import pandas as pd
import streamlit as st
from util.evaluator import evaluator, write_evaluation_commentary
import os

def check_password():
    with st.sidebar:
        password_input = st.text_input("Enter Password:", type="password")
        submit_button = st.button("Submit")
        if submit_button:
            if password_input == os.getenv('PASSWORD'):
                st.session_state['password_verified'] = True
                st.experimental_rerun()
            else:
                st.error("Incorrect Password, please try again.")

def batch_evaluate(uploaded_file):
    # Read the uploaded CSV file into DataFrame
    df = pd.read_csv(uploaded_file)
    eval_instance = evaluator('gpt4-1106')  # Using fixed model name for simplicity
    results = []

    # Process each row in the DataFrame
    for _, row in df.iterrows():
        question = row['question']
        explanation = row['explanation']
        scores = eval_instance(question, explanation)  # Evaluate using the evaluator
        commentary_details = write_evaluation_commentary(scores)  # Generate commentary based on scores
        results.append({
            'Question': question,
            'Explanation': explanation,
            **{detail['Principle']: detail['Score'] for detail in commentary_details}
        })

    return pd.DataFrame(results)

st.title('Natural Language Explanation Demo')

if 'password_verified' not in st.session_state or not st.session_state['password_verified']:
    check_password()
else:
    st.sidebar.success("Password Verified. Proceed with the demo.")
    uploaded_file = st.file_uploader("Upload CSV file with 'question' and 'explanation' columns", type=['csv'])

    if uploaded_file is not None:
        if st.button('Evaluate Explanations'):
            result_df = batch_evaluate(uploaded_file)
            st.write('### Evaluated Results')
            st.dataframe(result_df)

            # Create a CSV download link
            csv = result_df.to_csv(index=False)
            st.download_button(
                label="Download evaluation results as CSV",
                data=csv,
                file_name='evaluated_results.csv',
                mime='text/csv',
            )