pip install --upgrade pip
pip install 'model-card-toolkit>=1.0.0'
pip install 'tensorflow>=2.3.1'
pip install 'tensorflow-datasets>=4.8.2'

import tensorflow as tf
import numpy as np
import model_card_toolkit as mct
from model_card_toolkit.documentation.examples import cats_vs_dogs
from model_card_toolkit.utils.graphics import figure_to_base64str
import tempfile
import matplotlib.pyplot as plt
from IPython import display
import requests
import os
import zipfile

URL = 'https://storage.googleapis.com/cats_vs_dogs_model/cats_vs_dogs_model.zip'
BASE_PATH = tempfile.mkdtemp()
ZIP_PATH = os.path.join(BASE_PATH, 'cats_vs_dogs_model.zip')
MODEL_PATH = os.path.join(BASE_PATH,'cats_vs_dogs_model')

r = requests.get(URL, allow_redirects=True)
open(ZIP_PATH, 'wb').write(r.content)

with zipfile.ZipFile(ZIP_PATH, 'r') as zip_ref:
    zip_ref.extractall(BASE_PATH)

model = tf.keras.models.load_model(MODEL_PATH)

WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
2023-10-03 09:12:20.736066: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

def compute_accuracy(data):
  x = np.stack(data['examples'])
  y = np.asarray(data['labels'])
  _, metric = model.evaluate(x, y)
  return metric

examples = cats_vs_dogs.get_data()
print('num validation examples:', len(examples['combined']['examples']))
print('num cat examples:', len(examples['cat']['examples']))
print('num dog examples:', len(examples['dog']['examples']))

num validation examples: 320
num cat examples: 149
num dog examples: 171
2023-10-03 09:12:30.081069: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

accuracy = compute_accuracy(examples['combined'])
cat_accuracy = compute_accuracy(examples['cat'])
dog_accuracy = compute_accuracy(examples['dog'])

10/10 [==============================] - 2s 77ms/step - loss: 0.0794 - binary_accuracy: 0.9812
5/5 [==============================] - 1s 74ms/step - loss: 0.0608 - binary_accuracy: 0.9933
6/6 [==============================] - 0s 65ms/step - loss: 0.0956 - binary_accuracy: 0.9708

# https://github.com/tensorflow/model-card-toolkit/blob/master/model_card_toolkit/model_card_toolkit.py
model_card_dir = tempfile.mkdtemp()
toolkit = mct.ModelCardToolkit(model_card_dir)

# https://github.com/tensorflow/model-card-toolkit/blob/master/model_card_toolkit/model_card.py
model_card = toolkit.scaffold_assets()

model_card.model_details.name = 'Fine-tuned MobileNetV2 Model for Cats vs. Dogs'
model_card.model_details.overview = (
    'This model distinguishes cat and dog images. It uses the MobileNetV2 '
    'architecture (https://arxiv.org/abs/1801.04381) and is trained on the '
    'Cats vs Dogs dataset '
    '(https://www.tensorflow.org/datasets/catalog/cats_vs_dogs). This model '
    'performed with high accuracy on both Cat and Dog images.'
)
model_card.model_details.owners = [
  mct.Owner(name='Model Cards Team', contact='model-cards@google.com')
]
model_card.model_details.version = mct.Version(name='v1.0', date='08/28/2020')
model_card.model_details.references = [
    mct.Reference(reference='https://www.tensorflow.org/guide/keras/transfer_learning'),
    mct.Reference(reference='https://arxiv.org/abs/1801.04381'),
]
model_card.model_details.licenses = [mct.License(identifier='Apache-2.0')]
model_card.model_details.citations = [mct.Citation(citation='https://github.com/tensorflow/model-card-toolkit/blob/master/model_card_toolkit/documentation/examples/Standalone_Model_Card_Toolkit_Demo.ipynb')]

model_card.quantitative_analysis.performance_metrics = [
  mct.PerformanceMetric(type='accuracy', value=str(accuracy)),
  mct.PerformanceMetric(type='accuracy', value=str(cat_accuracy), slice='cat'),
  mct.PerformanceMetric(type='accuracy', value=str(dog_accuracy), slice='Dog'),
]

model_card.considerations.use_cases = [
    mct.UseCase(description='This model classifies images of cats and dogs.')
]
model_card.considerations.limitations = [
    mct.Limitation(description='This model is not able to classify images of other classes.')
]
model_card.considerations.ethical_considerations = [mct.Risk(
    name=
        'While distinguishing between cats and dogs is generally agreed to be '
        'a benign application of machine learning, harmful results can occur '
        'when the model attempts to classify images that don’t contain cats or '
        'dogs.',
    mitigation_strategy=
        'Avoid application on non-dog and non-cat images.'
)]

# Validation Set Size Bar Chart
fig, ax = plt.subplots()
width = 0.75
rects0 = ax.bar(0, len(examples['combined']['examples']), width, label='Overall')
rects1 = ax.bar(1, len(examples['cat']['examples']), width, label='Cat')
rects2 = ax.bar(2, len(examples['dog']['examples']), width, label='Dog')
ax.set_xticks(np.arange(3))
ax.set_xticklabels(['Overall', 'Cat', 'Dog'])
ax.set_ylabel('Validation Set Size')
ax.set_xlabel('Slices')
ax.set_title('Validation Set Size for Slices')
validation_set_size_barchart = figure_to_base64str(fig)

# Acuracy Bar Chart
fig, ax = plt.subplots()
width = 0.75
rects0 = ax.bar(0, accuracy, width, label='Overall')
rects1 = ax.bar(1, cat_accuracy, width, label='Cat')
rects2 = ax.bar(2, dog_accuracy, width, label='Dog')
ax.set_xticks(np.arange(3))
ax.set_xticklabels(['Overall', 'Cat', 'Dog'])
ax.set_ylabel('Accuracy')
ax.set_xlabel('Slices')
ax.set_title('Accuracy on Slices')
accuracy_barchart = figure_to_base64str(fig)

model_card.model_parameters.data.append(mct.Dataset())
model_card.model_parameters.data[0].graphics.collection = [
  mct.Graphic(name='Validation Set Size', image=validation_set_size_barchart),
]
model_card.quantitative_analysis.graphics.collection = [
  mct.Graphic(name='Accuracy', image=accuracy_barchart),
]

toolkit.update_model_card(model_card)

# Generate a model card document in HTML (default)
html_doc = toolkit.export_format()

# Display the model card document in HTML
display.display(display.HTML(html_doc))

# Generate a model card document in Markdown
md_path = os.path.join(model_card_dir, 'template/md/default_template.md.jinja')
md_doc = toolkit.export_format(template_path=md_path, output_file='model_card.md')

# Display the model card document in Markdown
display.display(display.Markdown(md_doc))