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generate_image.py
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generate_image.py
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from PIL import Image, ImageFont, ImageDraw
from wordcloud import WordCloud, STOPWORDS
from textblob import TextBlob # sentiment analysis
import tweepy, twitter_credentials
import textwrap # not used currently, implement in future
import re, os # regex / saving and loading tweets
import numpy as np # numerical python library
import pandas as pd # store content into dataframes
import sys # running python file with args, remove later
# Twitter API Client
def getClient():
client = tweepy.Client(bearer_token=twitter_credentials.bearer_token,
consumer_key=twitter_credentials.consumer_key,
consumer_secret=twitter_credentials.consumer_secret,
access_token=None, access_token_secret=None)
return client
# Return user information
def getUserInfo(user):
client = getClient()
user = client.get_user(username=user)
return user.data
# Return recent tweets of user
def getUserRecentTweets(id):
client = getClient()
user_tweets = client.get_users_tweets(id=id,
tweet_fields=['public_metrics,created_at'],
exclude=['retweets', 'replies'],
max_results=100,
#start_time = '2021-09-02T00:00:00.000Z'
)
return user_tweets
# Store recent user tweets in file
def storeUserTweets(username, user_tweets):
file_path = 'user_tweets/' + username + '.txt'
# user has tweets
if user_tweets.data is not None and len(user_tweets.data) > 0:
# user tweets not stored in file
if not os.path.exists(file_path):
# create new file
file = open(file_path, 'w', encoding='utf-8')
# write each tweet into new file
for x in user_tweets.data:
file.write(cleanTweet(str(x)) + '\n')
file.close()
return True
else:
# return as error in future
# print("User tweets file already exists")
return False
else:
# user has no tweets
# print("No tweets")
return False
# Remove special characters and hyperlinks
# Modified code from freeCodeCamp.org
def cleanTweet(tweet):
# replace ’ with '
tweet = tweet.replace('’', '\'')
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z' \t])|(\w+:\/\/\S+)", " ", tweet).split())
# Add public metrics to dataframe
def tweetsToDataFrame(tweets):
# Create new dataframe with tweet text
df = pd.DataFrame(
data=[tweet.text for tweet in tweets], columns=['tweets'])
# Create columns for metrics
df['retweet_count'] = np.array(
[tweet.public_metrics.get('retweet_count') for tweet in tweets])
df['reply_count'] = np.array(
[tweet.public_metrics.get('reply_count') for tweet in tweets])
df['like_count'] = np.array(
[tweet.public_metrics.get('like_count') for tweet in tweets])
df['quote_count'] = np.array(
[tweet.public_metrics.get('quote_count') for tweet in tweets])
df['created_at'] = np.array([tweet.created_at for tweet in tweets])
return df
# Sentiment analysis, returns
# -1 for negative
# 0 for neutral
# 1 for positive
def analyse_sentiment(tweet):
analysis = TextBlob(cleanTweet(tweet))
if analysis.sentiment.polarity > 0:
return 1
elif analysis.sentiment.polarity == 0:
return 0
else:
return -1
# Watermark text
tweet_wrapped_watermark = ["@TweetWrapped"]
# Fonts used in images
global_font = {
"title": ImageFont.truetype("fonts/theboldfont.ttf", 70),
"text": ImageFont.truetype("fonts/coolvetica-rg.otf", 60),
"number": ImageFont.truetype("fonts/theboldfont.ttf", 100),
"watermark": ImageFont.truetype("fonts/theboldfont.ttf", 40)
}
# Font colours
global_font_colour = {
"title": (228, 179, 143),
"text": (179, 145, 143),
"number": (192, 222, 106),
"watermark": (228, 179, 143)
}
# Text position and spacing
# global_text_pos = {
# "x": 50,
# "y": 50,
# "spacer": 50
# }
# Larger text position and spacing
global_text_pos = {
"x": 100,
"y": 100,
"spacer": 100
}
# Generate image 1
def generate_highest_metrics_image(username, most_likes, most_retweets, most_quotes):
# Open black image
img = Image.open("img/templates/purple_1000x1000.png")
draw = ImageDraw.Draw(img)
# Template size
image_width, image_height = img.size
font = global_font
font_colour = global_font_colour
x_pos = global_text_pos["x"]
y_pos = global_text_pos["y"]
spacer = global_text_pos["spacer"]
# Content
if most_likes > 1000:
popularity_txt = "You're Popular!"
