import streamlit as st
import pandas as pd
from transformers import pipeline, AutoTokenizer
import base64

# Load the EasyTerms/legalSummerizerET model from Hugging Face
summarizer = pipeline("summarization", model="EasyTerms/legalSummerizerET")

# Increase the maximum token limit
tokenizer = AutoTokenizer.from_pretrained("EasyTerms/legalSummerizerET")
summarizer.model.config.max_position_embeddings = tokenizer.model_max_length

# Function to generate summary using the EasyTerms/legalSummerizerET model
def generate_summary(contract_text):
    summary = summarizer(contract_text, max_length=512, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
    return summary[0]['summary_text']

# Function to handle feedback and store it in a CSV file
def handle_feedback(feedback_data, feedback_file):
    feedback_df = pd.DataFrame(feedback_data, columns=['Contract', 'Summary', '👍', '👎'])

    # Save the dataframe to the feedback CSV file
    feedback_df.to_csv(feedback_file, mode='a', index=False, header=not st.session_state.feedback_csv_exists)

    # Display a feedback collected message only if thumbs up or thumbs down is clicked
    if '👍' in feedback_df['👍'].values or '👎' in feedback_df['👎'].values:
        st.success("Feedback collected successfully!")

        # Display a download button for the user
        st.markdown(get_binary_file_downloader_html(feedback_file, 'Feedback Data'), unsafe_allow_html=True)

# Function to create a download link for a binary file
def get_binary_file_downloader_html(file_path, file_label):
    with open(file_path, 'rb') as file:
        file_content = file.read()
    b64 = base64.b64encode(file_content).decode()
    return f'<a href="data:file/csv;base64,{b64}" download="{file_label}.csv">Click here to download {file_label}</a>'

# Main Streamlit app
def main():
    st.title("Legal Contract Summarizer with Feedback")

    # Input area for legal contract
    contract_text = st.text_area("Enter the legal contract:", height=200)  # Increase the height to handle larger contracts

    # Button to generate summary
    if st.button("Generate Summary"):
        summary = generate_summary(contract_text)
        st.subheader("Generated Summary:")
        st.write(summary)

        # Feedback section
        st.subheader("Feedback:")
        thumbs_up = st.button("👍")
        thumbs_down = st.button("👎")

        chosen = "👍" if thumbs_up else None
        rejected = "👎" if thumbs_down else None

        feedback_data.append((contract_text, summary, chosen, rejected))

        # Handle feedback data
        if feedback_data:
            feedback_file = 'feedback.csv'
            st.session_state.feedback_csv_exists = True
            handle_feedback(feedback_data, feedback_file)

# Initialize feedback data
feedback_data = []

# Run the app
if __name__ == "__main__":
    main()