Skip to main content

Do I need to clear local storage on the frontend if my laravel controller logout() is handling the logging out of a user?

I have a function in my controller that's supposed to log a user out. It returns a success message upon clicking Logout on the frontend. My question is, on the JS side, do I need to clear local storage?

I'm asking this because upon refreshing my page after a successful logout and landing on the login screen (when logging out, the user gets redirected from the dashboard to the login screen) - I get sent back to dashboard which to me indicates that my logout function isn't working as intended (or is it?).

Am I doing something wrong?

Here's my controller code:

public function logout(Request $request) {

    try {
        $this->_usersRepository->userLogout();
        $loggedOut = $this->_usersRepository->userLogout()->getStatusCode();

        if($loggedOut != 200) {
            return response()->json(['message' => 'Error while logging out!'], 500);
        }
        return response()->json([
            'message' => 'Successfully logged out',
            'loggedOut' => $loggedOut
        ]);
    } catch (\Exception $e) {
        Log::error($e->getMessage());
        throw new \Exception($e->getMessage(), $e->getCode(), $e);
    }

}

Respository code for deleting oauth_access_tokens in the DB:

public function userLogout() {
    DB::table('oauth_access_tokens')->where('user_id', Auth::id())->delete();
    return response()->json(['message' => 'User successfully signed out'], 200);
}


source https://stackoverflow.com/questions/68966884/do-i-need-to-clear-local-storage-on-the-frontend-if-my-laravel-controller-logout

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...