Skip to main content

How to add GCP bucket to the Firestore with Python SDK?

I am trying to upload the file to the custom Google Cloud Storage bucket with a Flutter web app.

final _storage = FirebaseStorage.instanceFor(bucket: bucketName);
Reference documentRef = _storage.ref().child(filename);
await documentRef.putData(await data);

The code works fine for a default bucket but fails with a new custom GCP bucket.

Error: FirebaseError: Firebase Storage: An unknown error occurred, please check the error payload for server response. (storage/unknown)

The HTTP POST response causing this error says:

{
  "error": {
    "code": 400,
    "message": "Your bucket has not been set up properly for Firebase Storage. Please visit 'https://console.firebase.google.com/project/{my_project_name}/storage/rules' to set up security rules."
  }
}

So apparently, I need to add a new bucket to Firestore and set up access rules before I can upload the file there.

enter image description here

Since these buckets are created automatically by my backend microservice, is there a way to add them to Firestore and set up the rules with Python SDK? Alternatively, is there any other way to upload data to GCP buckets with Flutter besides Firebase Storage?

Thank you.



source https://stackoverflow.com/questions/74910304/how-to-add-gcp-bucket-to-the-firestore-with-python-sdk

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype
Post a Comment
Read more

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...
Post a Comment
Read more

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
Post a Comment
Read more