Skip to main content

How to download chromedriver instead of webpage with python requests library

Today I'm creating a Python script to find a specific version of chromedriver here. My intention is to make a request to the URL containing the specific version of chromedriver I need to download and save it to the same directory as the script. However, each time I run my code to install a file, say chromedriver_win32.zip in this folder, I end up downloading the webpage it is stored on instead of the file itself.

Here's my script:

#Get the exact folder name containing the version we need to download
chromedriverVersionUrl = "https://chromedriver.storage.googleapis.com/LATEST_RELEASE_100"
response = requests.get(chromedriverVersionUrl)
latestVersionNumber = response.text

#Target the folder of the exact version we need to download and download it!
downloadUrl = "https://chromedriver.storage.googleapis.com/index.html?path=" + latestVersionNumber + "/chromedriver_win32.zip"

#Make the request and write the response to a file
r = requests.get(downloadUrl, allow_redirects=True)
open('chromedriver.zip', 'wb').write(r.content)

Every time, chromedriver.zip ends up being a very small file that windows says is a corrupted zip file. I tried downloading the contents as a .txt file and it turned out I was just downloading the webpage.

I have tried using wget and the dload libraries to download this file in addition to requests, but they have all yielded the same result. Could someone please show me what I might be doing wrong?



source https://stackoverflow.com/questions/75803718/how-to-download-chromedriver-instead-of-webpage-with-python-requests-library

Labels

Labels:

Comments

Post a Comment

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...
Post a Comment
Read more

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
Post a Comment
Read more

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...
Post a Comment
Read more