Update app.py

app.py

@@ -1,6 +1,10 @@
 import streamlit as st
 from meta_ai_api import MetaAI
 from urllib.parse import urlparse
+import pandas as pd
+import plotly.express as px
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+import nltk
 
 # Initialize Meta AI API
 ai = MetaAI()
@@ -35,6 +39,35 @@ def display_sources(sources):
     else:
         st.write("No sources available.")
 
+# ---------------------------------------------------------------------------- #
+# Sentiment Analysis Function
+# ---------------------------------------------------------------------------- #
+
+# Download the VADER lexicon for sentiment analysis
+nltk.download('vader_lexicon')
+
+# Initialize the Sentiment Intensity Analyzer
+sid = SentimentIntensityAnalyzer()
+
+def sentiment_analysis(text):
+    # Split the text into sentences
+    sentences = [sentence.strip() for sentence in text.split('.') if sentence]
+
+    # Create a DataFrame to hold the content and sentiment scores
+    df = pd.DataFrame(sentences, columns=['content'])
+
+    # Calculate sentiment scores for each sentence
+    df['sentiment_scores'] = df['content'].apply(lambda x: sid.polarity_scores(x))
+
+    # Split sentiment_scores into separate columns
+    df = pd.concat([df.drop(['sentiment_scores'], axis=1), df['sentiment_scores'].apply(pd.Series)], axis=1)
+
+    # Determine the dominant sentiment and its confidence
+    df['dominant_sentiment'] = df[['neg', 'neu', 'pos']].idxmax(axis=1)
+    df['confidence'] = df[['neg', 'neu', 'pos']].max(axis=1)
+
+    return df
+        
 # ---------------------------------------------------------------------------- #
 # Main Function
 # ---------------------------------------------------------------------------- #
@@ -62,8 +95,22 @@ def main():
     if submit_button and user_query:
         # Fetching response from Meta AI
         response = fetch_response(user_query)
+        msg = response.get('message', 'No response message.')    
+        # Write response
+        st.write(msg)
+
+        # Run sentiment analysis
+        df_sentiment = sentiment_analysis(msg)
+
+        # Display negative sentence locations
+        fig = px.scatter(df_sentiment, y='dominant_sentiment', color='dominant_sentiment', size='confidence', 
+                         hover_data=['content'], 
+                         color_discrete_map={"neg": "firebrick", "neu": "navajowhite", "pos": "darkgreen"},
+                         labels={'dominant_sentiment': 'Sentiment'},
+                         title='Sentiment Analysis of Sentences')
+        fig.update_layout(width=800, height=300)
+        st.plotly_chart(fig)
 
-        st.write(response.get('message', 'No response message.'))
         
         # Display the AI response in a collapsible section
         with st.expander("Show Sources"):