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import glob
from pathlib import Path
import numpy as np
import pandas as pd
import regex as re
from matplotlib import pyplot as plt, use as plt_use
from textclassifier import TextClassifier as tc
plt_use('Agg')
# from functions import functions as f
# import time
TOPIC = "merged_topic"
SELECTED_COLUMN_DICT = {
TOPIC: ['tweet', 'main_topic', 'sub_topic', 'synonym_topic', 'cos_sim_topic', 'merged_topic'],
'sentiment': ['tweet', 'sentiment'],
'merged_target': ['tweet', 'target', 'synonym_target', 'cos_sim_target', 'merged_target']
}
USER_LIST = ['jimmieakesson', 'BuschEbba', 'annieloof', 'JohanPehrson', 'bolund', 'martastenevi', 'SwedishPM',
'dadgostarnooshi']
USER_NAMES = ['Jimmie Åkesson', 'Ebba Busch', 'Annie Lööf', 'Johan Pehrson', 'Per Bolund', 'Märta Stenevi',
'Magdalena Andersson', 'Nooshi Dadgostar']
CHOICE_LIST = ['Topic', 'Sentiment', 'Target']
# PLOT_CHOICES_DICT = {'Topic': 'sub_topic', 'Sentiment': 'sentiment', 'Target': 'target'} I just changed its pavue
# to merged target and merged topic
PLOT_CHOICES_DICT = {'Topic': TOPIC, 'Sentiment': 'sentiment', 'Target': 'merged_target'}
PLOT_CHOICES_REVERSE_DICT = {TOPIC: 'Topic', 'sentiment': 'Sentiment', 'merged_target': 'Target'}
# PLOT_CHOICES_REVERSE_DICT= {'sub_topic':'Topic', 'sentiment':'Sentiment' , 'target':'Target'}
UserNameDict = dict(zip(['Jimmie Åkesson', 'Ebba Busch', 'Annie Lööf', 'Johan Pehrson', 'Per Bolund',
'Märta Stenevi', 'Magdalena Andersson', 'Nooshi Dadgostar'], USER_LIST))
Columns = ['username', 'nlikes', 'nreplies', 'nretweets', 'main_topic', 'sub_topic', 'sentiment', 'target', 'tweet',
'date', 'urls', 'id', 'class_tuple', 'user_id']
# NUM_TWEETS = 1000
LIMIT = 0.04
def show_all_stats(see_full_stats):
dataframe = pd.read_csv("{}/data/twitterdata.csv".format(tc.ROOT_PATH))
if see_full_stats:
return dataframe
else:
return pd.DataFrame()
def fix_choices_correct_order(choices):
list_choices = [x for x in Columns if x in choices]
return list_choices
def match_name_lower_case(user_names):
users = []
for N in user_names:
users.append(UserNameDict[N])
return users
def convert_plot_choices(plot_choices):
return [PLOT_CHOICES_DICT[choice] for choice in plot_choices]
def convert_back_plot_choices(plot_choices_raw):
return [PLOT_CHOICES_REVERSE_DICT[choice] for choice in plot_choices_raw]
def main(from_date,
to_date,
usr_name_choices,
plot_choice,
save_selected,
rb1, rb2, rb3, rb4, rb5, rb6, rb7, rb8,
v1, v2, v3, v4, v5, v6, v7, v8,
s1, s2, s3, s4, s5, s6, s7, s8
):
save_file_bool = s1, s2, s3, s4, s5, s6, s7, s8
# Describe what save_file_bool is for: if you want to save the dataframe to a file, this is the boolean for that
def add_pie_chart(df, leaders, plot_choices):
df_list = []
pie_charts = []
return_list = []
leader_bool_list, plot_bool_list = convert_to_boolean(leaders, convert_back_plot_choices(plot_choices))
bool_list = []
for leader in leader_bool_list:
if leader:
for choice in plot_bool_list:
bool_list.append(choice)
else:
for i in range(len(plot_bool_list)):
bool_list.append(False)
for user in USER_NAMES: # leaders:
df_list.append((df.loc[df["username"] == UserNameDict[user]], user))
for db in df_list:
for col in PLOT_CHOICES_REVERSE_DICT: # plot_choices:
if col == 'merged_target':
pie_charts.append(bar(db[0], col + ": " + db[1]))
elif col == 'sentiment':
pie_charts.append(pie_chart(db[0], col, col + ": " + db[1]))
elif col == TOPIC:
pie_charts.append(nested_pie_chart(db[0]))
return pie_charts
def bar(db: pd.DataFrame, title):
"""
This method adds a stacked bar diagram for each target and each sentiment
NOTE: The tweets without any target are not shown in the plot, we just show distribution of tweets that have a
target.
