Skip to main content

Download zip file returned from server in React

The app: I'm building an app that takes screenshots with puppeteer and returns them in a zip file to a react front end.

Relevant technologies: node, react, express, puppeteer, AdmZip

The issue: I can get the data to the point where it triggers the automatic download, but what gets downloaded does not appear to be a proper zip file as I get the following error when attempting to unzip: 'Unable to expand "screenshot-download.zip".

Extra context: To ensure things were working as expected in the process of actually compressing the screenshots into a zip file, I also implemented the "writeZip" method to create a zip file straight from the server and onto my local file system (bypassing converting to buffer and sending to client). This zip file worked as expected and had all the correct contents. This is leading me to believe that the issue is somewhere in the process of sending to client and converting it to something usable.

App.js code (front end):

fetch(`/dcsgrab?tearsheetUrl=${screenShotData}&imagefilelocation=${imageFileLocationData}`)
      .then((response) => response.json())
      .then((data) => {
        const zipBlob = new Blob(data.zipFile.data);
        const url = window.URL.createObjectURL(zipBlob);
        const zipDownload = document.createElement("a");

        setMessageData(data.message);
        setZipData(data.zipFile);

        zipDownload.href = url;
        zipDownload.download = "screenshot-download.zip";
        document.body.appendChild(zipDownload);
        zipDownload.click();
      });
  };

Console log values from returned data (top) and after it's converted to blob (bottom):

{message: 'Screenshots are done!\nPlease check the root directory you previously designated.', zipFile: {ā€¦}}
message: "Screenshots are done!\nPlease check the root directory you previously designated."
zipFile: {type: 'Buffer', data: Array(8207179)}
[[Prototype]]: Object

Blob {size: 21304601, type: ''}
size: 21304601
type: ""
[[Prototype]]: Blob

Server.js code (back end - large chunks of puppeteer code removed to make it easier to read through, if it seems necessary though I will add back in):

app.get('/dcsgrab', (request, response) => {
    const zip = new AdmZip();

    (async () => {

      /**
       * Screenshot the creative elements on the current page
       * @return {Promise.<Array>} Promise which resolves with an array of clipping paths
       */
        async function getScreenShots() {
            const rects = await page.$$eval(PREVIEW_SELECTOR, iframes => {
              return Array.from(iframes, (el) => {
                const {x, y, width, height} = el.getBoundingClientRect();

                return {
                  left: x,
                  top: y,
                  width,
                  height,
                  id: el.id,
                };
              });
            }, PREVIEW_SELECTOR).catch(e => {
              console.error(e.message);
            });

            return Promise.all(rects.map(async (rect) => {
              return await page.screenshot({
                clip: {
                  x: rect.left,
                  y: rect.top,
                  width: rect.width,
                  height: rect.height,
                },
              }).then((content) => {
                zip.addFile(`screenshot-${screenshotCounter++}.png`, Buffer.from(content, "utf8"), "entry comment goes here");
                console.log(`${rect.id} element captured and store in zip`);
              })
                .catch((e) => {
                  console.error(e.message);
                });
            }));
        }

        const zipToSend = zip.toBuffer();

        response.json({ 
            message: 'Screenshots are done!\nPlease check the root directory you previously designated.',
            zipFile: zipToSend
        });
    })();
}); 
Via Active questions tagged javascript - Stack Overflow https://ift.tt/RIXwFcQ

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...