app.py

# app.py

import streamlit as st
from modules.data_loader import load_texts
from modules.preprocessing import preprocess_texts
from modules.sentiment_analysis import analyze_sentiment
from modules.summarizer import summarize_texts
from modules.visualization import generate_wordcloud, plot_sentiment_distribution
from modules.utils import save_results_to_csv, display_dataframe
from config.settings import DATA_PATH

# Title
st.title("📊 Sentiment Analysis of E-Consultation Comments")
st.write("AI-powered tool for sentiment analysis, summarization, and visualization.")

# File uploader
uploaded_file = st.file_uploader("Upload a .txt file (one comment per line)", type=["txt"])

if uploaded_file:
    # Save uploaded file
    with open(DATA_PATH, "wb") as f:
        f.write(uploaded_file.read())
    st.success("✅ File uploaded successfully!")

    # Load data
    texts = load_texts(DATA_PATH)
    st.write(f"Loaded **{len(texts)}** comments.")

    # Preprocess
    clean_texts = preprocess_texts(texts)

    # Sentiment Analysis
    st.subheader("🔍 Sentiment Analysis")
    sentiment_results = analyze_sentiment(clean_texts)
    sentiment_chart = plot_sentiment_distribution(sentiment_results)
    st.image(sentiment_chart, caption="Sentiment Distribution")

    # Summarization
    st.subheader("📝 Summarization")
    summaries = summarize_texts(clean_texts)
    st.write("Here are some summaries:")
    for s in summaries[:5]:
        st.write(f"- {s['summary']}")

    # Word Cloud
    st.subheader("☁ Word Cloud")
    wc_image = generate_wordcloud(clean_texts)
    st.image(wc_image, caption="Word Cloud of Comments")

    # Results Table
    st.subheader("📑 Results Table")
    df = display_dataframe(sentiment_results, summaries)
    st.dataframe(df)

    # Save results
    results_path = save_results_to_csv(sentiment_results, summaries)
    st.download_button("📥 Download Results CSV", data=open(results_path, "rb"), file_name="results.csv")