def processImage(documentId, features, bucketName, outputBucketName,
objectName, outputTableName, documentsTableName,
elasticsearchDomain):
detectText = "Text" in features
detectForms = "Forms" in features
detectTables = "Tables" in features
response = callTextract(bucketName, objectName, detectText, detectForms,
detectTables)
dynamodb = AwsHelper().getResource("dynamodb")
ddb = dynamodb.Table(outputTableName)
outputPath = '{}{}/{}'.format(PUBLIC_PATH_S3_PREFIX, documentId,
SERVICE_OUTPUT_PATH_S3_PREFIX)
print("Generating output for DocumentId: {} and storing in {}".format(
documentId, outputPath))
opg = OutputGenerator(documentId, response, outputBucketName, objectName,
detectForms, detectTables, ddb, outputPath,
elasticsearchDomain)
opg_output = opg.run()
generatePdf(documentId, bucketName, objectName, outputBucketName,
outputPath)
# generate Comprehend and ComprehendMedical entities in S3
comprehendOutputPath = "{}{}".format(outputPath, COMPREHEND_PATH_S3_PREFIX)
print("Comprehend output path: " + comprehendOutputPath)
maxPages = 100
comprehendClient = ComprehendHelper()
responseDocumentName = "{}{}response.json".format(outputPath,
TEXTRACT_PATH_S3_PREFIX)
comprehendAndMedicalEntities = comprehendClient.processComprehend(
outputBucketName, responseDocumentName, comprehendOutputPath, maxPages)
# if Kendra is available then let it index the document
# index the searchable pdf in Kendra
if 'KENDRA_INDEX_ID' in os.environ:
kendraClient = KendraHelper()
fileName = os.path.basename(objectName).split(".")[0]
fileExtension = os.path.basename(objectName).split(".")[1]
outputDocumentName = "{}{}-searchable.pdf".format(outputPath, fileName)
kendraClient.indexDocument(os.environ['KENDRA_INDEX_ID'],
os.environ['KENDRA_ROLE_ARN'],
outputBucketName, outputDocumentName,
documentId, fileExtension)
print("Processed Comprehend data for document: {}".format(documentId))
for key, val in opg_output[KVPAIRS].items():
if key not in comprehendAndMedicalEntities:
comprehendAndMedicalEntities[key] = val
else:
comprehendAndMedicalEntities[key].add(val)
opg.indexDocument(opg_output[DOCTEXT], comprehendAndMedicalEntities)
ds = datastore.DocumentStore(documentsTableName, outputTableName)
ds.markDocumentComplete(documentId)
def processImage(documentId, features, bucketName, outputBucketName,
objectName, outputTableName, documentsTableName,
elasticsearchDomain):
detectText = "Text" in features
detectForms = "Forms" in features
detectTables = "Tables" in features
response = callTextract(bucketName, objectName, detectText, detectForms,
detectTables)
dynamodb = AwsHelper().getResource("dynamodb")
ddb = dynamodb.Table(outputTableName)
outputPath = '{}{}/{}'.format(PUBLIC_PATH_S3_PREFIX, documentId,
SERVICE_OUTPUT_PATH_S3_PREFIX)
print("Generating output for DocumentId: {} and storing in {}".format(
documentId, outputPath))
opg = OutputGenerator(documentId, response, outputBucketName, objectName,
detectForms, detectTables, ddb, outputPath,
elasticsearchDomain)
opg_output = opg.run()
generatePdf(documentId, bucketName, objectName, outputBucketName,
outputPath)
# generate Comprehend and ComprehendMedical entities in S3
comprehendOutputPath = "{}{}".format(outputPath, COMPREHEND_PATH_S3_PREFIX)
print("Comprehend output path: " + comprehendOutputPath)
maxPages = 100
comprehendClient = ComprehendHelper()
responseDocumentName = "{}{}response.json".format(outputPath,
TEXTRACT_PATH_S3_PREFIX)
comprehendAndMedicalEntities = comprehendClient.processComprehend(
outputBucketName, responseDocumentName, comprehendOutputPath, maxPages)
print("DocumentId: {}".format(documentId))
print("Processed Comprehend data: {}".format(comprehendAndMedicalEntities))
for key, val in opg_output[KVPAIRS].items():
if key not in comprehendAndMedicalEntities:
comprehendAndMedicalEntities[key] = val
else:
comprehendAndMedicalEntities[key].add(val)
opg.indexDocument(opg_output[DOCTEXT], comprehendAndMedicalEntities)
ds = datastore.DocumentStore(documentsTableName, outputTableName)
ds.markDocumentComplete(documentId)
def processImage(documentId, features, bucketName, outputBucketName,
objectName, outputTableName, documentsTableName,
elasticsearchDomain):
detectText = "Text" in features
detectForms = "Forms" in features
detectTables = "Tables" in features
response = callTextract(bucketName, objectName, detectText, detectForms,
detectTables)
dynamodb = AwsHelper().getResource("dynamodb")
ddb = dynamodb.Table(outputTableName)
print("Generating output for DocumentId: {}".format(documentId))
opg = OutputGenerator(documentId, response, outputBucketName, objectName,
detectForms, detectTables, ddb, elasticsearchDomain)
opg_output = opg.run()
generatePdf(documentId, bucketName, objectName, outputBucketName)
# generate Comprehend and ComprehendMedical entities in S3
path = objectName + "-analysis" + "/" + documentId + "/"
print("path: " + path)
maxPages = 100
comprehendClient = ComprehendHelper()
comprehendAndMedicalEntities = comprehendClient.processComprehend(
outputBucketName, 'response.json', path, maxPages)
print("DocumentId: {}".format(documentId))
print("Processed Comprehend data: {}".format(comprehendAndMedicalEntities))
