Update app.py

Update to better layout of text and a new form of the bar chart

app.py

@@ -3,35 +3,38 @@ import streamlit as st
 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
+# Create variable for Tweet being analyzed in this app
+tweet_url = "https://twitter.com/Meta/status/1453795115701440524"
 
-# DISPLAY RESULTS
-# Create dictionary of sentiment categories and corresponding scores
-values = df_redacted['sentiment_score'].value_counts(dropna=False).keys().tolist()
-counts = df_redacted['sentiment_score'].value_counts(dropna=False).tolist()
-value_dict = dict(zip(values, counts))
-
-# Calculate the max of all 3 sentiment categories
-sentiment = max(value_dict, key=value_dict.get)
+# 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']
 
+# DISPLAY DATA
+# Display the count for each sentiment category and overall sentiment of the tweet replies
 def main():
     # Header display section of app
-    st.header('This app runs a sentiment analysis on the replies to a Tweet from Facebook when they announced their '
-              'rebranding to Meta on     October 28th, 2021')
-    st.write('The Tweet analyzed for this project can be viewed here: '
-             'https://twitter.com/Meta/status/1453795115701440524')
-
-    # Display the count for each sentiment category and overall sentiment of the tweet replies
-    st.write('Highest sentiment category for the replies to the Facebook Tweet announcing Meta: ', sentiment)
-
+    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
-    st.bar_chart(df_redacted['sentiment_score'].value_counts())
+    fig = px.bar(sentiment_counts, x='Sentiment', y='Count')
+    st.plotly_chart(fig, use_container_width=True)
 
 
 if __name__ == '__main__':