I know there is a way by which we can call Document AI from python environment in local system. In that process one needs to upload the local file to GCS bucket so that Document AI can access the file from there. Is there any way by which we can give direct access of local files to Document AI (i.e., without uploading the file to GCS bucket) using python? [Note that it's a mandatory requirement for me to run python code in local system, not in GCP.]
DocumentAI cannot "open" files by itself from your local filesystem.
If you don't want / cannot upload the documents to a bucket, you can send them in as part of the REST API. BUT in this case you cannot use BatchProcessing: I mean, you must process the files one by one and wait for a response.
The relevant REST API documentation is here: https://cloud.google.com/document-ai/docs/reference/rest/v1/projects.locations.processors/process
In the quickstart documentation for python you've got this sample code that reads a file and sends it inline as part of the request:
# The full resource name of the processor, e.g.:
# projects/project-id/locations/location/processor/processor-id
# You must create new processors in the Cloud Console first
name = f"projects/{project_id}/locations/{location}/processors/{processor_id}"
# Read the file into memory
with open(file_path, "rb") as image:
image_content = image.read()
document = {"content": image_content, "mime_type": "application/pdf"}
# Configure the process request
request = {"name": name, "raw_document": document}
result = client.process_document(request=request)
Related
I'm trying to retrieve a DataFrame with pd.read_parquet and I get the following error:
OSError: Passed non-file path: my/path
I access the .parquet file in GCS with a path with prefix gs://
For some unknown reason, the OSError shows the path without the prefix gs://
I did suspect it was related to credentials, however from my Mac I can use gsutil with no problem. I can access and download the parquet file via the Google Cloud Platform. I just can't read the .parquet file directly in the gs:// path. Any ideas why not?
Arrow does not currently natively support loading data from GCS. There is some progress in implementing this the tasks are tracked in JIRA.
As a workaround you should be able to use the fsspec GCS filesystem implementation to access objects (I would try installing it and using 'gcs://' instead 'gs://'), or opening the file directly with the gcsfs api and passing it through.
I am accessing a website that allows me to download CSV file. I would like to store the CSV file directly to the blob container. I know that one way is to download the file locally and then upload the file, but I would like to skip the step of downloading the file locally. Is there a way in which I could achieve this.
i tried the following:
block_blob_service.create_blob_from_path('containername','blobname','https://*****.blob.core.windows.net/containername/FlightStats',content_settings=ContentSettings(content_type='application/CSV'))
but I keep getting errors stating path is not found.
Any help is appreciated. Thanks!
The file_path in create_blob_from_path is the path of your local file, looks like "C:\xxx\xxx". This path('https://*****.blob.core.windows.net/containername/FlightStats') is Blob URL.
You could download your file to byte array or stream, then use create_blob_from_bytes or create_blob_from_stream method.
Other answer uses the so called "Azure SDK for Python legacy".
I recommend that if it's fresh implementation then use Gen2 Storage Account (instead of Gen1 or Blob storage).
For Gen2 storage account, see example here:
from azure.storage.filedatalake import DataLakeFileClient
data = b"abc"
file = DataLakeFileClient.from_connection_string("my_connection_string",
file_system_name="myfilesystem", file_path="myfile")
file.append_data(data, offset=0, length=len(data))
file.flush_data(len(data))
It's painful, if you're appending multiple times then you'll have to keep track of offset on client side.
I am trying to write a new file (not upload an existing file) to a Google Cloud Storage bucket from inside a Python Google Cloud Function.
I tried using google-cloud-storage but it does not have the
"open" attribute for the bucket.
I tried to use the App Engine library GoogleAppEngineCloudStorageClient but the function cannot deploy with this dependencies.
I tried to use gcs-client but I cannot pass the credentials inside the function as it requires a JSON file.
Any ideas would be much appreciated.
Thanks.
from google.cloud import storage
import io
# bucket name
bucket = "my_bucket_name"
# Get the bucket that the file will be uploaded to.
storage_client = storage.Client()
bucket = storage_client.get_bucket(bucket)
# Create a new blob and upload the file's content.
my_file = bucket.blob('media/teste_file01.txt')
# create in memory file
output = io.StringIO("This is a test \n")
# upload from string
my_file.upload_from_string(output.read(), content_type="text/plain")
output.close()
# list created files
blobs = storage_client.list_blobs(bucket)
for blob in blobs:
print(blob.name)
# Make the blob publicly viewable.
my_file.make_public()
You can now write files directly to Google Cloud Storage. It is no longer necessary to create a file locally and then upload it.