for key, val in opg_output[KVPAIRS].items():
if key not in comprehendAndMedicalEntities:
comprehendAndMedicalEntities[key] = val
else:
comprehendAndMedicalEntities[key].add(val)
opg.indexDocument(opg_output[DOCTEXT], comprehendAndMedicalEntities)
ds = datastore.DocumentStore(documentsTableName, outputTableName)
ds.markDocumentComplete(documentId)
def processRequest(request):
output = ""
print(request)
jobId = request['jobId']
jobTag = request['jobTag']
jobStatus = request['jobStatus']
jobAPI = request['jobAPI']
bucketName = request['bucketName']
outputBucketName = request['outputBucketName']
objectName = request['objectName']
outputTable = request["outputTable"]
documentsTable = request["documentsTable"]
elasticsearchDomain = request["elasticsearchDomain"]
pages = getJobResults(jobAPI, jobId)
print("Result pages recieved: {}".format(len(pages)))
dynamodb = AwsHelper().getResource("dynamodb")
ddb = dynamodb.Table(outputTable)
detectForms = False
detectTables = False
if (jobAPI == "StartDocumentAnalysis"):
detectForms = True
detectTables = True
dynamodb = AwsHelper().getResource('dynamodb')
ddb = dynamodb.Table(outputTable)
opg = OutputGenerator(jobTag, pages, outputBucketName, objectName,
detectForms, detectTables, ddb, elasticsearchDomain)
opg_output = opg.run()
generatePdf(jobTag, bucketName, objectName, outputBucketName)
# generate Comprehend and ComprehendMedical entities
path = objectName + "-analysis" + "/" + jobTag + "/"
print("path: " + path)
maxPages = 100
comprehendClient = ComprehendHelper()
comprehendAndMedicalEntities = comprehendClient.processComprehend(
outputBucketName, 'response.json', path, maxPages)
print("DocumentId: {}".format(jobTag))
# index document once the comprehend entities and KVPairs have been extracted
for key, val in opg_output[KVPAIRS].items():
if key not in comprehendAndMedicalEntities:
comprehendAndMedicalEntities[key] = val
else:
comprehendAndMedicalEntities[key].add(val)
opg.indexDocument(opg_output[DOCTEXT], comprehendAndMedicalEntities)
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.markDocumentComplete(jobTag)
output = "Processed -> Document: {}, Object: {}/{} processed.".format(
jobTag, bucketName, objectName)
return {'statusCode': 200, 'body': output}
def processRequest(request):
output = ""
print("Request : {}".format(request))
jobId = request['jobId']
documentId = request['jobTag']
jobStatus = request['jobStatus']
jobAPI = request['jobAPI']
bucketName = request['bucketName']
outputBucketName = request['outputBucketName']
objectName = request['objectName']
outputTable = request["outputTable"]
documentsTable = request["documentsTable"]
elasticsearchDomain = request["elasticsearchDomain"]
pages = getJobResults(jobAPI, jobId)
print("Result pages recieved: {}".format(len(pages)))
dynamodb = AwsHelper().getResource("dynamodb")
ddb = dynamodb.Table(outputTable)
detectForms = False
detectTables = False
if (jobAPI == "StartDocumentAnalysis"):
detectForms = True
detectTables = True
dynamodb = AwsHelper().getResource('dynamodb')
ddb = dynamodb.Table(outputTable)
outputPath = '{}{}/{}'.format(PUBLIC_PATH_S3_PREFIX, documentId,
SERVICE_OUTPUT_PATH_S3_PREFIX)
print("Generating output for DocumentId: {} and storing in {}".format(
documentId, outputPath))
opg = OutputGenerator(documentId, pages, outputBucketName, objectName,
detectForms, detectTables, ddb, outputPath,
elasticsearchDomain)
opg_output = opg.run()
generatePdf(documentId, bucketName, objectName, outputBucketName,
outputPath)
# generate Comprehend and ComprehendMedical entities
comprehendOutputPath = "{}{}".format(outputPath, COMPREHEND_PATH_S3_PREFIX)
print("Comprehend output path: " + comprehendOutputPath)
maxPages = 100
comprehendClient = ComprehendHelper()
responseDocumentName = "{}{}response.json".format(outputPath,
TEXTRACT_PATH_S3_PREFIX)
comprehendAndMedicalEntities = comprehendClient.processComprehend(
outputBucketName, responseDocumentName, comprehendOutputPath, maxPages)
# if Kendra is available then let it index the document
if 'KENDRA_INDEX_ID' in os.environ:
kendraClient = KendraHelper()
fileName = os.path.basename(objectName).split(".")[0]
fileExtension = os.path.basename(objectName).split(".")[1]
outputDocumentName = "{}{}-searchable.pdf".format(outputPath, fileName)
kendraClient.indexDocument(os.environ['KENDRA_INDEX_ID'],
os.environ['KENDRA_ROLE_ARN'], bucketName,
outputDocumentName, documentId,
fileExtension)
print("DocumentId: {}".format(documentId))
print("Processed Comprehend data: {}".format(comprehendAndMedicalEntities))
# index document once the comprehend entities and KVPairs have been extracted
for key, val in opg_output[KVPAIRS].items():
if key not in comprehendAndMedicalEntities:
comprehendAndMedicalEntities[key] = val
else:
comprehendAndMedicalEntities[key].add(val)
opg.indexDocument(opg_output[DOCTEXT], comprehendAndMedicalEntities)
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.markDocumentComplete(documentId)
output = "Processed -> Document: {}, Object: {}/{} processed.".format(
documentId, bucketName, objectName)
return {'statusCode': 200, 'body': output}
