Create app.py

app.py

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+import streamlit as st
+from mca_comment_analyzer import MCACommentAnalyzer
+
+st.set_page_config(page_title="MCA Comment Analyzer", layout="wide")
+
+st.title("📊 MCA eConsultation Comment Analyzer")
+
+# Sidebar for batch input
+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):")
+
+comments = []
+if upload_file:
+    comments = upload_file.read().decode("utf-8").splitlines()
+elif manual_input.strip():
+    comments = manual_input.strip().split("\n")
+
+if st.sidebar.button("Analyze"):
+    if comments:
+        analyzer = MCACommentAnalyzer()
+        df, keyword_freq = analyzer.process_comments(comments)
+
+        st.subheader("📌 Analysis Results")
+        st.dataframe(df, use_container_width=True)
+
+        st.subheader("📊 Sentiment Distribution")
+        sentiment_counts = df["Sentiment"].value_counts()
+        st.bar_chart(sentiment_counts)
+
+        st.subheader("☁️ Word Cloud")
+        plt = analyzer.generate_wordcloud(keyword_freq)
+        st.pyplot(plt)
+    else:
+        st.warning("⚠️ Please provide comments to analyze.")