
-val_dir = os.path.normpath(val_dir)
-
-# Load the datasets
-train_ds = tf.keras.utils.image_dataset_from_directory(
-    directory=train_dir,
-    labels='inferred',
-    label_mode='int',  # Use 'int' for sparse_categorical_crossentropy loss
-    batch_size=12,
-    image_size=(150, 150))
-
-validation_ds = tf.keras.utils.image_dataset_from_directory(
-    directory=val_dir,
-    labels='inferred',
-    label_mode='int',
-    batch_size=12,
-    image_size=(150, 150))
-
-# %%
-val_batches = tf.data.experimental.cardinality(validation_ds)
-test_ds = validation_ds.take(val_batches // 5)
-validation_ds = validation_ds.skip(val_batches // 5)
-
-# %%
-print('Number of training batches: %d' % tf.data.experimental.cardinality(train_ds))
-print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_ds))
-print('Number of test batches: %d' % tf.data.experimental.cardinality(test_ds))
-
-# %%
-class_names = train_ds.class_names
-
-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")
-
-# %%
-number_of_classes = len(train_ds.class_names)
-print(number_of_classes)
-print(class_names)
-
-# %%
-#resize 150x150?
-resize_fn = tf.keras.layers.Resizing(150, 150)
-
-train_ds = train_ds.map(lambda x, y: (resize_fn(x), y))
-validation_ds = validation_ds.map(lambda x, y: (resize_fn(x), y))
-test_ds = test_ds.map(lambda x, y: (resize_fn(x), y))
-
-# %%
-
-# Build the model
-base_model = tf.keras.applications.Xception(
-    weights="imagenet",  # Load weights pre-trained on ImageNet.
-    input_shape=(150, 150, 3),
-    include_top=False,
-)  # Do not include the ImageNet classifier at the top.
-
-# Freeze the base_model
-base_model.trainable = False
-
-# Create new model on top
-inputs = tf.keras.Input(shape=(150, 150, 3))
-
-# Pre-trained Xception weights require that input be scaled
-# from (0, 255) to a range of (-1., +1.), the rescaling layer
-# outputs: `(inputs * scale) + offset`
-scale_layer = tf.keras.layers.Rescaling(scale=1 / 127.5, offset=-1)
-x = scale_layer(inputs)
-
-# The base model contains batchnorm layers. We want to keep them in inference mode
-# when we unfreeze the base model for fine-tuning, so we make sure that the
-# base_model is running in inference mode here.
-x = base_model(x, training=False)
-x = tf.keras.layers.GlobalAveragePooling2D()(x)
-x = tf.keras.layers.Dropout(0.2)(x)  # Regularize with dropout
-outputs = tf.keras.layers.Dense(number_of_classes, activation="softmax")(x)
-model = tf.keras.Model(inputs, outputs)
-
-model.summary(show_trainable=True)
-
-
-# %%
-model.compile(optimizer=tf.keras.optimizers.Adam(),
-loss="sparse_categorical_crossentropy",
-metrics=['accuracy'])
-
-initial_epochs = 4
-print("Fitting the top layer of the model")
-history = model.fit(train_ds, epochs=initial_epochs, validation_data=validation_ds)
-
-
-# %%
-acc = history.history['accuracy']
-val_acc = history.history['val_accuracy']
-
-loss = history.history['loss']
-val_loss = history.history['val_loss']
-
-plt.figure(figsize=(8, 8))
-plt.subplot(2, 1, 1)
-plt.plot(acc, label='Training Accuracy')
-plt.plot(val_acc, label='Validation Accuracy')
-plt.legend(loc='lower right')
-plt.ylabel('Accuracy')
-plt.ylim([min(plt.ylim()),1])
-plt.title('Training and Validation Accuracy')
-
-plt.subplot(2, 1, 2)
-plt.plot(loss, label='Training Loss')
-plt.plot(val_loss, label='Validation Loss')
-plt.legend(loc='upper right')
-plt.ylabel('Cross Entropy')
-plt.title('Training and Validation Loss')
-plt.xlabel('epoch')
-plt.show()
-
-# %%
-base_model.trainable = True
-model.summary(show_trainable=True)
-
-model.compile(
-    optimizer=tf.keras.optimizers.Adam(1e-5),  # Low learning rate
-    loss="sparse_categorical_crossentropy",
-    metrics=['accuracy']
+from PIL import Image
+
+# Modell laden
+model = tf.keras.models.load_model('pokemon-model_transferlearning')
+
+class_names = ['Chansey', 'Growlithe', 'Lapras']
+
+def predict(image):
+    image = image.resize((150, 150))
+    img_array = tf.keras.preprocessing.image.img_to_array(image)
+    img_array = np.expand_dims(img_array, axis=0)
+    predictions = model.predict(img_array)
+    score = tf.nn.softmax(predictions[0])
+    return {class_names[i]: float(score[i]) for i in range(3)}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(150, 150)),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="Pokémon Classifier",
+    description="Upload an image of Chansey, Growlithe, or Lapras"
 )
 
-epochs = 1
-print("Fitting the end-to-end model")
-history_fine = model.fit(train_ds, epochs=epochs, validation_data=validation_ds)
-
-
-# %%
-acc += history_fine.history['accuracy']
-val_acc += history_fine.history['val_accuracy']
-
-loss += history_fine.history['loss']
-val_loss += history_fine.history['val_loss']
-
-plt.figure(figsize=(8, 8))
-plt.subplot(2, 1, 1)
-plt.plot(acc, label='Training Accuracy')
-plt.plot(val_acc, label='Validation Accuracy')
-plt.ylim([0.4, 1]) # set the y-axis limits
-plt.plot([initial_epochs-1,initial_epochs-1],
-plt.ylim(), label='Start Fine Tuning')
-plt.legend(loc='lower right')
-plt.title('Training and Validation Accuracy')
-
-plt.subplot(2, 1, 2)
-plt.plot(loss, label='Training Loss')
-plt.plot(val_loss, label='Validation Loss')
-plt.plot([initial_epochs-1,initial_epochs-1],
-plt.ylim(), label='Start Fine Tuning')
-plt.legend(loc='upper right')
-plt.title('Training and Validation Loss')
-plt.xlabel('epoch')
-plt.show()
-
-# %%
-print("Test dataset evaluation")
-model.evaluate(test_ds)
-
-# %%
-image_batch, label_batch = test_ds.as_numpy_iterator().next()
-predictions_in_percentage = model.predict_on_batch(image_batch)
-predictions = np.argmax(predictions_in_percentage, axis=-1)
-print('Predictions:\n', predictions)
-print('Labels:\n', label_batch)
-plt.figure(figsize=(10, 10))
-for i in range(9):
-  ax = plt.subplot(3, 3, i + 1)
-  plt.imshow(image_batch[i].astype("uint8"))
-  plt.title('pred. ' + class_names[predictions[i]] + ' was ' + class_names[label_batch[i]] + ' ' + str(np.round(predictions_in_percentage[i], 2)), fontsize=8)
-  plt.axis("off")
-
-# %%
-model.save('pokemon-model_transferlearning.keras')
-
-
+if __name__ == "__main__":
+    interface.launch()