Update app.py

Update to cleaner code with functions for most all processes

app.py

@@ -4,39 +4,92 @@ import pandas as pd
 import sys
 from streamlit import cli as stcli
 import plotly.express as px
-import numpy as np
 
 
-# DATA IMPORT
-# Import data from "df_redacted.csv" as a dataframe
-df_redacted = pd.read_csv('df_redacted.csv')
-
-# DATA MANIPULATION
+# GLOBAL VARIABLES
 # Create variable for Tweet being analyzed in this app
-tweet_url = "https://twitter.com/Meta/status/1453795115701440524"
+tweet_url = 'https://twitter.com/Meta/status/1453795115701440524'
 
-# Create new dataframe, reset the index, and rename columns
-sentiment_counts = pd.DataFrame(df_redacted['sentiment_score'].value_counts(dropna=False))
-sentiment_counts = sentiment_counts.reset_index()
-sentiment_counts.columns = ['Sentiment', 'Count']
 
-# Find sentiment category with the highest count
-sentiment = sentiment_counts.loc[sentiment_counts['Count'].idxmax(), 'Sentiment']
+# FUNCTIONS
+def data_import():
+    """
+    Imports data from "df_redacted.csv" as a dataframe.
+    """
+    df_redacted = pd.read_csv('df_redacted.csv')
+    return df_redacted
+
+
+def data_manipulation(df_redacted):
+    """
+    Manipulates the data imported from the CSV file to prepare for bar chart.
+    """
+
+    # Create new dataframe, reset the index, and rename columns
+    sentiment_counts = pd.DataFrame(df_redacted['sentiment_score'].value_counts(dropna=False))
+    sentiment_counts = sentiment_counts.reset_index()
+    sentiment_counts.columns = ['Sentiment', 'Count']
+
+    # Find sentiment category with the highest count
+    sentiment = sentiment_counts.loc[sentiment_counts['Count'].idxmax(), 'Sentiment']
+
+    return sentiment_counts, sentiment
+
+
+def display_header(tweet_url, sentiment):
+    """
+    Displays the header section of the app.
+    """
+    st.markdown('**Objective:** Understand public sentiment of a Tweet by analyzing the sentiment of each reply.')
+    st.markdown('**Analysis:** This app runs sentiment analysis on 10,948 replies to a Facebook Tweet announcing '
+                'their rebranding to Meta on 10/28/2021. Link to Tweet: {}'.format(tweet_url))
+    st.markdown('**Results:** Most frequent sentiment category for the replies to this Tweet: **{}**'.format(sentiment))
+
+
+def display_chart(sentiment_counts):
+    """
+    Displays the chosen chart for the data.
+    """
+    # Display count for each sentiment category
+    fig = px.bar(sentiment_counts,
+                 x='Sentiment',
+                 y='Count',
+                 title='Tweet Replies Sentiment Counts by Category')
 
-# DISPLAY DATA
-# Display the count for each sentiment category and overall sentiment of the tweet replies
-def main():
-    # Header display section of app
-    st.markdown("**Objective:** Understand public sentiment of a Tweet by analyzing the sentiment of each reply.")
-    st.markdown("**Analysis:** This app runs sentiment analysis on the replies to a Facebook Tweet announcing their "
-                "rebranding to Meta on 10/28/2021. Link to Tweet: {}".format(tweet_url)) 
-    st.markdown("**Results:** Most frequent sentiment category for this Tweet's replies: **{}**".format(sentiment))
-  
-    # Display histogram of count for each sentiment category
-    fig = px.bar(sentiment_counts, x='Sentiment', y='Count', title='Tweet Replies Sentiment Counts by Category')
     st.plotly_chart(fig, use_container_width=True)
 
 
+def display_footer():
+    """
+    Displays the footer section of the app.
+    """
+    st.markdown('**Notes:** ')
+    st.markdown('- The VADER model was used to analyze the sentiment of each reply: '
+                'https://github.com/cjhutto/vaderSentiment')
+    st.markdown('- Due to Twitter developer policies, I am not able to share the data set of downloaded Tweet replies '
+                'so my DATA EXTRACTION and DATA CLEANSING steps are not shown at this time.')
+    st.markdown('**Plans for Version 2.0:**')
+    st.markdown('- Formulate method for cleaning Tweet replies, such as removing those that are from bots or are spam.')
+    st.markdown('- Analyze the sentiment of replies using the BERTweet model, which would be more appropriate for this '
+                'project since it was trained on a corpus of Tweets: https://github.com/VinAIResearch/BERTweet')
+
+
+def main():
+    """
+    Main function for the app which calls all other functions to display the app.
+    """
+    # DATA IMPORT
+    df_redacted = data_import()
+
+    # DATA MANIPULATION
+    sentiment_counts, sentiment = data_manipulation(df_redacted)
+
+    # DISPLAY DATA
+    display_header(tweet_url, sentiment)
+    display_chart(sentiment_counts)
+    display_footer()
+
+
 if __name__ == '__main__':
     if st._is_running_with_streamlit:
         main()