

"""simple docstring"""




import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
      # Initialising the CNN
      # (Sequential- Building the model layer by layer)
      __UpperCAmelCase			=					models.Sequential()

      # Step 1 - Convolution
      # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
      # (3,3) is the kernel size (filter matrix)
      classifier.add(
          layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
      )

      # Step 2 - Pooling
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Step 3 - Flattening
      classifier.add(layers.Flatten())

      # Step 4 - Full connection
      classifier.add(layers.Dense(units=128, activation='relu'))
      classifier.add(layers.Dense(units=1, activation='sigmoid'))

      # Compiling the CNN
      classifier.compile(
          optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
      )

      # Part 2 - Fitting the CNN to the images

      # Load Trained model weights

      # from keras.models import load_model
      # regressor=load_model('cnn.h5')

      __UpperCAmelCase			=					tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      __UpperCAmelCase			=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      __UpperCAmelCase			=					train_datagen.flow_from_directory(
          'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
      )

      __UpperCAmelCase			=					test_datagen.flow_from_directory(
          'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
      )

      classifier.save('cnn.h5')

      # Part 3 - Making new predictions

      __UpperCAmelCase			=					tf.keras.preprocessing.image.load_img(
          'dataset/single_prediction/image.png', target_size=(64, 64)
      )
      __UpperCAmelCase			=					tf.keras.preprocessing.image.img_to_array(test_image)
      __UpperCAmelCase			=					np.expand_dims(test_image, axis=0)
      __UpperCAmelCase			=					classifier.predict(test_image)
      # training_set.class_indices
      if result[0][0] == 0:
            __UpperCAmelCase			=					'Normal'
      if result[0][0] == 1:
            __UpperCAmelCase			=					'Abnormality detected'












'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase				:      Optional[int]						      ={
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =["VivitImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "VivitModel",
      "VivitPreTrainedModel",
      "VivitForVideoClassification",
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase				:      List[Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



