import streamlit as st
from utils.logger import setup_logger
import pandas as pd
from PIL import Image
import os

logger = setup_logger(__name__)


def prune_p60_for_display(analysis_results_for_id):
    data_to_display = {}

    data_to_display["document_category"] = "income_document"
    data_to_display["document_type"] = "p60"

    employee_details = analysis_results_for_id.get(
        "employee_details", None)

    data_to_display["surname"] = employee_details.get(
        "surname", None)
    data_to_display["forenames_or_initials"] = employee_details.get(
        "forenames_or_initials", None)
    data_to_display["national_insurance_number"] = employee_details.get(
        "national_insurance_number", None)
    data_to_display["works_payroll_number"] = employee_details.get(
        "works_payroll_number", None)

    pay_and_income_tax_details = analysis_results_for_id.get(
        "pay_and_income_tax_details", None)

    data_to_display["previous_employments"] = pay_and_income_tax_details.get(
        "previous_employments", None)
    data_to_display["current_employment"] = pay_and_income_tax_details.get(
        "current_employment", None)
    data_to_display["total_for_year"] = pay_and_income_tax_details.get(
        "total_for_year", None)
    data_to_display["final_tax_code"] = pay_and_income_tax_details.get(
        "final_tax_code", None)

    data_to_display["national_insurance_contributions"] = analysis_results_for_id.get(
        "national_insurance_contributions", None)

    employer_details = analysis_results_for_id.get(
        "employer_details", None)

    data_to_display["employer_name_and_address"] = employer_details.get(
        "employer_name_and_address", None)
    data_to_display["paye_reference"] = employer_details.get(
        "paye_reference", None)

    return data_to_display


def display_p60(extracted_files, analysis_results_pruned):

    col1, col2 = st.columns([2, 3])

    logger.info(f"file_path while displaying: {extracted_files}")
    st.markdown("---")

    with col1:
        if len(extracted_files) > 1:
            st.image(extracted_files, caption=[os.path.basename(
                img) for img in extracted_files], use_container_width=True)
        else:
            image = Image.open(extracted_files[0])
            st.image(image, caption=os.path.basename(
                extracted_files[0]))  # ,
            # use_container_width=True)

        logger.info(
            f"analysis_results_pruned : {analysis_results_pruned}")

    with col2:

        dict_str = {}

        for key, value in analysis_results_pruned.items():
            # if key not in ['other_deductions', 'salary_components']:
            dict_str[key] = value

        simple_df = pd.DataFrame.from_dict(
            dict_str,
            orient='index', columns=['Value']).reset_index()
        simple_df.columns = ['Key', 'Value']
        simple_df = simple_df.fillna(value="Missing")
        simple_df.index += 1
        st.dataframe(simple_df, use_container_width=True)

        # st.markdown("Other Deductions")

        # other_dedecutions_dict = st.session_state['tab_ocr'][
        #     'values_display'][original_file]['other_deductions']
        # logger.info(f"other_dedecutions_dict : {other_dedecutions_dict}")
        # for other_deduc in other_dedecutions_dict:
        #     simple_df = pd.DataFrame.from_dict(
        #         other_deduc,
        #         orient='index', columns=['Value']).reset_index()
        #     simple_df.columns = ['Key', 'Value']
        #     simple_df = simple_df.fillna(value="Missing")
        #     st.dataframe(simple_df, use_container_width=True)

        logger.info(f"simple_df: {simple_df}")