import pandas as pd
import streamlit as st

# Title
st.title("Q&A Evaluation Tool")

# Load the dataset from the repository
@st.cache_data
def load_data():
    return pd.read_csv("dummy_qa_data.csv")

# Load data
data = load_data()

# Add columns for evaluation if not present
if "Rating" not in data.columns:
    data["Rating"] = ""
if "Comments" not in data.columns:
    data["Comments"] = ""

# Initialize session state for tracking progress
if "current_index" not in st.session_state:
    st.session_state.current_index = 0

# Display current question-answer pair
idx = st.session_state.current_index
if idx < len(data):
    st.subheader(f"Question {idx + 1}:")
    st.write(data.loc[idx, "Question"])

    st.subheader("Generated Answer:")
    st.write(data.loc[idx, "Generated Answer"])

    # Rating input
    rating = st.slider("Rate the answer (1 = Poor, 5 = Excellent)", 1, 5, step=1)
    comment = st.text_area("Add any comments or suggestions")

    # Save feedback
    if st.button("Submit Feedback"):
        data.at[idx, "Rating"] = rating
        data.at[idx, "Comments"] = comment
        st.session_state.current_index += 1

        # Save the updated dataset
        data.to_csv("evaluated_data.csv", index=False)

        # Confirmation and next question
        st.success("Feedback submitted!")
        st.experimental_rerun()
else:
    st.write("You have completed the evaluation. Thank you!")

    # Download the evaluated file
    with open("evaluated_data.csv", "rb") as f:
        st.download_button(
            "Download Evaluated Data",
            f,
            file_name="evaluated_data.csv",
            mime="text/csv",
        )