elif most_likes > 500:
popularity_txt = "You're Growing!"
elif most_likes > 100:
popularity_txt = "You're Doing OK!"
elif most_likes > 10:
popularity_txt = "You're Doing Meh."
else:
popularity_txt = "You're not popular :("
title_text = [username + ",", popularity_txt]
metrics_text = ["Most Likes", "Most Retweets", "Most Quotes"]
metrics_values = [str(most_likes), str(most_retweets), str(most_quotes)]
# Draw title
draw.text((x_pos, y_pos), title_text[0],
font_colour["title"], font=font["title"])
draw.text((x_pos, y_pos + spacer*1.1),
title_text[1], font_colour["title"], font=font["title"])
# Draw metric text
draw.text((x_pos, y_pos + spacer*3),
metrics_text[0], font_colour["text"], font=font["text"])
draw.text((x_pos, y_pos + spacer*4.5),
metrics_text[1], font_colour["text"], font=font["text"])
draw.text((x_pos, y_pos + spacer*6),
metrics_text[2], font_colour["text"], font=font["text"])
# Width to right align
num_width_0 = font["number"].getsize(metrics_values[0])[0]
num_width_1 = font["number"].getsize(metrics_values[1])[0]
num_width_2 = font["number"].getsize(metrics_values[2])[0]
# Draw metric values
temp_x_pos = image_width - x_pos - num_width_0
draw.text((temp_x_pos, y_pos + spacer*3),
metrics_values[0], font_colour["number"], font=font["number"])
temp_x_pos = image_width - x_pos - num_width_1
draw.text((temp_x_pos, y_pos + spacer*4.5),
metrics_values[1], font_colour["number"], font=font["number"])
temp_x_pos = image_width - x_pos - num_width_2
draw.text((temp_x_pos, y_pos + spacer*6),
metrics_values[2], font_colour["number"], font=font["number"])
# Draw watermark
draw.text((image_width - 350, image_height - 60),
tweet_wrapped_watermark[0], font_colour["title"], font=font["watermark"])
# Save image
img.save("img/outputs/highest_metrics/" +
username + ".png")
#print("Created highest metrics image.")
# Generate image 2
def generate_word_cloud_image(username):
# Get user data
text = open('user_tweets\\' + username + '.txt',
'r', encoding='utf-8').read()
# Stop words, add 'gt' to it
stopwords = STOPWORDS.add('gt')
# Mask
custom_mask = np.array(Image.open(
'img\\masks\\twitter_logo_1000x1000.png'))
font = 'fonts\\SFProDisplay-Light.ttf'
# WordCloud attributes
wordCloud = WordCloud(
width=1000, height=1000,
font_path=font,
mask=custom_mask,
stopwords=stopwords,
background_color=(80, 54, 89),
color_func=lambda *args, **kwargs: (199, 219, 115), # text colour
include_numbers=False
# margin = 10, background_color = None, mode = 'RGBA',
)
# Generate
wordCloud.generate(text)
# Use colour of mask image
## image_colours = ImageColorGenerator(custom_mask)
## wordCloud.recolor(color_func = image_colours)
# Store to file
wordCloud.to_file(
'img\\outputs\\word_clouds\\' + username + '.png')
# Open pre-gen word cloud
img = Image.open(
"img/outputs/word_clouds/" + username + ".png")
draw = ImageDraw.Draw(img)
# Template size
image_width, image_height = img.size
font = global_font
font_colour = global_font_colour
x_pos = global_text_pos["x"]
y_pos = global_text_pos["y"]
spacer = global_text_pos["spacer"]
# Content
title_text = ["What you're Tweeting."]
