Skip to main content

Retrieve and send a Postico bytea image

I have an app which uses AWS Lambda functions to store images in a AWS PostgreSQL RDS as bytea file types.

The app is written in javascript and allows users to upload an image (typically small).

<input
  className={style.buttonInputImage}
  id="logo-file-upload"
  type="file"
  name="myLogo"
  accept="image/*"
  onChange={onLogoChange}
/>

Currently I am not concerned about what format the images are in, although if it makes storage and retrieval easier I could add restrictions.

I am using python to query my database and post and retrieve these files.

INSERT INTO images (logo, background_image, uuid) VALUES ('{0}','{1}','{2}') ON CONFLICT (uuid) DO UPDATE SET logo='{0}', background_image='{1}';".format(data['logo'], data['background_image'], data['id']);

and when I want to retrieve the images:

"SELECT logo, background_image FROM clients AS c JOIN images AS i ON c.id = i.uuid WHERE c.id = '{0}';".format(id);

I try to return this data to the frontend:

    return {
        'statusCode': 200,
        'body': json.dumps(response_list),
         'headers': {
            "Access-Control-Allow-Origin" : "*"
         },
    }

I get the following error: Object of type memoryview is not JSON serializable.

So I have a two part question. First, the images are files being uploaded by a customer (typically they are logos or background images). Does it make sense to store these in my database as bytea files? Or is there a better way to store image uploads.

Second, how do I go about retrieving these files and converting them into a format usable by my front end.



source https://stackoverflow.com/questions/73822668/retrieve-and-send-a-postico-bytea-image

Comments

Popular posts from this blog

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...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...