"""
if db.empty:
return None
else:
db['merged_target'] = db["merged_target"].apply(lambda
x: "other" if x == "ERROR_9000" or x == "ERROR_496" else x) # replacing Different Error type with string "other"
db['sentiment'] = db['sentiment'].apply(
lambda x: re.sub('\s+', "", x)) # removing extra spaces in at the end and beginning of the sentiments.
# This can be removed after we remove all unnessary spaces from twitter data
all_targets = ['v', 'mp', 's', 'c', 'l', 'kd', 'm', 'sd', 'Red-Greens', 'The opposition']
db_new = db.loc[db["merged_target"] != "other"] # dataframe with other category removed
percent_target = (len(db_new) / len(db)) * 100
positive = [0] * len(all_targets)
negative = [0] * len(all_targets)
neutral = [0] * len(all_targets)
other = [0] * len(all_targets)
for i, target in enumerate(all_targets):
temp_db = db_new.loc[db_new["merged_target"] == target]
if temp_db.empty:
pass
else:
sent = temp_db['sentiment'].to_list()
positive[i] += sent.count('positive')
negative[i] += sent.count('negative')
neutral[i] += sent.count('neutral')
other[i] += sent.count('other')
font1 = {'family': 'serif', 'color': 'blue', 'size': 10}
fig = plt.figure()
y1 = np.array(positive) / len(db_new) if len(db_new) > 0 else np.array(positive)
y2 = np.array(negative) / len(db_new) if len(db_new) > 0 else np.array(negative)
y3 = np.array(neutral) / len(db_new) if len(db_new) > 0 else np.array(neutral)
y4 = np.array(other) / len(db_new) if len(db_new) > 0 else np.array(other)
plt.bar(all_targets, y1, color='g')
plt.bar(all_targets, y2, bottom=y1, color='r')
plt.bar(all_targets, y3, bottom=(y1 + y2), color='yellow')
plt.bar(all_targets, y4, bottom=(y1 + y2 + y3), color='b')
plt.xticks(rotation=15)
plt.ylim(0, 1)
plt.title(
str(percent_target)[0:4] + "% " + " of tweets have target. " + "Number of tweets with target:" + str(
len(db_new)), loc='right', fontdict=font1)
# plt.xlabel("Targets")
plt.ylabel("Procent")
plt.legend(["positive", "negative", "neutral", "other"])
return fig
def pie_chart(db, col_name, title):
if db.empty:
return None
else:
# df = df[col_name].value_counts()[:5] # Lägg till "Others sedan"
db = pie_chart_input(db, col_name, LIMIT)
labels = db[col_name].to_list()
sizes = db['frequency'].values
# explode = (0, 0.1, 0, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
font1 = {'family': 'serif', 'color': 'blue', 'size': 20}
fig = plt.figure()
plt.pie(sizes, labels=labels, radius=1, autopct='%1.1f%%')
plt.title(title, fontdict=font1)
return fig
def nested_pie_chart(df):
"""
This method adds a nested pie chart. The pie chart shows the sentiment for each target.
:param df: dataframe with data. The selected leaders have non-empty entries in the dataframe.
:return: figure with the nested pie chart.
