app.py

import streamlit as st
import matplotlib.pyplot as plt
from mca_comment_analyzer import MCACommentAnalyzer

# Streamlit Page Config
st.set_page_config(
    page_title="MCA Comment Analyzer",
    page_icon="📊",
    layout="wide"
)

st.title("📊 MCA eConsultation Comment Analyzer")

# 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):")

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)

        # 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")
        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.")