app.py

import gradio as gr 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
import plotly.express as px
from stop_words import get_stop_words
from wordcloud import WordCloud
from datasets import load_dataset
import re

## import data 

dataset = load_dataset("Santarabantoosoo/italian_long_covid_tweets")
data = pd.DataFrame.from_dict(dataset["train"])


# load stop words

it_stop_words = load_dataset("Santarabantoosoo/italian-stopwords")
it_stop = pd.DataFrame.from_dict(it_stop_words["train"])

it_stop = it_stop.text.to_list()

## Optimize stop words according to Luca's repo

def format_input(user_key, stopwords):
  '''
  format user input request to lookup in the database of frequencies

  input:  
    user_key is a string
    stopwords is a list of strings
  output: 
    key is a string
  '''

  key = user_key.lower()
  key = re.sub(r'[^\w\s]', ' ', key)

  key = ' '.join([el for el in key.split() if not (el in stopwords)])


  return key


### Loading TFIDF

TFIDF_21_Jul_Oct = load_dataset("Santarabantoosoo/Long_Covid_word_frequency_TFIDF_21_Jul_Oct")

TFIDF_22_Feb_Apr = load_dataset("Santarabantoosoo/Long_Covid_word_frequency_TFIDF_22_Feb_Apr")

TFIDF_22_May_Jul = load_dataset("Santarabantoosoo/Long_Covid_word_frequency_TFIDF_22_May_Jul")

TFIDF_21_Nov_22_Jan = load_dataset("Santarabantoosoo/Long_Covid_word_frequency_TFIDF_21_Nov_22_Jan")


## Loading whole_text

whole_text_21_Jul_Oct = load_dataset("Santarabantoosoo/whole_text_TF_21_Jul_Oct")

whole_text_22_Feb_Apr = load_dataset("Santarabantoosoo/whole_text_TF_22_Feb_Apr")

whole_text_22_May_Jul = load_dataset("Santarabantoosoo/whole_text_TF_22_May_Jul")

whole_text_21_Nov_22_Jan = load_dataset("Santarabantoosoo/whole_text_TF_21_Nov_22_Jan")

TFIDF_21_Jul_Oct = pd.DataFrame.from_dict(TFIDF_21_Jul_Oct["train"])

TFIDF_22_Feb_Apr = pd.DataFrame.from_dict(TFIDF_22_Feb_Apr["train"])

TFIDF_22_May_Jul = pd.DataFrame.from_dict(TFIDF_22_May_Jul["train"])

TFIDF_21_Nov_22_Jan = pd.DataFrame.from_dict(TFIDF_21_Nov_22_Jan["train"])

whole_text_21_Jul_Oct = pd.DataFrame.from_dict(whole_text_21_Jul_Oct["train"])

whole_text_22_Feb_Apr = pd.DataFrame.from_dict(whole_text_22_Feb_Apr["train"])

whole_text_22_May_Jul = pd.DataFrame.from_dict(whole_text_22_May_Jul["train"])

whole_text_21_Nov_22_Jan = pd.DataFrame.from_dict(whole_text_21_Nov_22_Jan["train"])

ser_TFIDF = []

ser_TFIDF.append(TFIDF_21_Jul_Oct.transpose()[0])
ser_TFIDF.append(TFIDF_22_Feb_Apr.transpose()[0])
ser_TFIDF.append(TFIDF_22_May_Jul.transpose()[0])
ser_TFIDF.append(TFIDF_21_Nov_22_Jan.transpose()[0])

ser_whole_text = []

ser_whole_text.append(whole_text_21_Jul_Oct.transpose()[0])
ser_whole_text.append(whole_text_22_Feb_Apr.transpose()[0])
ser_whole_text.append(whole_text_22_May_Jul.transpose()[0])
ser_whole_text.append(whole_text_21_Nov_22_Jan.transpose()[0])


def plot_time_series(choice, keyword, user_keys):
    
    x = np.arange(2,10,2)
    
    y = [[] for j in range(len(keyword))]
    
    for j in range(len(keyword)):
      i=0
      while i < len(choice):
        try:
          y[j].append(choice[i][keyword[j]])
          i += 1
        except:
          y[j].append(0.0)
          i += 1

      y[j] = np.array(y[j])


    x_ticks_labels = ['Q1','Q2','Q3','Q4']

    fig, ax = plt.subplots(1,1) 

    for j in range(len(keyword)):
      ax.plot(x,y[j], label = user_keys[j].lower())


    # Set number of ticks for x-axis
    ax.set_xticks(x)
    ax.set_xticklabels(x_ticks_labels, fontsize=12)

    leg = plt.legend(loc='best')
    plt.xlabel('Time')
    plt.title("keywords quartely analysis (July 2021 - July 2022)")
    plt.ylabel(f'Freq. from {user_choice}')
    return fig


