Delete helper.py

helper.py

@@ -1,106 +0,0 @@
-import matplotlib.pyplot as plt
-from urlextract import URLExtract
-from collections import Counter
-from wordcloud import WordCloud, STOPWORDS ,ImageColorGenerator
-import pandas as pd
-import matplotlib.pylab as plt
-import PIL.Image
-import numpy as np
-
-extract=URLExtract()
-def fetch_stats(selected_user,df):
-
-    if selected_user!= "Group analysis":
-        df=df[df['users']==selected_user]
-    num_messages = df.shape[0]
-    words = []
-    for message in df['message']:
-        words.extend(message.split())
-
-
-    links=[]
-    for message in df['message']:
-        links.extend(extract.find_urls(message))
-
-    return num_messages, len(words),len(links)
-
-def most_busy_users(df):
-    x = df['users'].value_counts().head()
-    df=round((df['users'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
-        columns={'index': 'name', 'user': 'percent'})
-    return x,df
-
-def most_common_words(selected_user,df):
-    f = open('stop_hinglish.txt', 'r')
-    stop_words = f.read()
-
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-    temp = df[df['users'] != 'group_notification']
-    temp = temp[temp['message'] != '<Media omitted>\n']
-
-    words = []
-
-    for message in temp['message']:
-        for word in message.lower().split():
-            if word not in stop_words:
-                words.append(word)
-    most_common_df=pd.DataFrame(Counter(words).most_common(30))
-    return most_common_df
-
-def positive_word_cloud(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    pos_word = df[df['roberta_pos'] > 0.5]
-    pos_word = pos_word.pop('message')
-    pos_word_df = pd.DataFrame(pos_word)
-    stopwords = set(STOPWORDS)
-    mask = np.array(PIL.Image.open('wcc.png'))
-
-    # wordcloud
-    wordcloud = WordCloud(stopwords=stopwords, mask=mask, background_color="White").generate(
-        ''.join(pos_word_df['message']))
-    plt.figure(figsize=(12,6), facecolor='k')
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.show()
-
-    return wordcloud
-
-def negative_word_cloud(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    pos_word = df[df['roberta_neg'] > 0.5]
-    pos_word = pos_word.pop('message')
-    pos_word_df = pd.DataFrame(pos_word)
-    stopwords = set(STOPWORDS)
-    mask = np.array(PIL.Image.open('wcc.png'))
-
-    # wordcloud
-    wordcloud = WordCloud(stopwords=stopwords, mask=mask, background_color="White").generate(
-        ''.join(pos_word_df['message']))
-    plt.figure(figsize=(12,6), facecolor='k')
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.show()
-
-    return wordcloud
-
-def neutral_word_cloud(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    pos_word = df[df['roberta_neu'] > 0.5]
-    pos_word = pos_word.pop('message')
-    pos_word_df = pd.DataFrame(pos_word)
-    stopwords = set(STOPWORDS)
-    mask = np.array(PIL.Image.open('wcc.png'))
-
-    # wordcloud
-    wordcloud = WordCloud(stopwords=stopwords, mask=mask, background_color="White").generate(
-        ''.join(pos_word_df['message']))
-    plt.figure(figsize=(12,6), facecolor='k')
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.show()
-
-    return wordcloud