import table_funcs
import config
import utils
import preprocessing_funcs
import concurrent.futures
import pandas as pd
import os

input_dict = utils.read_input()
table_analysis_dicts = []


def table_analysis(file_object):
    filename, contract_text = file_object
    text_dict = preprocessing_funcs.split_text(contract_text)
    file_dict = {"Filename": filename, "Filename_pdf": filename.replace(".txt", ".pdf")}

    contains_reimbursement = utils.contains_reimbursement(contract_text)
    file_dict["Contains Reimbursement"] = contains_reimbursement

    # Determine if the file should be present in the output
    file_dict["Present in Output"] = contains_reimbursement

    stats_dict = table_funcs.get_table_stats(text_dict)
    file_dict.update(stats_dict)
    table_analysis_dicts.append(file_dict)


with concurrent.futures.ThreadPoolExecutor(max_workers=config.MAX_WORKERS) as executor:
    futures = [executor.submit(table_analysis, item) for item in input_dict.items()]
    for future in concurrent.futures.as_completed(futures):
        try:
            future.result()
        except Exception as e:
            print(f"Error: {e}")

# Create the main table analysis DataFrame
table_analysis_df = pd.DataFrame(table_analysis_dicts)
table_analysis_df.to_csv(
    os.path.join(config.REPORTING_OUTPUT_DIRECTORY, config.TABLE_ANALYSIS_NAME)
)

# Define the columns we want in our report
report_columns = [
    "Filename",
    "Contains Reimbursement",
    "Present in Output",
    "Table Count",
    "Table Page Count",
    "Rate Count",
    "Num Rate Pages",
    "Num >=10 Rates",
]

# Create the new report DataFrame with available columns
report_df = table_analysis_df[
    [col for col in report_columns if col in table_analysis_df.columns]
]


# Function to safely create summary columns
def create_summary_column(df, source_col, target_col):
    if source_col in df.columns:
        df[target_col] = df[source_col].apply(
            lambda x: ", ".join(map(str, eval(x))) if x != "[]" else "None"
        )
    else:
        df[target_col] = "N/A"


# Add summary columns if the data is available
create_summary_column(report_df, "Table Pages", "Table Pages Summary")
create_summary_column(report_df, "Rate Pages", "Rate Pages Summary")
create_summary_column(report_df, ">=10 Rate Pages", ">=10 Rate Pages Summary")

# Define the final column order
final_columns = [
    "Filename",
    "Contains Reimbursement",
    "Present in Output",
    "Table Count",
    "Table Page Count",
    "Table Pages Summary",
    "Rate Count",
    "Num Rate Pages",
    "Rate Pages Summary",
    "Num >=10 Rates",
    ">=10 Rate Pages Summary",
]

# Reorder columns, including only those that exist
report_df = report_df[[col for col in final_columns if col in report_df.columns]]

# Save the new report to a CSV file
report_output_path = os.path.join(
    config.REPORTING_OUTPUT_DIRECTORY, "enhanced_reimbursement_report.csv"
)
report_df.to_csv(report_output_path, index=False)

print(f"Enhanced reimbursement report saved to: {report_output_path}")
print("Columns in the report:", ", ".join(report_df.columns))