# Wordcloud with anger tweets
angry_tweets = data['tweet'][data["emotion"] == 'anger']
angry_tweets = angry_tweets.apply(format_input, args = [it_stop])
stop_words = ["https", 'http', "co", "RT"] + list(it_stop)
anger_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(angry_tweets))


# Wordcloud with sad tweets
sad_tweets = data['tweet'][data["emotion"] == 'sadness']
sad_tweets = sad_tweets.apply(format_input, args = [it_stop])
stop_words = ["https", 'http', "co", "RT"] + list(it_stop)
sad_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(sad_tweets))


 # Wordcloud with joy tweets
joy_tweets = data['tweet'][data["emotion"] == 'joy']
joy_tweets = joy_tweets.apply(format_input, args = [it_stop])
stop_words = ["https", 'http', "co", "RT"] + list(it_stop)
joy_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(joy_tweets))


 # Wordcloud with fear tweets
fear_tweets = data['tweet'][data["emotion"] == 'fear']
fear_tweets = fear_tweets.apply(format_input, args = [it_stop])
stop_words = ["https", 'http', "co", "RT"] + list(it_stop)
fear_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(fear_tweets))

## COmbine all plots in a single plot

wc_fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2)

# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

wc_fig.tight_layout()

ax1.imshow(sad_wordcloud, interpolation="bilinear")

ax1.axis("off")

ax1.set_title('Sadness', {'fontsize': 30})


ax2.imshow(joy_wordcloud, interpolation="bilinear")

ax2.axis("off")

ax2.set_title('Joy', {'fontsize': 30})


ax3.imshow(fear_wordcloud, interpolation="bilinear")

ax3.axis("off")

ax3.set_title('Fear', {'fontsize': 30})



ax4.imshow(anger_wordcloud, interpolation="bilinear")

ax4.axis("off")

ax4.set_title('Anger', {'fontsize': 30})


# plot a pie plot for emotions' distribution 

number_tweets_per_day = data.groupby(['date', 'emotion']).agg({'id': 'count'}).reset_index()

number_tweets_per_day["tweet_date"] = pd.to_datetime(number_tweets_per_day["date"])

time_fig = px.line(number_tweets_per_day, x = 'tweet_date', y = 'id', labels = {'id': 'count'}, color = 'emotion', 
                  color_discrete_sequence=px.colors.qualitative.G10)

# create a lineplot for emotions 

sentiment_counts = data.groupby('emotion').agg({'id' : 'size'}).reset_index()
sentiment_counts.rename(columns = {'id':'count'}, inplace = True)
sent_fig = px.pie(sentiment_counts, values='count', names='emotion', title='Tweets within each emotion', labels = {'id': 'count'}, 
                 color_discrete_sequence=px.colors.qualitative.G10)
sent_fig

def display_plot(image_choice):
    
    if image_choice == 'Sentiment distribution':
        return sent_fig
    
    elif image_choice == 'Time series':
        return time_fig
    
    elif image_choice == 'Word clouds':
        return wc_fig
    
def display_freq_plot(choice, *args):
    
    user_keys = [arg for arg in args]
    
    # clean input strings to match keywords in the database
    keyword = []
    for key in user_keys:
        keyword.append(format_input(key, it_stop))
        
    if choice == "TFIDF":
        return plot_time_series(ser_TFIDF, keyword, user_keys)
   
    elif choice == "Whole_text":
        return plot_time_series(ser_whole_text, keyword, user_keys)


with gr.Blocks() as demo:
    gr.Markdown("## Choose your adventure")
    
    with gr.Tabs():
        
        with gr.TabItem("Topic modeling"):
            gr.Markdown("Nothing here yet")

        with gr.TabItem("Word frequency"):

            inputs =  [gr.Radio(choices = ['TFIDF', 'Whole_text'], label = 'Choose ur method'), 
                      gr.Textbox(label = 'word 1'),
                      gr.Textbox(label = 'word 2'),
                      gr.Textbox(label = 'word 3'),
                      gr.Textbox(label = 'word 4')]
            plot_output = gr.Plot(elem_id = 1)
            freq_button = gr.Button("Submit")
            
        freq_button.click(display_freq_plot, inputs=inputs, outputs=plot_output)

        with gr.TabItem("Sentiment analysis"):
            text_input =  gr.Radio(choices = ['Sentiment distribution', 'Word clouds', 'Time series'], label = 'Choose ur plot')
            sent_plot = gr.Plot()
            sent_button = gr.Button("Submit")          
            
        sent_button.click(display_plot, inputs=text_input, outputs= sent_plot)
 

demo.launch(debug=True, show_error = True);