You can use the blob.open() as follows:
from google.cloud import storage
def write_file():
client = storage.Client()
bucket = client.get_bucket('bucket-name')
blob = bucket.blob('path/to/new-blob.txt')
with blob.open(mode='w') as f:
for line in object:
f.write(line)
You can find more examples and snippets here:
https://github.com/googleapis/python-storage/tree/main/samples/snippets
You have to create your file locally and then to push it to GCS. You can't create a file dynamically in GCS by using open.
For this, you can write in the /tmp directory which is an in memory file system. By the way, you will never be able to create a file bigger than the amount of the memory allowed to your function minus the memory footprint of your code. With a function with 2Gb, you can expect a max file size of about 1.5Gb.
Note: GCS is not a file system, and you don't have to use it like this
EDIT 1
Things have changed since my answer:
It's now possible to write in any directory in the container (not only the /tmp)
You can stream write a file in GCS, as well as you receive it in streaming mode on CLoud Run. Here a sample to stream write to GCS.
Note: stream write deactivate the checksum validation. Therefore, you won't have integrity checks at the end of the file stream write.
How can I read the data stored in my Cloud Storage bucket of my project and use it in my Python code that I am writing in App Engine?
I tried using:
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
But I am unable to figure out how to extract actual data from the code to get it in a usable form.
Any help would be appreciated.
Getting a file from a Google Cloud Storage bucket means that you are just getting an object. This concept abstract the file itself from your code. You will either need to store locally the file to perform any operation on it or depending on the extension of your file put that object inside of a file readstreamer or the method that you need to read the file.
Here you can see a code example on how to read a file from app engine:
def read_file(self, filename):
self.response.write('Reading the full file contents:\n')
gcs_file = gcs.open(filename)
contents = gcs_file.read()
gcs_file.close()
self.response.write(contents)
You have a couple of options.
content = blob.download_as_string() --> Converts the content of your Cloud Storage object to String.
blob.download_to_file(file_obj) --> Updates an existing file_obj to include the Cloud Storage object content.
blob.download_to_filename(filename) --> Saves the object in a file. On App Engine Standard environment, you can store files in /tmp/ directory.
Refer this link for more information.
I would like to upload invoking a REST endpoint in multi-part.
In particular, I am looking at this API: Google Cloud Storage: Objects: insert
I did read about using multer, however I did not find any complete example showing me how to perform this operation.
Could someone help me with that?
https://cloud.google.com/nodejs/getting-started/using-cloud-storage#uploading_to_cloud_storage
^^ this is a a good example of how to use multer to upload a single image to Google Cloud Storage. Use multer to create filestream for each file ( storage: multer.memoryStorage() ), and handle the file stream by sending it to your GCS bucket in your callback.
However link only shows an example for one image. If you want to do an array of images, create a for-loop, where you create a stream for each file in your request, but only put the next() function after the for loop ends. If you keep the next(); in each loop cycle you will get the error: Error: Can't set headers after they are sent.
There is an example for uploading files with the nodejs client library and multer. You can modify this example and set the multipart option:
Download the sample code and cd into the folder:
git clone https://github.com/GoogleCloudPlatform/nodejs-docs-samples/
cd nodejs-docs-samples/appengine/storage
Edit the app.yaml file and include your bucket name:
GCLOUD_STORAGE_BUCKET: YOUR_BUCKET_NAME
Then in the source code, you can modify the publicUrl variable according to Objects: insert example:
const publicUrl = format(`https://www.googleapis.com/upload/storage/v1/b/${bucket.name}/o?uploadType=multipart`);
Download a key file for your service account and set the environment variable:
Go to the Create service account key page in the GCP Console.
From the Service account drop-down list, select New service account.
Input a name into the Service account name field.
From the Role drop-down list, select Project > Owner.
Click Create. A JSON file that contains your key downloads to your computer. And finally export the environment variable:
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/key/file
After that, yo're ready to run npm start and go to the app's frontend and upload your file: