import streamlit as st
import pandas as pd
from db import insert_data_if_empty, get_mongo_client
from chatbot import chatbot_response

# Ensure historical data is inserted into MongoDB if not already present.
insert_data_if_empty()

# Connect to MongoDB (optional: for additional visualizations)
collection = get_mongo_client()

# Create a two-column layout: left for the image, right for the chatbot UI.
col1, col2 = st.columns([1, 3])

with col1:
    st.image("https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment.png", use_column_width=True)

with col2:
    st.subheader("💬 Chatbot with Sentiment Analysis & Category Extraction")

    # Create an expander to display example questions on separate lines.
    with st.expander("👋 Hi, allow me to help you with prompts:"):
        st.write("💡 Provide analysis for data entry 1 in the dataset")
        st.write("💡 What is the dataset summary?")
        st.write("💡 or just ask me something of your own, I'll be happy to help 😊")

    # Text area for user input.
    user_prompt = st.text_area("Ask me something:")

    if st.button("Get Response"):
        ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence = chatbot_response(user_prompt)
        if ai_response:
            st.write("### Response:")
            st.markdown(ai_response)
            st.write("### Sentiment Analysis:")
            st.write(f"**Sentiment Detected:** {sentiment_label} ({sentiment_confidence*100:.2f}% confidence)")
            st.write("### Category Extraction:")
            st.write(f"**Category Detected:** {topic_label} ({topic_confidence*100:.2f}% confidence)")
        else:
            st.warning("⚠️ Please enter a question or text for analysis.")