message

Dockerfile

@@ -1,18 +1,13 @@
-FROM python:2-alpine
+from sklearn.model_selection import train_test_split
 
-COPY ./requirements.txt /app/requirements.txt
+def split_train_dev_test(X, y, test_size, dev_size):
+    # Split into train+dev and test sets first
+    X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=test_size)
 
-WORKDIR /app
+    # Compute actual dev size relative to the combined train+dev set
+    actual_dev_size = dev_size / (1 - test_size)
+
+    # Split the train+dev set into separate training and dev sets
+    X_train, X_dev, y_train, y_dev = train_test_split(X_temp, y_temp, test_size=actual_dev_size)
 
-RUN apk --update add python py-pip openssl ca-certificates py-openssl wget bash linux-headers
-RUN apk --update add --virtual build-dependencies libffi-dev openssl-dev python-dev py-pip build-base \
-  && pip install --upgrade pip \
-  && pip install --upgrade pipenv\
-  && pip install --upgrade -r /app/requirements.txt\
-  && apk del build-dependencies
-
-COPY . /app
-
-ENTRYPOINT [ "python" ]
-
-CMD [ "hello.py" ]
+    return X_train, X_dev, X_test, y_train, y_dev, y_test

README.md

@@ -1,5 +1,6 @@
 # Getting Started with Python on IBM Cloud
 
+
 To get started, we'll take you through a sample Python Flask app, help you set up a development environment, deploy to IBM Cloud and add a Cloudant database.
 
 The following instructions are for deploying the application as a Cloud Foundry application. To deploy as a container to **IBM Cloud Kubernetes Service** instead, [see README-kubernetes.md](README-kubernetes.md)

hello.py

@@ -86,3 +86,18 @@ def shutdown():
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=port, debug=True)
+
+
+
+
+
+# Calculate the total number of samples
+total_samples = len(train_data) + len(test_data) + len(dev_data)
+
+# Get the image dimensions (assuming all images have the same size)
+image_height, image_width = train_data[0].shape[:2]  # Assuming the shape is (height, width, channels)
+
+# Print statements
+print(f"Total number of samples in the dataset: {total_samples}")
+print(f"Size of the images in the dataset: {image_height}x{image_width}")
+

image_resize.py

@@ -0,0 +1,19 @@
+import numpy as np
+from skimage.transform import resize
+
+def resize_images(images, size):
+  """Resizes images to the given size.
+
+  Args:
+    images: A numpy array of images.
+    size: The target size of the images.
+
+  Returns:
+    A numpy array of resized images.
+  """
+
+  resized_images = np.zeros((images.shape[0], size[0], size[1], images.shape[3]))
+  for i in range(images.shape[0]):
+    resized_images[i] = resize(images[i], size, order=3)
+  return resized_images
+

quiz3.py

@@ -0,0 +1,38 @@
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+app = Flask(__name__)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Get the two images from the request
+    image1 = request.files['image1']
+    image2 = request.files['image2']
+
+    # Convert the images to numpy arrays
+    image1_array = np.array(Image.open(image1))
+    image2_array = np.array(Image.open(image2))
+
+    # Preprocess the images
+    image1_array = image1_array / 255.0
+    image1_array = image1_array.reshape(1, 28, 28, 1)
+    image2_array = image2_array / 255.0
+    image2_array = image2_array.reshape(1, 28, 28, 1)
+
+    # Load the trained model
+    model = tf.keras.models.load_model('model.h5')
+
+    # Make predictions on the images
+    prediction1 = model.predict(image1_array)
+    prediction2 = model.predict(image2_array)
+
+    # Check if the predictions are the same
+    if np.argmax(prediction1) == np.argmax(prediction2):
+        return jsonify({'is_same_digit': True})
+    else:
+        return jsonify({'is_same_digit': False})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)