tiaplagata

# Function to generate word cloud with basic contour
def generate_better_wordcloud(data, title, mask=None):
    cloud = WordCloud(scale=3,
                      max_words=150,
                      colormap='RdYlGn',
                      mask=mask,
                      background_color='white',
                      stopwords=stopwords,
                      collocations=True,
                      contour_color='black',

tiaplagata

from wordcloud import ImageColorGenerator

#Create the mask
colosseum_mask = np.array(Image.open('/Users/tiaplagata/Downloads/colosseum.jpg'))

#Grab the mask colors
colors = ImageColorGenerator(colosseum_mask)

#Instantiate the wordcloud using color_func argument
cloud = WordCloud(mask=colosseum_mask,

tiaplagata

# Function to generate word cloud with dark red contour
def generate_better_wordcloud(data, title, mask=None):
    cloud = WordCloud(scale=3,
                      max_words=150,
                      colormap='RdYlGn',
                      mask=mask,
                      background_color='white',
                      stopwords=stopwords,
                      collocations=True,
                      contour_color='#5d0f24',

tiaplagata

# A similar function, but using the mask
def generate_better_wordcloud(data, title, mask=None):
    cloud = WordCloud(scale=3,
                      max_words=150,
                      colormap='RdYlGn',
                      mask=mask,
                      background_color='white',
                      stopwords=stopwords,
                      collocations=True).generate_from_text(data)
    plt.figure(figsize=(10,8))

tiaplagata

# Create an array from the image you want to use as a mask
## Your file path will look different
rome_mask = np.array(Image.open('/Users/tiaplagata/Downloads/italy.jpg'))

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#Define a list of stop words
stopwords = ['private', 'tour', 'transfer', 'guide', 'skip', 'line',
             'skiptheline', 'vice', 'versa']

#A function to generate the word cloud from text
def generate_basic_wordcloud(data, title):
    cloud = WordCloud(width=400,
                      height=330,
                      max_words=150,
                      colormap='tab20c',

tiaplagata

# Store our document term matrix data for Rome 
data = dtm.transpose()['Rome, Italy'].sort_values(ascending=False)

# Generate the word cloud from frequencies
wc = WordCloud().generate_from_frequencies(data)

plt.imshow(wc)
plt.axis('off')
plt.show()

tiaplagata

#Give our Rome corpus a variable name
rome_corpus = df.lemmatized[10]

#Instantiate wordcloud object and use method to feed it our corpus
wc = WordCloud().generate_from_text(rome_corpus)

#Use matplotlib.pyplot to display the fitted wordcloud
#Turn axis off to get rid of axis numbers
plt.imshow(wc)
plt.axis('off')

tiaplagata

from wordcloud import WordCloud, ImageColorGenerator
from PIL import Image
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np

tiaplagata

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Create data generators
# File path = path to train/test/val folders respectively
# Use a target size of 224x224 px for each image (or whatever size you choose)
# Batch size = total number of images in the train set, test set, val set respectively
# Ensure class_mode is binary
train_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(
                                                    '/content/chest_xray/train',
                                                     target_size=(224, 224),