Update app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled44.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/18Lqtn6Wg43WV6woldVaN-GMbpBiOTrrC
+"""
+
+import zipfile
+
+# Download zip file of pizza_steak images
+!wget https://storage.googleapis.com/ztm_tf_course/food_vision/pizza_steak.zip 
+
+# Unzip the downloaded file
+zip_ref = zipfile.ZipFile("pizza_steak.zip", "r")
+zip_ref.extractall()
+zip_ref.close()
+
+import os
+
+# Walk through pizza_steak directory and list number of files
+for dirpath, dirnames, filenames in os.walk("pizza_steak"):
+  print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
+
+# Get the class names (programmatically, this is much more helpful with a longer list of classes)
+import pathlib
+import numpy as np
+data_dir = pathlib.Path("pizza_steak/train/") # turn our training path into a Python path
+class_names = np.array(sorted([item.name for item in data_dir.glob('*')])) # created a list of class_names from the subdirectories
+print(class_names)
+
+# View an image
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import random
+
+def view_random_image(target_dir, target_class):
+  # Setup target directory (we'll view images from here)
+  target_folder = target_dir+target_class
+
+  # Get a random image path
+  random_image = random.sample(os.listdir(target_folder), 1)
+
+  # Read in the image and plot it using matplotlib
+  img = mpimg.imread(target_folder + "/" + random_image[0])
+  plt.imshow(img)
+  plt.title(target_class)
+  plt.axis("off");
+
+  print(f"Image shape: {img.shape}") # show the shape of the image
+
+  return img
+
+# View a random image from the training dataset
+img = view_random_image(target_dir="pizza_steak/train/",
+                        target_class="steak")
+
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Define training and test directory paths
+train_dir = "pizza_steak/train/"
+test_dir = "pizza_steak/test/"
+
+# Plot the validation and training data separately
+def plot_loss_curves(history):
+  """
+  Returns separate loss curves for training and validation metrics.
+  """ 
+  loss = history.history['loss']
+  val_loss = history.history['val_loss']
+
+  accuracy = history.history['accuracy']
+  val_accuracy = history.history['val_accuracy']
+
+  epochs = range(len(history.history['loss']))
+
+  # Plot loss
+  plt.plot(epochs, loss, label='training_loss')
+  plt.plot(epochs, val_loss, label='val_loss')
+  plt.title('Loss')
+  plt.xlabel('Epochs')
+  plt.legend()
+
+  # Plot accuracy
+  plt.figure()
+  plt.plot(epochs, accuracy, label='training_accuracy')
+  plt.plot(epochs, val_accuracy, label='val_accuracy')
+  plt.title('Accuracy')
+  plt.xlabel('Epochs')
+  plt.legend();
+
+# Create ImageDataGenerator training instance with data augmentation
+train_datagen_augmented = ImageDataGenerator(rescale=1/255.,
+                                             rotation_range=20, # rotate the image slightly between 0 and 20 degrees (note: this is an int not a float)
+                                             shear_range=0.2, # shear the image
+                                             zoom_range=0.2, # zoom into the image
+                                             width_shift_range=0.2, # shift the image width ways
+                                             height_shift_range=0.2, # shift the image height ways
+                                             horizontal_flip=True) # flip the image on the horizontal axis
+
+# Create ImageDataGenerator training instance without data augmentation
+train_datagen = ImageDataGenerator(rescale=1/255.) 
+
+# Create ImageDataGenerator test instance without data augmentation
+test_datagen = ImageDataGenerator(rescale=1/255.)