#title_text = [username + ",", "Tweets Visualized."]
# Draw title
# Since word cloud image is large, move text away from it
draw.text((x_pos-25, y_pos-25), title_text[0],
font_colour["title"], font=font["title"])
#draw.text((x_pos, y_pos + spacer), title_text[1], font_colour["title"], font = font["title"])
# Draw watermark
draw.text((image_width - 350, image_height - 60),
tweet_wrapped_watermark[0], font_colour["title"], font=font["watermark"])
# Save
img.save("img/outputs/word_clouds/" + username + ".png")
#print("Created word cloud image.")
# Generate image 3
def generate_likes_performance_image(username, likes_performance):
# Open black image
img = Image.open("img/templates/purple_1000x1000.png")
draw = ImageDraw.Draw(img)
# Template size
image_width, image_height = img.size
font = global_font
font_colour = global_font_colour
x_pos = global_text_pos["x"]
y_pos = global_text_pos["y"]
spacer = global_text_pos["spacer"]
lp_title_text = ["Get Any Big Tweets?"] # LP = 'likes performance'
lp_text = ["> 100 likes.", "> 500 likes.",
"> 1,000 likes.", "> 10,000 likes."]
lp_values = [str(likes_performance[100]), str(likes_performance[500]), str(
likes_performance[1000]), str(likes_performance[10000])]
lp_values_additional_text = ["tweets"]
# Likes Performance section
# Right align
#title_width = font["title"].getsize(lp_title_text[0])[0]
#temp_x_pos = image_width - x_pos - title_width
# Width to right align
txt_width_0 = font["text"].getsize(lp_text[0])[0]
txt_width_1 = font["text"].getsize(lp_text[1])[0]
txt_width_2 = font["text"].getsize(lp_text[2])[0]
txt_width_3 = font["text"].getsize(lp_text[3])[0]
# Move base-level y-pos down
# not relevant with 4 images, so ignore for now
#y_pos = image_height/1.8
# Draw title
# Right-align not used currently
# use temp_x_pos for right-align
draw.text((x_pos, y_pos),
lp_title_text[0], font_colour["title"], font=font["title"])
# Draw lp text
temp_x_pos = image_width - x_pos - txt_width_0
draw.text((temp_x_pos, y_pos + spacer*1.8),
lp_text[0], font_colour["text"], font=font["text"])
temp_x_pos = image_width - x_pos - txt_width_1
draw.text((temp_x_pos, y_pos + spacer*3.3),
lp_text[1], font_colour["text"], font=font["text"])
temp_x_pos = image_width - x_pos - txt_width_2
draw.text((temp_x_pos, y_pos + spacer*4.8),
lp_text[2], font_colour["text"], font=font["text"])
temp_x_pos = image_width - x_pos - txt_width_3
draw.text((temp_x_pos, y_pos + spacer*6.3),
lp_text[3], font_colour["text"], font=font["text"])
# Draw lp values
draw.text((x_pos, y_pos + spacer*1.75),
lp_values[0], font_colour["number"], font=font["number"])
draw.text((x_pos, y_pos + spacer*3.25),
lp_values[1], font_colour["number"], font=font["number"])
draw.text((x_pos, y_pos + spacer*4.75),
lp_values[2], font_colour["number"], font=font["number"])
draw.text((x_pos, y_pos + spacer*6.25),
lp_values[3], font_colour["number"], font=font["number"])
# Draw additional text 'twitter' next to lp values
value_width_1 = font["title"].getsize(lp_values[0])[0]
value_width_2 = font["title"].getsize(lp_values[1])[0]
value_width_3 = font["title"].getsize(lp_values[2])[0]
value_width_4 = font["title"].getsize(lp_values[3])[0]
draw.text((value_width_1 + x_pos + 50, y_pos + spacer*1.8),
lp_values_additional_text[0], font_colour["text"], font=font["text"])
draw.text((value_width_2 + x_pos + 50, y_pos + spacer*3.3),
lp_values_additional_text[0], font_colour["text"], font=font["text"])
draw.text((value_width_3 + x_pos + 50, y_pos + spacer*4.8),
lp_values_additional_text[0], font_colour["text"], font=font["text"])
draw.text((value_width_4 + x_pos + 50, y_pos + spacer*6.3),
lp_values_additional_text[0], font_colour["text"], font=font["text"])
# Draw watermark
draw.text((image_width - 350, image_height - 60),
tweet_wrapped_watermark[0], font_colour["title"], font=font["watermark"])
# Save image
img.save("img/outputs/likes_performance/" +
username + ".png")
#print("Created likes performance image.")
