gistfile1.txt

!pip install azure-ai-formrecognizer
!pip install azure-storage-blob

def train_model(form_training_client, container_sas_url, container_name):

        poller = form_training_client.begin_training(
            container_sas_url, use_training_labels=True, model_name=container_name
        )
        model = poller.result()

        # Custom model information
        print("Model ID: {}".format(model.model_id))
        print("Status: {}".format(model.status))
        print("Model name: {}".format(model.model_name))
        print("Is this a composed model?: {}".format(model.properties.is_composed_model))
        print("Training started on: {}".format(model.training_started_on))
        print("Training completed on: {}".format(model.training_completed_on))

        print("Recognized fields:")
        # looping through the submodels, which contains the fields they were trained on
        # The labels are based on the ones you gave the training document.
        for submodel in model.submodels:
            print("...The submodel has model ID: {}".format(submodel.model_id))
            print("...The submodel with form type {} has an average accuracy '{}'".format(
                submodel.form_type, submodel.accuracy
            ))
            for name, field in submodel.fields.items():
                print("...The model found the field '{}' with an accuracy of {}".format(
                    name, field.accuracy
                ))

        # Training result information
        for doc in model.training_documents:
            print("Document name: {}".format(doc.name))
            print("Document status: {}".format(doc.status))
            print("Document page count: {}".format(doc.page_count))
            print("Document errors: {}".format(doc.errors))
        return model
        
        

from azure.storage.blob import BlobServiceClient
from datetime import datetime, timedelta
from azure.storage.blob import BlobClient, generate_container_sas, ContainerSasPermissions
from azure.ai.formrecognizer import FormTrainingClient
endpoint = "Your Form Recognizer Endpoint"
key = "Your Form Recognizer Key"
storage_account = "Your storage account name"
storage_key = "Your storage key"
service = BlobServiceClient(account_url=f"https://{storage_account}.blob.core.windows.net/", credential=storage_key)
containers = service.list_containers()
form_training_client = FormTrainingClient(endpoint, AzureKeyCredential(key))
models = []
for container in containers:
    print(container.name)
   
    sas_container = generate_container_sas(account_name=storage_account, 
                        container_name=container.name ,
                        account_key=storage_key,
                        permission=ContainerSasPermissions(read=True, write=True, list=True),
                        expiry=datetime.utcnow() + timedelta(hours=1))
    print(sas_blob)

    container_name = container.name
    sas_uri = f"https://{storage_account}.blob.core.windows.net/{container_name}?{sas_container}"
    print(sas_uri)
    models.add( train_model(form_training_client, sas_uri, "test"))