+
+# Import data and augment it from training directory
+print("Augmented training images:")
+train_data_augmented = train_datagen_augmented.flow_from_directory(train_dir,
+                                                                   target_size=(224, 224),
+                                                                   batch_size=32,
+                                                                   class_mode='binary',
+                                                                   shuffle=False) # Don't shuffle for demonstration purposes, usually a good thing to shuffle
+
+# Create non-augmented data batches
+print("Non-augmented training images:")
+train_data = train_datagen.flow_from_directory(train_dir,
+                                               target_size=(224, 224),
+                                               batch_size=32,
+                                               class_mode='binary',
+                                               shuffle=False) # Don't shuffle for demonstration purposes
+
+print("Unchanged test images:")
+test_data = test_datagen.flow_from_directory(test_dir,
+                                             target_size=(224, 224),
+                                             batch_size=32,
+                                             class_mode='binary')
+
+# Import data and augment it from directories
+train_data_augmented_shuffled = train_datagen_augmented.flow_from_directory(train_dir,
+                                                                            target_size=(224, 224),
+                                                                            batch_size=32,
+                                                                            class_mode='binary',
+                                                                            shuffle=True)
+
+# Make the creating of our model a little easier
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Activation
+from tensorflow.keras import Sequential
+
+# Create a CNN model (same as Tiny VGG but for binary classification - https://poloclub.github.io/cnn-explainer/ )
+model_8 = Sequential([
+  Conv2D(10, 3, activation='relu', input_shape=(224, 224, 3)), # same input shape as our images
+  Conv2D(10, 3, activation='relu'),
+  MaxPool2D(),
+  Conv2D(10, 3, activation='relu'),
+  Conv2D(10, 3, activation='relu'),
+  MaxPool2D(),
+  Flatten(),
+  Dense(1, activation='sigmoid')
+])
+
+# Compile the model
+model_8.compile(loss="binary_crossentropy",
+                optimizer=tf.keras.optimizers.Adam(),
+                metrics=["accuracy"])
+
+# Fit the model
+history_8 = model_8.fit(train_data_augmented_shuffled,
+                        epochs=30,
+                        steps_per_epoch=len(train_data_augmented_shuffled),
+                        validation_data=test_data,
+                        validation_steps=len(test_data))
+
+"""# Making a prediction with our trained model"""
+
+# Classes we're working with
+print(class_names)
+
+# View our example image
+!wget https://github.com/mrdbourke/tensorflow-deep-learning/blob/ff0a93f68915e85bcb509a0c636d16f4567fbf8a/images/03-steak.jpeg
+steak = mpimg.imread("03-steak.jpeg")
+plt.imshow(steak)
+plt.axis(False);
+
+# Check the shape of our image
+steak.shape
+
+# Add an extra axis
+print(f"Shape before new dimension: {steak.shape}")
+steak = tf.expand_dims(steak, axis=0) # add an extra dimension at axis 0
+#steak = steak[tf.newaxis, ...] # alternative to the above, '...' is short for 'every other dimension'
+print(f"Shape after new dimension: {steak.shape}")
+steak
+
+# Make a prediction on custom image tensor
+pred = model_8.predict(steak)
+pred
+
+# Load in and preprocess our custom image
+#steak = pred_and_plot("03-steak.jpeg")
+#steak
+
+# Create a function to import an image and resize it to be able to be used with our model
+def load_and_prep_image(filename, img_shape=224):
+  """
+  Reads an image from filename, turns it into a tensor
+  and reshapes it to (img_shape, img_shape, colour_channel).
+  """
+  # Read in target file (an image)
+  #img = tf.io.read_file(filename)
+  #print(img)
+  # Decode the read file into a tensor & ensure 3 colour channels 
+  # (our model is trained on images with 3 colour channels and sometimes images have 4 colour channels)
+  img = filename
+  #img = tf.image.decode_image(img, channels=3)
+  
+  # Resize the image (to the same size our model was trained on)
+  img = tf.image.resize(img, size = [img_shape, img_shape])
+  print(img.shape)
+  # Rescale the image (get all values between 0 and 1)
+  img = img/255.
+  return img
+
+def pred_and_plot(filename):
+  """
+  Imports an image located at filename, makes a prediction on it with
+  a trained model and plots the image with the predicted class as the title.
+  """
+  # Import the target image and preprocess it
+  # print(filename.shape)
+  img = load_and_prep_image(filename)
+  
+  # Make a prediction
+  pred = model_8.predict(tf.expand_dims(img, axis=0))
+  class_names = ['Pizza🍕','Steak🥩']
+  # Get the predicted class
+  pred_class = class_names[int(tf.round(pred)[0][0])]
+  return pred_class
+  # Plot the image and predicted class
+ # plt.imshow(img)
+  #plt.title(f"Prediction: {pred_class}")
+  #plt.axis(False);
+
+# Test our model on a custom image
+#pred_and_plot("03-steak.jpeg")
+
+import cv2
+val = cv2.imread("03-steak.jpeg")
+val.shape
+
+from pathlib import Path
+# Create a list of example inputs to our Gradio demo
+test_data_paths = list(Path(test_dir).glob("*/*.jpg"))
+
+example_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=3)]
+example_list
+
+# Import/install Gradio 
+try:
+    import gradio as gr
+except: 
+    !pip -q install gradio
+    import gradio as gr
+    
+print(f"Gradio version: {gr.__version__}")
+
+import gradio as gr
+
+# Create title, description and article strings
+title = "FoodVision Mini 🥩🍕"
+description = "A CNN model to classify images of food as pizza or steak ."
+
+
+# Create the Gradio demo
+demo = gr.Interface(fn=pred_and_plot, # mapping function from input to output
+                    inputs=["image"], # what are the inputs?
+                    outputs=["text"], # our fn has two outputs, therefore we have two outputs
+                    examples=example_list, 
+                    title=title,
+                    description=description)
+
+# Launch the demo!
+demo.launch(inline=True) # generate a publically shareable URL?
+