Update app.py

app.py

@@ -1,11 +1,17 @@
 import streamlit as st
+import matplotlib.pyplot as plt
 from mca_comment_analyzer import MCACommentAnalyzer
 
-st.set_page_config(page_title="MCA Comment Analyzer", layout="wide")
+# Streamlit Page Config
+st.set_page_config(
+    page_title="MCA Comment Analyzer",
+    page_icon="📊",
+    layout="wide"
+)
 
 st.title("📊 MCA eConsultation Comment Analyzer")
 
-# Sidebar for batch input
+# Sidebar
 st.sidebar.header("Upload or Enter Comments")
 upload_file = st.sidebar.file_uploader("Upload a text file with comments", type=["txt"])
 manual_input = st.sidebar.text_area("Or enter comments (one per line):")
@@ -16,20 +22,28 @@ if upload_file:
 elif manual_input.strip():
     comments = manual_input.strip().split("\n")
 
-if st.sidebar.button("Analyze"):
+if st.sidebar.button("🚀 Analyze"):
     if comments:
         analyzer = MCACommentAnalyzer()
         df, keyword_freq = analyzer.process_comments(comments)
 
+        # Show Analysis Results
         st.subheader("📌 Analysis Results")
         st.dataframe(df, use_container_width=True)
 
+        # Sentiment Distribution Chart
         st.subheader("📊 Sentiment Distribution")
         sentiment_counts = df["Sentiment"].value_counts()
         st.bar_chart(sentiment_counts)
 
+        # Word Cloud
         st.subheader("☁️ Word Cloud")
-        plt = analyzer.generate_wordcloud(keyword_freq)
-        st.pyplot(plt)
+        fig = analyzer.generate_wordcloud(keyword_freq)
+        st.pyplot(fig)
+
+        # Keyword Frequency Table
+        st.subheader("🔑 Keyword Frequency")
+        st.dataframe(keyword_freq, use_container_width=True)
+
     else:
         st.warning("⚠️ Please provide comments to analyze.")