pages/2_explanation_generation.py

import pandas as pd
import streamlit as st
from util.assistants import GPTAgent
import os


# Function to generate explanations based on a template
def generate_explanations(model_name, questions, template, temperature, max_tokens, custom_template=None):
    agent = GPTAgent(model_name)
    explanations = []
    progress_bar = st.progress(0)
    total_questions = len(questions)

    for i, question in enumerate(questions):
        if template == "Chain of Thought":
            prompt = f"""Generate an explanation using the Chain of Thought template for the following question:

            Question: {question}

            Let's think step by step.

            Explanation:
            """
        elif template == "Custom" and custom_template:
            prompt = custom_template.replace("{question}", question)
        else:
            prompt = f"""Generate an explanation for the following question:

            Question: {question}

            Explanation:
            """
        response = agent.invoke(prompt, temperature=temperature, max_tokens=max_tokens).strip()
        explanations.append(response)

        # Update progress bar
        progress_bar.progress((i + 1) / total_questions)

    return explanations


# Predefined examples
examples = {
    'good': {
        'question': "What causes rainbows to appear in the sky?",
        'explanation': "Rainbows appear when sunlight is refracted, dispersed, and reflected inside water droplets in the atmosphere, resulting in a spectrum of light appearing in the sky."
    },
    'bad': {
        'question': "What causes rainbows to appear in the sky?",
        'explanation': "Rainbows happen because light in the sky gets mixed up and sometimes shows colors when it's raining or when there is water around."
    }
}


# Function to check password
def check_password():
    def password_entered():
        if password_input == os.getenv('PASSWORD'):
            st.session_state['password_correct'] = True
        else:
            st.error("Incorrect Password, please try again.")

    password_input = st.text_input("Enter Password:", type="password")
    submit_button = st.button("Submit", on_click=password_entered)

    if submit_button and not st.session_state.get('password_correct', False):
        st.error("Please enter a valid password to access the demo.")


# Title of the application
st.title('Explanation Generation')

# Sidebar description of the application
st.sidebar.write("""
### Welcome to the Natural Language Explanation Generation Demo
This application allows you to generate high-quality explanations for various questions using different templates. Upload a CSV of questions, select an explanation template, and generate explanations.
""")

# Check if password has been validated
if not st.session_state.get('password_correct', False):
    check_password()
else:
    st.sidebar.success("Password Verified. Proceed with the demo.")

    st.write("""
    ### Instructions for Uploading CSV
    Please upload a CSV file with the following column:
    - `question`: The question you want explanations for.

    **Example CSV Format:**
    """)

    # Display an example DataFrame
    example_data_gen = {
        "question": [
            "What causes rainbows to appear in the sky?",
            "Why is the sky blue?"
        ]
    }
    example_df_gen = pd.DataFrame(example_data_gen)
    st.dataframe(example_df_gen)

    uploaded_file_gen = st.file_uploader("Upload CSV file with 'question' column", type=['csv'])

    if uploaded_file_gen is not None:
        template = st.selectbox("Select an explanation template", ["Default", "Chain of Thought", "Custom"])
        model_name = st.selectbox('Select a model:', ['gpt4-1106', 'gpt35-1106'])

        temperature = st.sidebar.slider('Temperature', min_value=0.0, max_value=1.0, value=0.8)
        max_tokens = st.sidebar.slider('Max Tokens', min_value=50, max_value=500, value=150)

        custom_template = ""
        if template == "Custom":
            custom_template = st.text_area("Enter your custom template",
                                           value="Generate an explanation for the following question:\n\nQuestion: {question}\n\nExplanation:")

        if st.button('Generate Explanations'):
            questions_df = pd.read_csv(uploaded_file_gen)
            questions = questions_df['question'].tolist()
            explanations = generate_explanations(model_name, questions, template, temperature, max_tokens, custom_template)

            result_df_gen = pd.DataFrame({
                'question': questions,
                'explanation': explanations
            })

            st.write('### Generated Explanations')
            st.dataframe(result_df_gen)

            # Create a CSV download link
            csv_gen = result_df_gen.to_csv(index=False)
            st.download_button(
                label="Download generated explanations as CSV",
                data=csv_gen,
                file_name='generated_explanations.csv',
                mime='text/csv',
            )