# Generate image 4
def generate_sentiment_analysis_image(username, sentiment):
# Open black image
img = Image.open("img/templates/purple_1000x1000.png")
draw = ImageDraw.Draw(img)
# Template size
image_width, image_height = img.size
font = global_font
font_colour = global_font_colour
x_pos = global_text_pos["x"]
y_pos = global_text_pos["y"]
spacer = global_text_pos["spacer"]
# Classify based on numerical sentiment value (-100 to 100)
if sentiment > 10:
sentiment_class = "SUPER HAPPY!" # EMOJI
sentiment_emoji = Image.open(
"img/emojis/grinning-face-with-sweat_1f605.png")
elif sentiment > 5:
sentiment_class = "HAPPY!"
sentiment_emoji = Image.open(
"img/emojis/beaming-face-with-smiling-eyes_1f601.png")
elif sentiment > 0:
sentiment_class = "KINDA HAPPY..." # EMOJI
sentiment_emoji = Image.open(
"img/emojis/emoji-upside-down-face_1f643.png")
elif sentiment > -5:
sentiment_class = "SAD!"
sentiment_emoji = Image.open(
"img/emojis/face-with-head-bandage_1f915.png")
else:
sentiment_class = "DOWN BAD!"
sentiment_emoji = Image.open("img\emojis\sleepy-face_1f62a.png")
# Resize emoji
(emoji_width, emoji_height) = (
sentiment_emoji.width/2, sentiment_emoji.height/2)
sentiment_emoji = sentiment_emoji.resize(
(int(emoji_width), int(emoji_height)))
# Sentiment title
sentiment_title = ["How were you feeling?", "Happy or Sad?"]
# Sentiment text
sentiment_text = ["Emotionally your tweets", "scored", str(
sentiment), "meaning you", "were...", sentiment_class]
# Move base-level y-pos down
# not relevant for 4 images, so ignore
# y_pos = image_height/1.5
# Draw sentiment title
# Not using right-align
# use temp_x_pos for right-align
#title_width = font["title"].getsize(sentiment_title[0])[0]
#temp_x_pos = image_width - x_pos - title_width
draw.text((x_pos, y_pos),
sentiment_title[0], font_colour["title"], font=font["title"])
draw.text((x_pos, y_pos + spacer*1.1),
sentiment_title[1], font_colour["title"], font=font["title"])
# Item 1
# Draw text 1
draw.text((x_pos, y_pos + spacer * 3),
sentiment_text[0], font_colour["text"], font=font["text"])
# Item 2
# Draw text 2
draw.text((x_pos, y_pos + spacer * 4.25),
sentiment_text[1], font_colour["text"], font=font["text"])
# Item 3
# Draw sentiment value
# Get width of text to prevent overlap
txtwrap_x_pos = font["text"].getsize(sentiment_text[1])[0] + x_pos + 25
draw.text((txtwrap_x_pos, y_pos + spacer * 4.2),
sentiment_text[2], font_colour["number"], font=font["number"])
# Item 4
# Draw text 4
# Move text to be positioned after value number
txtwrap_x_pos = font["number"].getsize(sentiment_text[2])[
0] + txtwrap_x_pos + 25
draw.text((txtwrap_x_pos, y_pos + spacer * 4.25),
sentiment_text[3], font_colour["text"], font=font["text"])
# Item 5
# Draw text 5
draw.text((x_pos, y_pos + spacer * 5.5),
sentiment_text[4], font_colour["text"], font=font["text"])
# Item 6
# Draw sentiment class
temp_x_pos = font["text"].getsize(sentiment_text[4])[0] + x_pos + 25
draw.text((temp_x_pos, y_pos + spacer * 5.5),
sentiment_text[5], font_colour["number"], font=font["number"])
# If sentiment class is too long, draw emoji on new line
# e.g 'SUPER HAPPY!' is too long
if sentiment_class == "SUPER HAPPY!" or sentiment_class == "KINDA HAPPY...":