"""
if df.empty:
return None
else:
count_dict = {}
sent_dict = {'positive': 0, 'negative': 1, 'neutral': 2, 'other': 3}
tot_sum = len(df)
for i in range(df.shape[0]):
topic = df.iloc[i][TOPIC]
sentiment = df.iloc[i]['sentiment'] if df.iloc[i]['sentiment'] in sent_dict else 'other'
if topic not in count_dict:
count_dict[topic] = [0, 0, 0, 0]
count_dict[topic][sent_dict[sentiment]] += 1
else:
count_dict[topic][sent_dict[sentiment]] += 1
count_list = []
other_list = np.array([0, 0, 0, 0])
labels = []
for topic in count_dict:
if tot_sum > 0 and np.sum(count_dict[topic]) / tot_sum > LIMIT:
count_list.append(count_dict[topic])
labels.append(topic)
else:
other_list += np.array(count_dict[topic])
count_list.append(list(other_list))
labels.append('Other')
fig, ax = plt.subplots()
size = 0.3
vals = np.array(count_list)
inner_colors = ['green', 'red', 'blue', 'yellow'] * len(count_dict)
if vals.shape[0] == 0:
pass
else:
ax.pie(vals.sum(axis=1), radius=1, labels=labels, pctdistance=0.9,
wedgeprops=dict(width=size, edgecolor='w'))
ax.pie(vals.flatten(), radius=1 - size, colors=inner_colors,
wedgeprops=dict(width=size, edgecolor='w'))
ax.set(aspect='equal', title='Topic (outer circle) and its corresponding sentiment (inner circle)')
return fig
# text_classifier = tc.TextClassifier(from_date=from_date, to_date=to_date,
# user_list=match_name_lower_case(usr_name_choices),
# num_tweets=NUM_TWEETS)
# text_classifier.run_main_pipeline()
# dataframe = text_classifier.get_dataframe()
dataframe = pd.read_csv("{}/data/twitterdata.csv".format(tc.ROOT_PATH))
# choose subset between from_date and to_date and username is in usr_name_choices
df = dataframe.loc[(dataframe['date'] >= from_date) & (dataframe['date'] <= to_date) & \
(dataframe['username'].isin(match_name_lower_case(usr_name_choices)))].copy()
# Sort df by date
df.sort_values(by=['date'], inplace=True)
# Remove entries from df where 'tweet' starts with '@'
df = df[df['tweet'].str.startswith('@') == False]
# change 'merged_topic' to 'Other' if it is 'ERROR_9000' or 'ERROR_496'
df[TOPIC] = df[TOPIC].apply(lambda x: "N/A" if x == "ERROR_9000" or x == "ERROR_496" else x)
# change 'merged_topic' to 'Government' if it is 's'
df[TOPIC] = df[TOPIC].apply(lambda x: "The Government" if x == "s" else x)
if save_selected:
user_list = match_name_lower_case(usr_name_choices)
df_l = []
for user in user_list:
df_l.append(pd.DataFrame(df.loc[df['username'] == user]))
selected_df = pd.concat(df_l).reset_index(drop=True)
export_to_download(selected_df, "selected_leaders")
save_selected_checkbox = [gr.Checkbox.update(interactive=False)]
else:
save_selected_checkbox = [gr.Checkbox.update(interactive=True)]
pie_charts = add_pie_chart(df, usr_name_choices, convert_plot_choices(plot_choice))
rb_components = [rb1, rb2, rb3, rb4, rb5, rb6, rb7, rb8] # radio_buttons
df_visibility_check = [v1, v2, v3, v4, v5, v6, v7, v8]
def get_selected_df_list(d_frame, save_or_no, selected_users, radio, visibility):
leader_bool_list = [True if leader in selected_users else False for leader in USER_NAMES]
df_list = []
number_tweets = []
save_file_components_list = []
for i, u_bool in enumerate(leader_bool_list):
user_df = d_frame.loc[d_frame['username'] == USER_LIST[i]]
number_tweets.append(gr.Number.update(value=len(user_df), visible=u_bool))
if save_or_no[i]:
export_to_download(pd.DataFrame(user_df), "one_leader")
save_file_components_list.append(gr.Checkbox.update(visible=u_bool, interactive=False))
else:
save_file_components_list.append(gr.Checkbox.update(visible=u_bool))
if u_bool and visibility[i]:
