app.py

import streamlit as st
from mca_comment_analyzer_light import MCACommentAnalyzerLight

st.set_page_config(page_title="MCA Comment Analyzer Light", layout="wide")
st.title("📊 MCA eConsultation Comment Analyzer (Light)")

# Sidebar
st.sidebar.header("Upload or Enter Comments")
upload_file = st.sidebar.file_uploader("Upload a text file (.txt)", 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 = MCACommentAnalyzerLight()
        df, keyword_freq = analyzer.process_comments(comments)

        st.subheader("📌 Analysis Results")
        st.dataframe(df, use_container_width=True)

        st.subheader("📊 Sentiment Distribution")
        st.bar_chart(df["Sentiment"].value_counts())

        st.subheader("☁️ Word Cloud")
        plt = analyzer.generate_wordcloud(keyword_freq)
        st.pyplot(plt)
    else:
        st.warning("⚠️ Please provide comments to analyze.")