reduce_and_convert_PDF.py

import os
import shutil
import re
from PyPDF2 import PdfReader, PdfWriter
import pandas as pd
import camelot
import openpyxl
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.styles import numbers
from openpyxl.worksheet.table import Table, TableStyleInfo
import tabula

def extract_pages(pdf_path, start_page, end_page, output_path):
    reader = PdfReader(pdf_path)
    writer = PdfWriter()
    for page_num in range(start_page, end_page + 1):
        if page_num <= len(reader.pages):
            writer.add_page(reader.pages[page_num - 1])

    with open(output_path, 'wb') as output_pdf_file:
        writer.write(output_pdf_file)


def reduce_pdf(pdf_folder,reduced_pdf_folder):
    if os.path.exists(reduced_pdf_folder):
        shutil.rmtree(reduced_pdf_folder)
    os.makedirs(reduced_pdf_folder)

    for filename in os.listdir(pdf_folder):
        if filename.endswith('.pdf'):
            match = re.search(r'_CbCR_(\d+)(?:-(\d+))?', filename)
            if match:
                start_page = int(match.group(1))
                end_page = int(match.group(2)) if match.group(2) else start_page
                base_name = re.sub(r'_CbCR_\d+(?:-\d+)?\.pdf$', '_CbCR.pdf', filename)
                pdf_path = os.path.join(pdf_folder, filename)
                output_path = os.path.join(reduced_pdf_folder, base_name)

                extract_pages(pdf_path, start_page, end_page, output_path)
                print(f'Processed {filename} -> {base_name}')








def extract_tables_camelot_or_tabula(pdf_path):
    try:
        tables = camelot.read_pdf(pdf_path, pages='all', flavor='stream')
        return [table.df for table in tables]
    except Exception as e:
        print(f"Camelot failed with error: {e}")
        print("Trying with Tabula...")
        dfs = tabula.read_pdf(pdf_path, pages='all', multiple_tables=True)
        return dfs


def get_numeric_count(row):
    # Get the number of numerical values in a row
    return sum(1 for x in row if (pd.notna(pd.to_numeric(str(x).replace(",", "").strip('()'), errors='coerce')) or x in ['-','–']))


def convert_to_numeric(value):
    if isinstance(value, str) and value.startswith('(') and value.endswith(')'):
        value = '-' + value[1:-1]

    if all(char.isdigit() or char in '-,.' for char in str(value)):
        cleaned_value = pd.to_numeric(str(value).replace(',', ''), errors='coerce')
        return cleaned_value
    return value

def get_headers(dataframes):
    # Get the dataframe columns name
    # if len(dataframes) >= 2:
    #     df_for_columns_names = dataframes[1]
    # else:
    #     df_for_columns_names = dataframes[0]
    first_numeric_idx = None

    order = list(range(1, len(dataframes))) + [0]

    for k in order:
        if first_numeric_idx is None:
            df_for_columns_names = dataframes[k]
            df_for_columns_names = df_for_columns_names.astype(str).where(pd.notna(df_for_columns_names), "")
            for i, row in df_for_columns_names.iterrows():
                numeric_count = get_numeric_count(row)
                if numeric_count >= 2:
                    first_numeric_idx = i
                    break
        if first_numeric_idx is not None:
            break 


    new_header = [" ".join(filter(None, df_for_columns_names.iloc[:first_numeric_idx, col].values)) for col in range(df_for_columns_names.shape[1])]

    return new_header

def clean_dataframe(df,header):
    # Rule : if a row is not numerical, merge it with the next numerical one
    if len(header) < len(df.columns):
        df.columns = header + [f"Unnamed_{i}" for i in range(len(header), len(df.columns))]
    elif len(header) > len(df.columns):
        df.columns = header[:len(df.columns)]
    else:
        df.columns = header
    first_numeric_idx = None
    for i, row in df.iterrows():
        numeric_count = get_numeric_count(row)
        if numeric_count >= 2:
            first_numeric_idx = i
            break

    df = df.iloc[first_numeric_idx:]
    df = df.reset_index(drop=True)

    merged_rows = []
    buffer = None 

    for i in range(len(df)):
        row = df.iloc[i]
        numeric_count = get_numeric_count(row)
        
        if numeric_count < 2:
            if buffer is None:
                buffer = list(df.iloc[i].copy())
            else:
                buffer = [
                    " ".join(filter(lambda x: x not in [None, "None", ""], [str(buffer[j]), str(df.iloc[i, j])]))
                    for j in range(df.shape[1])
                ]
            merged_rows.append(i)
        else:
            if buffer is not None:
                df.iloc[i] = [
                    " ".join(filter(lambda x: x not in [None, "None", ""], [str(buffer[j]), str(df.iloc[i, j])]))
                    for j in range(df.shape[1])
                ]
                buffer = None

