def processRequest(request):
output = ""
print("request: {}".format(request))
bucketName = request["bucketName"]
objectName = request["objectName"]
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
print("Input Object: {}/{}".format(bucketName, objectName))
ext = FileHelper.getFileExtenstion(objectName.lower())
print("Extension: {}".format(ext))
if ext and ext in ["jpg", "jpeg", "png", "pdf"]:
documentId = str(uuid.uuid1())
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.createDocument(documentId, bucketName, objectName)
output = "Saved document {} for {}/{}".format(documentId, bucketName,
objectName)
print(output)
return {"statusCode": 200, "body": json.dumps(output)}
def processImage(
documentId, features, bucketName, objectName, outputTableName, documentsTableName
):
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, bucketName, objectName, detectForms, detectTables, ddb
)
opg.run()
print("DocumentId: {}".format(documentId))
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)
# 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 processRequest(request):
output = ""
print(f"request: {request}")
bucketName = request["bucketName"]
objectName = request["objectName"]
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
print(f"Input Object: {bucketName}/{objectName}")
ext = FileHelper.getFileExtenstion(objectName.lower())
print(f"Extension: {ext}")
if(ext and ext in ["jpg", "jpeg", "png", "pdf"]):
documentId = str(uuid.uuid1())
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.createDocument(documentId, bucketName, objectName)
output = f"Saved document {documentId} for {bucketName}/{objectName}"
print(output)
return {
'statusCode': 200,
'body': json.dumps(output)
}
def deleteDocument(request):
print("DeleteDocument request: {}".format(request))
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
documentId = request["documentId"]
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.deleteDocument(documentId)
def processRequest(request):
output = ""
print(request)
jobId = request['jobId']
jobTag = request['jobTag']
jobStatus = request['jobStatus']
jobAPI = request['jobAPI']
bucketName = request['bucketName']
objectName = request['objectName']
outputTable = request["outputTable"]
outputBucket = request["outputBucket"]
documentsTable = request["documentsTable"]
qUrl = request["elasticQueueUrl"]
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
opg = OutputGenerator(jobId, jobTag, pages, outputBucket, objectName,
detectForms, detectTables, ddb)
opg.run()
print("DocumentId: {}".format(jobTag))
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.markDocumentComplete(jobTag, jobId)
jsonMessage = {
'documentId': jobTag,
'jobId': jobId,
'bucketName': outputBucket,
'objectName': objectName
}
client = AwsHelper().getClient('sqs')
postMessage(client, qUrl, jsonMessage)
output = "Processed -> Document: {}, Object: {}/{} processed.".format(
jobTag, bucketName, objectName)
print(output)
return {'statusCode': 200, 'body': output}
def dataStore_getDocuments():
#Document
print("*******************")
dstore = datastore.DocumentStore(documentsTableName, outputTableName)
docs = dstore.getDocuments()
print(docs)
print("------------")
while ("nextToken" in docs):
print(docs["nextToken"])
docs = dstore.getDocuments(docs["nextToken"])
print(docs)
print("------------")
def createDocument(request):
print("CreateDocument request: {}".format(request))
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
bucketName = request["bucketName"]
objectName = request["objectName"]
ds = datastore.DocumentStore(documentsTable, outputTable)
documentId = str(uuid.uuid1())
ds.createDocument(documentId, bucketName, objectName)
output = {"documentId": documentId}
return output
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.run()
generatePdf(jobTag, bucketName, objectName, outputBucketName)
print("DocumentId: {}".format(jobTag))
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.markDocumentComplete(jobTag)
output = "Processed -> Document: {}, Object: {}/{} processed.".format(
jobTag, bucketName, objectName)
print(output)
return {'statusCode': 200, 'body': output}
def setUp(self):
self.conn = boto3.client('dynamodb', region_name=REGION)
self.conn.create_table(
TableName=DOCUMENTS_TABLE_NAME,
KeySchema=[{
"AttributeName": "documentId",
"KeyType": "HASH"
}],
AttributeDefinitions=[{
"AttributeName": "documentId",
"AttributeType": "S"
}],
ProvisionedThroughput={
"ReadCapacityUnits": 5,
"WriteCapacityUnits": 5
},
)
self.conn.put_item(TableName=DOCUMENTS_TABLE_NAME,
Item={
"documentId": {
"S": "b1a54fda-1809-49d7-8f19-0d1688eb65b9"
},
"objectName": {
"S": "public/samples/Misc/expense.png"
},
"bucketName": {
"S": "dusstack-sample-s3-bucket"
},
"documentStatus": {
"S": "IN_PROGRESS"
}
})
self.conn.put_item(TableName=DOCUMENTS_TABLE_NAME,
Item={
"documentId": {
"S": "b1a99fda-1809-49d7-8f19-0d1688eb65b9"
},
"objectName": {
"S": "public/samples/Misc/expense.png"
},
"bucketName": {
"S": "dusstack-sample-s3-bucket"
},
"documentStatus": {
"S": "IN_PROGRESS"
}
})
self.ds = datastore.DocumentStore(DOCUMENTS_TABLE_NAME,
OUTPUT_TABLE_NAME)
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 getPageResponse(request):
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
documentId = request["documentId"]
page = request["page"]
ds = datastore.DocumentStore(documentsTable, outputTable)