df_list.append(get_example_df(user_df, PLOT_CHOICES_DICT[radio[i]]))
else:
df_list.append(None)
return df_list + number_tweets + save_file_components_list
return pie_charts + save_selected_checkbox + get_selected_df_list(df, save_file_bool, list(usr_name_choices),
rb_components, df_visibility_check)
''' END OF MAIN
####
#####
####
####
'''
def get_example_df(df: pd.DataFrame, column: str):
print(column)
df = df[SELECTED_COLUMN_DICT[column]]
unique_labels = df[column].value_counts().keys()
stat = []
for label in unique_labels:
df_temp = df.loc[df[column] == label]
if len(df_temp) > 5:
df_temp = df_temp[0:5]
stat.append(df_temp)
example_df = pd.concat(stat)
# stat =stat.reset_index(drop=True) just in case u want to reset indexing
return example_df
def export_to_download(_data_frame, _type: str):
downloads_path = str(Path.home()) + "/Downloads/"
if _type == "one_leader":
file_name = _data_frame['username'].to_list()[0] # df['username'][0] + "_data"
else:
file_name = "selected_leaders"
full_path = downloads_path + file_name + ".csv"
while full_path in glob.glob(downloads_path + "*"):
search_list = re.findall('\p{N}+', full_path)
if search_list:
index = search_list[0]
full_path = re.sub(index, str(int(index) + 1), full_path)
else:
suffix = " (1).csv"
full_path = re.sub('\.csv', suffix, full_path)
_data_frame.to_csv(full_path, index=False)
# , pie_chart(df, "main_topic"), pie_chart("target")
def pie_chart_input(df, column, limit):
df_len = len(df)
if column == "sentiment":
ds_sentiment = df[column].apply(lambda x: re.sub("\s+", "", str(x)))
df_v = ds_sentiment.apply(lambda x: x if str(x).lower() == "positive" or str(x).lower() == "negative" or str(
x).lower() == "neutral" else "other").value_counts()
elif column == "merged_target":
ds_target = df[column].apply(lambda x: "other" if x == "ERROR_9000" or x == "ERROR_496" else x)
df_v = ds_target.value_counts()
freq = df_v.to_list()
labels = df_v.keys().to_list
freq_dict = {column: labels, "frequency": freq}
return pd.DataFrame.from_dict(freq_dict)
else:
df_v = df[column].value_counts()
freq = df_v.to_list()
labels = df_v.keys().to_list()
freq_other = 0
freq_dict = {column: [], "frequency": []}
for i in range(len(df_v)):
if freq[i] / df_len < limit:
freq_other += freq[i]
else:
freq_dict[column].append(labels[i])
freq_dict["frequency"].append(freq[i])
if "other" not in freq_dict[column]:
freq_dict[column].append("other")
freq_dict["frequency"].append(freq_other)
else:
ind_other = freq_dict[column].index("other")
freq_dict["frequency"][ind_other] += freq_other
test_frame = pd.DataFrame.from_dict(freq_dict)
return pd.DataFrame.from_dict(freq_dict)
def convert_to_boolean(leaders, plot_choices):
leaders_converted = [True if leader in leaders else False for leader in USER_NAMES]
plot_converted = [True if choice in plot_choices else False for choice in CHOICE_LIST]
return leaders_converted, plot_converted
def update_window(leaders: list, plot_choices: list,
v1, v2, v3, v4, v5, v6, v7, v8
):
leader_bool_list, plot_bool_list = convert_to_boolean(leaders, plot_choices)
bool_list = []
df_visibility_bool = [v1, v2, v3, v4, v5, v6, v7, v8]
# this loop sets boolean for plots
for leader in leader_bool_list:
if leader:
for choice in plot_bool_list:
bool_list.append(choice)
# bool_list.append(True) ## this is for radio component
else:
for i in range(len(plot_bool_list)):
bool_list.append(False)
# bool_list.append(False)
update_blocks = []
update_plots = []
update_radio = []
update_nr_tweet = []
update_checkbox = []
update_save_file_checkboxes = []
update_df = []
