import streamlit as st
import pandas as pd
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from transformers import pipeline

# Load Hugging Face models
sentiment_analyzer = pipeline("sentiment-analysis")
summarizer = pipeline("summarization")

st.set_page_config(page_title="Sentiment Analysis App", layout="wide")

st.title("📊 Sentiment Analysis of e-Consultation Comments")
st.write("Analyze single or multiple comments: sentiment, summary, and word cloud.")

# --- Sidebar mode selection ---
mode = st.sidebar.radio("Choose mode:", ["Single Comment", "Upload File"])

# --- Mode 1: Single Comment Analysis ---
if mode == "Single Comment":
    user_input = st.text_area("Enter a comment:", height=150)

    if st.button("Analyze Comment"):
        if user_input.strip():
            # Sentiment
            sentiment = sentiment_analyzer(user_input)[0]
            st.subheader("🔹 Sentiment Analysis")
            st.write(f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}")

            # Summarization (if long enough)
            if len(user_input.split()) > 30:
                summary = summarizer(user_input, max_length=50, min_length=20, do_sample=False)[0]['summary_text']
                st.subheader("🔹 Summary")
                st.write(summary)
            else:
                st.info("Not enough text for summarization (need > 30 words).")

            # Word Cloud
            st.subheader("🔹 Word Cloud")
            wordcloud = WordCloud(width=800, height=400, background_color="white").generate(user_input)
            fig, ax = plt.subplots(figsize=(10, 5))
            ax.imshow(wordcloud, interpolation="bilinear")
            ax.axis("off")
            st.pyplot(fig)
        else:
            st.warning("⚠️ Please enter a comment before analyzing.")

# --- Mode 2: Batch Analysis from File ---
else:
    st.info("Upload a CSV or Excel file containing a column named **comment**.")

    uploaded_file = st.file_uploader("Upload file", type=["csv", "xlsx"])

    if uploaded_file:
        # Read file
        if uploaded_file.name.endswith(".csv"):
            df = pd.read_csv(uploaded_file)
        else:
            df = pd.read_excel(uploaded_file)

        if "comment" not in df.columns:
            st.error("❌ File must contain a column named 'comment'.")
        else:
            st.write("### Uploaded Data", df.head())

            if st.button("Analyze All Comments"):
                sentiments = []
                all_text = " "

                for text in df["comment"].dropna():
                    result = sentiment_analyzer(str(text))[0]
                    sentiments.append(result["label"])
                    all_text += " " + str(text)

                df["sentiment"] = sentiments

                st.subheader("🔹 Sentiment Results")
                st.write(df)

                # Overall Word Cloud
                st.subheader("🔹 Word Cloud (All Comments)")
                wordcloud = WordCloud(width=800, height=400, background_color="white").generate(all_text)
                fig, ax = plt.subplots(figsize=(10, 5))
                ax.imshow(wordcloud, interpolation="bilinear")
                ax.axis("off")
                st.pyplot(fig)

                # Sentiment Distribution
                st.subheader("🔹 Sentiment Distribution")
                st.bar_chart(df["sentiment"].value_counts())