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)
예제 #2
0
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)
예제 #4
0
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}
예제 #5
0
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}