# all_visual = block_list + plots + radio_list + nr_tweet_list + checkbox_list + saving_file_checkboxes + df_list
for i, vis_or_not in enumerate(leader_bool_list):
update_blocks.append(gr.Row.update(visible=vis_or_not))
update_blocks.append(gr.Row.update(visible=vis_or_not))
if vis_or_not:
update_blocks.append(gr.Row.update(visible=df_visibility_bool[i]))
update_df.append(gr.DataFrame.update(visible=df_visibility_bool[i]))
else:
update_blocks.append(gr.Row.update(visible=False))
update_df.append(gr.DataFrame.update(visible=False))
update_nr_tweet.append(gr.Number.update(visible=vis_or_not))
update_radio.append(gr.Radio.update(visible=vis_or_not))
update_checkbox.append(gr.Checkbox.update(visible=vis_or_not))
update_save_file_checkboxes.append(gr.Checkbox.update(visible=vis_or_not))
for choice in bool_list:
update_plots.append(gr.Plot.update(visible=choice))
return update_blocks + update_plots + update_radio + update_nr_tweet + update_checkbox + update_save_file_checkboxes + update_df
def add_plots(user):
plot_list = []
for plot_type in PLOT_CHOICES_DICT:
plot_list.append(gr.Plot(label=plot_type + " for " + user, visible=False))
return plot_list
def add_nbr_boxes():
return [gr.Number(value=0, label='Tweets by ' + user, visible=False) for user in USER_NAMES]
if __name__ == "__main__":
import gradio as gr
demo = gr.Blocks(title='Politweet')
with demo:
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Row():
date1 = gr.Textbox(label="from_date", value='2022-01-01')
date2 = gr.Textbox(label="to_date", value='2022-05-31')
leaders = gr.Checkboxgroup(choices=USER_NAMES,
label="")
plot_choices = gr.CheckboxGroup(choices=CHOICE_LIST, label='Choose what to show')
save_selected_data_checkbox = gr.Checkbox(label="Export selected data")
with gr.Row():
update = gr.Button('Apply')
btn = gr.Button("Run")
# show_stat = gr.Checkbox(label="Show full statistics", value=True)
# show_plots = gr.components.Checkbox(label='Show topics', value=True)
with gr.Column():
selected = gr.DataFrame(label="Summary statistics for the selected choices",
max_rows=None, visible=False)
# all_data = gr.components.DataFrame(label="Summary statistics of the total database",
# max_rows=None)
plots = []
radio_list = []
checkbox_list = []
df_list = []
block_list = []
saving_file_checkboxes = []
nr_tweet_list = []
with gr.Column():
for i in range(len(USER_NAMES)):
block_list += [gr.Row()] * 3
for i, leader in enumerate(USER_NAMES):
with gr.Row():
plots += add_plots(leader)
with gr.Row():
radio_list.append(gr.Radio(list(PLOT_CHOICES_DICT.keys()), visible=False, interactive=True))
nr_tweet_list.append(gr.Number(visible=False))
checkbox_list.append(gr.Checkbox(label="Show stats ", value=False, visible=False))
saving_file_checkboxes.append(gr.Checkbox(label="Export file", value=False, visible=False))
with gr.Row():
df_list.append(gr.DataFrame(visible=False))
inp = [date1,
date2,
leaders,
plot_choices, save_selected_data_checkbox] + radio_list + checkbox_list + saving_file_checkboxes
output = plots + [save_selected_data_checkbox] + df_list + nr_tweet_list + saving_file_checkboxes
all_visual = block_list + plots + radio_list + nr_tweet_list + checkbox_list + saving_file_checkboxes + df_list # + df_list # df_comps
update_inp = [leaders, plot_choices] + checkbox_list
update.click(fn=update_window, inputs=update_inp, outputs=all_visual)
btn.click(fn=main, inputs=inp, outputs=output)
# input.change(fn=main, inputs=input, outputs=output)
demo.launch(share=False)
# df= pd.read_csv(os.getcwd()+"/data/twitterdata.csv")
# https://51285.gradio.app
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