    clean_df = df.drop(merged_rows).reset_index(drop=True)
    return clean_df


def clean_and_concatenate_tables(dataframes):
    for i in range(len(dataframes)):
        df = dataframes[i]
        row_counts = df.apply(lambda row: row.notna().sum() - (row.astype(str) == "").sum(), axis=1)
        col_counts = df.apply(lambda col: col.notna().sum() - (col.astype(str) == "").sum(), axis=0)
        dataframes[i] = df.loc[row_counts >= 1, col_counts >= 3].reset_index(drop = True)

    new_header = get_headers(dataframes) 
    cleaned_dfs = []

    for df in dataframes:
        if len(df.columns) >= 3 : 
            cleaned_dfs.append(clean_dataframe(df,new_header))
    
    cleaned_dfs = [df.reset_index(drop=True) for df in cleaned_dfs if isinstance(df, pd.DataFrame) and not df.empty]

    if not cleaned_dfs:
        raise ValueError("After cleaning, no valid dataframe left.")

    for _, df in enumerate(cleaned_dfs):
        if any(col == '' for col in df.columns):  # Check if there are empty column names
            df.columns = [f"col_{j}" if col == '' else col for j, col in enumerate(df.columns)]

    concatenated_df = pd.concat(cleaned_dfs, ignore_index=True)

    if concatenated_df.shape[0] <= 4 :
        raise ValueError("Dataframe too small, probable mistake")
    if concatenated_df.shape[1] <= 2 :
        raise ValueError("Less than 3 columns, probable mistake")
    print("Success of conversion : dataframe of shape ",concatenated_df.shape)
    return concatenated_df



def convert_to_excel(reduced_pdf_folder, output_folder):
    if os.path.exists(output_folder):
        shutil.rmtree(output_folder)
    os.makedirs(output_folder)
    
    failed_folder = os.path.join(output_folder, "failed_to_convert")
    if os.path.exists(failed_folder):
        shutil.rmtree(failed_folder)
    os.makedirs(failed_folder)
    
    if os.path.exists("./log_errors.txt"):
        os.remove("./log_errors.txt")

    number_of_files = 0
    number_of_fails = 0
    for filename in os.listdir(reduced_pdf_folder):
        if filename.endswith('.pdf'):
            number_of_files += 1
            print("Trying to convert :", filename, "to excel")
            pdf_path = os.path.join(reduced_pdf_folder, filename)
            try:
                dataframes = extract_tables_camelot_or_tabula(pdf_path)
                if not dataframes:
                    raise ValueError(f'No tables found in {filename}')
                concatenated_df = clean_and_concatenate_tables(dataframes)
                excel_path = os.path.join(output_folder, filename.replace('.pdf', '.xlsx'))

                for col in concatenated_df.columns:
                    if any(str(cell).strip() and not str(cell).strip().startswith('(') for cell in concatenated_df[col]):
                        concatenated_df[col] = concatenated_df[col].apply(convert_to_numeric)

                wb = openpyxl.Workbook()
                ws = wb.active

                percentage_cells = []

                # Add the DataFrame data to the worksheet
                for r_idx, r in enumerate(dataframe_to_rows(concatenated_df, index=False, header=True)):
                    ws.append(r)
                    for c_idx, value in enumerate(r):
                        if isinstance(value, str) and value.endswith('%'):
                            numeric_value = pd.to_numeric(value.strip('%'), errors='coerce') / 100
                            ws.cell(row=r_idx + 1, column=c_idx + 1, value=numeric_value)
                            percentage_cells.append((r_idx + 1, c_idx + 1)) 

                tab = Table(displayName="Table1", ref=ws.dimensions)
                style = TableStyleInfo(
                    name="TableStyleMedium9",
                    showFirstColumn=False,
                    showLastColumn=False,
                    showRowStripes=True,
                    showColumnStripes=True
                )
                tab.tableStyleInfo = style

                ws.add_table(tab)

                # Ajuster la largeur des colonnes
                for column_cells in ws.columns:
                    length = min(max(len(str(cell.value)) for cell in column_cells), 30)
                    ws.column_dimensions[column_cells[0].column_letter].width = length + 2

                for row, col in percentage_cells:
                    cell = ws.cell(row=row, column=col)
                    cell.number_format = numbers.BUILTIN_FORMATS[10]
                wb.save(excel_path)          
            except Exception as e:
                error_message = f"Error converting {filename}: {e}"
                print(error_message)
                number_of_fails += 1
                shutil.copy(pdf_path, os.path.join(failed_folder, filename))
                with open("./log_errors.txt", "a") as log_file:
                    log_file.write(error_message + "\n")
    print("Number of files considered : ",number_of_files)
    print("Number of success : ", number_of_files - number_of_fails)
    print("Number of fails : ", number_of_fails)
    shutil.make_archive(base_name="./output", format='zip', root_dir=output_folder)


def reduce_and_convert(input_folder):
    reduced_pdf_folder = "./reduced_pdf"
    output_folder = "./outputs"
    reduce_pdf(input_folder,reduced_pdf_folder)
    convert_to_excel(reduced_pdf_folder, output_folder)


if __name__ == "__main__":
    input_folder = "../example_pdf"
    reduce_and_convert(input_folder)