doc = ds.getDocument(documentId)
if(doc and doc["documentStatus"] == "SUCCEEDED"):
fileName = "{}-analysis/{}/page-{}-response.json".format(doc["objectName"], doc["documentId"], page)
responseJson = json.loads(S3Helper.readFromS3(doc["bucketName"], fileName))
doc["textractResponse"] = responseJson
output = {}
if(doc):
output = doc
return output
def processRequest(request):
output = ""
print(request)
jobId = request["jobId"]
jobTag = request["jobTag"]
jobStatus = request["jobStatus"]
jobAPI = request["jobAPI"]
bucketName = request["bucketName"]
objectName = request["objectName"]
outputTable = request["outputTable"]
documentsTable = request["documentsTable"]
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, bucketName, objectName, detectForms, detectTables, ddb
)
opg.run()
print("DocumentId: {}".format(jobTag))
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.markDocumentComplete(jobTag)
output = "Processed -> Document: {}, Object: {}/{} processed.".format(
jobTag, bucketName, objectName
)
print(output)
return {"statusCode": 200, "body": output}
def getPageForm(request):
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
documentId = request["documentId"]
page = request["page"]
ds = datastore.DocumentStore(documentsTable, outputTable)
doc = ds.getDocument(documentId)
if(doc and doc["documentStatus"] == "SUCCEEDED"):
fileName = "{}-analysis/{}/page-{}-forms.csv".format(doc["objectName"], doc["documentId"], page)
file = S3Helper.readFromS3(doc["bucketName"], fileName)
doc["textractResponse"] = parsePairs(file)
output = {}
print(output)
if(doc):
output = doc
return output
def getPageTable(request):
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
documentId = request["documentId"]
page = request["page"]
ds = datastore.DocumentStore(documentsTable, outputTable)
doc = ds.getDocument(documentId)
if (doc and doc["documentStatus"] == "SUCCEEDED"):
fileName = "{}-analysis/{}/page-{}-tables.csv".format(
doc["objectName"], doc["documentId"], page)
file = S3Helper.readFromS3(doc["bucketName"], fileName)
tables = parseTables(getTableFromString(file))
output = {"tables": []}
if (tables):
output["tables"] = tables
return output
def getDocument(request):
print("GetDocument request: {}".format(request))
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
documentId = request["documentId"]
ds = datastore.DocumentStore(documentsTable, outputTable)
doc = ds.getDocument(documentId)
output = {}
if (doc):
output = doc
return output
def processRequest(request):
output = ""
print("request: {}".format(request))
bucketName = request["bucketName"]
objectName = request["objectName"]
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
jobId = request["jobId"]
invocationId = request["invocationId"]
invocationSchemaVersion = request["invocationSchemaVersion"]
taskId = request["taskId"]
print("Input Object: {}/{}".format(bucketName, objectName))
ext = FileHelper.getFileExtenstion(objectName.lower())
print("Extension: {}".format(ext))
if ext and ext in ["jpg", "jpeg", "png", "pdf"]:
documentId = str(uuid.uuid1())
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.createDocument(documentId, bucketName, objectName)
output = "Saved document {} for {}/{}".format(documentId, bucketName,
objectName)
print(output)
results = [{
"taskId":
taskId,
"resultCode":
"Succeeded",
"resultString":
"Document submitted for processing with Id: {}".format(documentId),
}]
return {
"invocationSchemaVersion": invocationSchemaVersion,
"treatMissingKeysAs": "PermanentFailure",
"invocationId": invocationId,
"results": results,
}
def processRequest(request):
output = ""
print("request: {}".format(request))
bucketName = request["bucketName"]
objectName = request["objectName"]
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
jobId = request["jobId"]
invocationId = request['invocationId']
invocationSchemaVersion = request['invocationSchemaVersion']
taskId = request['taskId']
print("Input Object: {}/{}".format(bucketName, objectName))
ext = FileHelper.getFileExtenstion(objectName.lower())
print("Extension: {}".format(ext))
if (ext and ext in ["jpg", "jpeg", "png", "pdf"]):
documentId = str(uuid.uuid1())
ds = datastore.DocumentStore(documentsTable, outputTable)
ds.createDocument(documentId, bucketName, objectName)
output = "Saved document {} for {}/{}".format(documentId, bucketName,
objectName)
print(output)
results = [{
'taskId':
taskId,
'resultCode':
'Succeeded',
'resultString':
"Document submitted for processing with Id: {}".format(documentId)
}]
return {
'invocationSchemaVersion': invocationSchemaVersion,
'treatMissingKeysAs': 'PermanentFailure',
'invocationId': invocationId,
'results': results
}
def createDocument(request):
print("CreateDocument request: {}".format(request))
documentsTable = request["documentsTable"]
outputTable = request["outputTable"]
bucketName = request["bucketName"]
objectName = request["objectName"]
ds = datastore.DocumentStore(documentsTable, outputTable)
objectRootPrefix = objectName.split('/')[1]
# if one of the available sample files, backend has to generate UUID.
if objectRootPrefix == 'samples':
documentId = generateDocumentID(bucketName)
else:
documentId = objectRootPrefix
ds.createDocument(documentId, bucketName, objectName)
output = {"documentId": documentId}
return output
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}