# Draw sentiment emoji on new line
img.paste(sentiment_emoji, (x_pos, int(y_pos + spacer * 6.8)))
else:
# Draw sentiment emoji after sentiment class
txtwrap_x_pos = font["number"].getsize(sentiment_text[5])[
0] + temp_x_pos + 25
img.paste(sentiment_emoji, (txtwrap_x_pos, int(y_pos + spacer * 5.5)))
# Draw watermark
draw.text((image_width - 350, image_height - 60),
tweet_wrapped_watermark[0], font_colour["title"], font=font["watermark"])
# Save
img.save("img/outputs/sentiment_analysis/" + username + ".png")
#print("Created sentiment analysis image.")
# Test image gen without calling api
#if __name__ == "__main__":
# # main(sys.argv[1])
# username = "FinessTV"
# most_likes = 5
# most_retweets = 10
# most_quotes = 2
# likes_performance = {
# 100: 19,
# 500: 3,
# 1000: 0,
# 10000: 0
# }
# sentiment = 11
# generate_highest_metrics_image(username, most_likes, most_retweets, most_quotes)
# generate_word_cloud_image(username)
# generate_likes_performance_image(username, likes_performance)
# generate_sentiment_analysis_image(username, sentiment)
# Main method called by stream_mentions.py
def main(username):
# Get user info, such as id
user = getUserInfo(username)
# Get tweets of user by id
try:
user_tweets = getUserRecentTweets(user.id)
except Exception as e:
print(e)
return False
# If user has already been processed, i.e. already used bot...
# storeUserTweets will return false, and program will stop...
# else, continue
if storeUserTweets(username, user_tweets):
# Get user stats
df = tweetsToDataFrame(user_tweets.data)
# Carry out sentiment analysis
df['sentiment'] = np.array([analyse_sentiment(tweet)
for tweet in df['tweets']])
# Get average sentiment of all user tweets
sentiment = np.average(df['sentiment']) * 100
# Remove repeating demial e.g. 12.11111...
sentiment = float("{0:.2f}".format(sentiment))
# pd.set_option('display.max_rows', 100) # Change how many rows df prints
# print(df.head(100)) # Print dataframe
# print(dir(user_tweets.data)) # What attributes exist
# print(user_tweets.data[0].public_metrics)
# Get highest metrics
most_likes = np.max(df['like_count'])
most_retweets = np.max(df['retweet_count'])
most_quotes = np.max(df['quote_count'])
# How many tweets with more than X likes
likes_performance = {
100: len(df[df['like_count'] > 100]),
# 500 likes metric only used in 4 image format
500: len(df[df['like_count'] > 500]),
1000: len(df[df['like_count'] > 1000]),
10000: len(df[df['like_count'] > 10000])
}
# Old 2 image format #
# generate_highest_metrics_and_likes_performance_image(username,
# most_likes,
# most_retweets,
# most_quotes,
# likes_performance)
# generate_word_clouds_and_sentiment_analysis_image(username, sentiment)
# New 4 image format #
# Generate image 1 - Highest metrics
generate_highest_metrics_image(
username, most_likes, most_retweets, most_quotes)
# Generate image 2 - Word cloud
generate_word_cloud_image(username)
# Generate image 3 - Likes performance
generate_likes_performance_image(username, likes_performance)
# Generate image 4 - Sentiment analysis
generate_sentiment_analysis_image(username, sentiment)
return True
else:
return False