The Code I am using is import boto3 from collections import defaultdict from urllib.parse import unquote_plus

#Timestamp for appending to File Name from datetime import datetime

Get the current date and time

current_datetime = datetime.now()

Extract date, hours, and minutes

current_date = current_datetime.strftime("%Y-%m-%d") current_hours_minutes = current_datetime.strftime("%H:%M")

Concatenate date and time with a space in between

CurrentTime = str(current_date + "-" + current_hours_minutes)

def get_rows_columns_map(table_result, blocks_map): rows = {} scores = [] for relationship in table_result['Relationships']: if relationship['Type'] == 'CHILD': for child_id in relationship['Ids']: cell = blocks_map[child_id] if cell['BlockType'] == 'CELL': row_index = cell['RowIndex'] col_index = cell['ColumnIndex'] if row_index not in rows: # create new row rows[row_index] = {}

                # get confidence score
                #scores.append(str(cell['Confidence']))
                    
                # get the text value
                rows[row_index][col_index] = get_text(cell, blocks_map)
return rows, scores

def get_table_csv_results(bucket, file_name):

# with open(file_name, 'rb') as file:
#     img_test = file.read()
#     bytes_test = bytearray(img_test)
#     print('Image loaded', file_name)

# process using image bytes
# get the results
# session = boto3.Session(profile_name='profile-name')
client = boto3.client('textract')
# response = client.analyze_document(Document={'Bytes': bytes_test}, FeatureTypes=['TABLES'])
response = client.analyze_document(Document={'S3Object': {'Bucket': bucket, "Name": file_name}}, FeatureTypes=['TABLES'])

# Get the text blocks
blocks=response['Blocks']
print(blocks)

blocks_map = {}
table_blocks = []
for block in blocks:
    blocks_map[block['Id']] = block
    if block['BlockType'] == "TABLE":
        table_blocks.append(block)

if len(table_blocks) <= 0:
    return "<b> NO Table FOUND </b>"

csv = ''
for index, table in enumerate(table_blocks):
    csv += generate_table_csv(table, blocks_map, index +1)
    csv += '\n\n'

return csv

def generate_table_csv(table_result, blocks_map, table_index): rows, scores = get_rows_columns_map(table_result, blocks_map)

table_id = 'Table_' + str(table_index)

# get cells.
csv = 'Table: {0}\n\n'.format(table_id)

for row_index, cols in rows.items():
    for col_index, text in cols.items():
        col_indices = len(cols.items())
        csv += '{}'.format(text) + ","
    csv += '\n'
    
csv += '\n\n Confidence Scores % (Table Cell) \n'
cols_count = 0
for score in scores:
    cols_count += 1
    csv += score + ","
    if cols_count == col_indices:
        csv += '\n'
        cols_count = 0

csv += '\n\n\n'
return csv

def find_value_block(key_block, value_map): for relationship in key_block['Relationships']: if relationship['Type'] == 'VALUE': for value_id in relationship['Ids']: value_block = value_map[value_id] return value_block

def get_text(result, blocks_map): text = '' if 'Relationships' in result: for relationship in result['Relationships']: if relationship['Type'] == 'CHILD': for child_id in relationship['Ids']: word = blocks_map[child_id] if word['BlockType'] == 'WORD': text += word['Text'] + ' ' if word['BlockType'] == 'SELECTION_ELEMENT': if word['SelectionStatus'] == 'SELECTED': text += 'X ' return text

def lambda_handler(event, context): print(event) print(context) file_obj = event["Records"][0] bucket = unquote_plus(str(file_obj["s3"]["bucket"]["name"])) file_name = unquote_plus(str(file_obj["s3"]["object"]["key"]))

print(f'Bucket: {bucket}, file: {file_name}')
table_csv = get_table_csv_results(bucket, file_name)
output_file =CurrentTime+'_outputfile.csv'

s3 = boto3.resource("s3")
s3.Bucket("outputbucketforcsv").put_object(Key=output_file, Body=table_csv)

============================================================================== what needs to be changes to get all other tables and forms data to be extracted.