Python script works locally but encounters HTTPS timeout on PythonAnywhere [duplicate]

I have a Python script that fetches data from an API (specifically www.nseindia.com/api/corporate-announcements). When I run the script locally in PyCharm, it executes without any issues and fetches the data quickly. However, when I run the same script on PythonAnywhere, I encounter a timeout error:

HTTPSConnectionPool(host='www.nseindia.com', port=443): Read timed out. (read timeout=300)

My Python Code

import requestsimport pandas as pdimport gspreadfrom oauth2client.service_account import ServiceAccountCredentialsimport scheduleimport timeimport numpy as np# Define Google Sheets credentialsscope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']credentials = ServiceAccountCredentials.from_json_keyfile_name('announcement-capture-7bef8a7ce6eb.json', scope)client = gspread.authorize(credentials)# Google Sheets document IDsheet_id = '1NKSFgnIUB3dW7Y0ZHCfXRd-B6QEYmCdW3km8g61X_bM'headers = {'Accept-Encoding': 'gzip, deflate, br, zstd','Accept-Language': 'en-US,en;q=0.9','User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36'}baseurl = 'https://www.nseindia.com/'url = 'https://www.nseindia.com/api/corporate-announcements?index=equities'def get_announcements_nse():    try:        session = requests.Session()        request = session.get(baseurl, headers=headers, timeout=300)        cookies = dict(request.cookies)        data = session.get(url, headers=headers, timeout=300, cookies=cookies)        data.raise_for_status()  # Raise HTTPError for bad responses        json_data = data.json()        if not json_data:  # Check if JSON data is empty            print("No announcements found.")            return pd.DataFrame(                columns=['Symbol', 'Name', 'Description', 'Announcement_Date', 'Announcement_Text', 'Attachment_File'])        else:            # Select only the desired columns from the JSON response            selected_columns = ['symbol', 'sm_name', 'desc', 'an_dt', 'attchmntText', 'attchmntFile']            # Create the DataFrame with selected columns            announcements = pd.DataFrame(json_data)[selected_columns]            # Rename the columns to match your Google Sheets column names            announcements.columns = ['Symbol', 'Name', 'Description', 'Announcement_Date', 'Announcement_Text','Attachment_File']            # Filter out announcements with specific keywords in 'Description' column            keywords_to_skip = ['Disclosure', 'Certificate', 'Spurt', 'Loss', 'Duplicate', 'Share Certificate','Price Movement', 'Price']            announcements_filtered = announcements[                ~announcements['Description'].str.contains('|'.join(keywords_to_skip),                                                           case=False)]            return announcements_filtered    except (requests.exceptions.RequestException, ValueError) as e:        print(f"An error occurred: {e}")        return pd.DataFrame(            columns=['Symbol', 'Name', 'Description', 'Announcement_Date', 'Announcement_Text', 'Attachment_File'])def update_google_sheet():    # Get NSE announcements    announcements_nse = get_announcements_nse()    # Open the Google Sheets document by ID    sheet = client.open_by_key(sheet_id).sheet1    # Define the expected headers    expected_headers = ['Symbol', 'Name', 'Description', 'Announcement_Date', 'Attachment_Text', 'Attachment_File']    # If the sheet is empty with only column names, directly add all NSE announcements    if len(sheet.get_all_values()) == 1:  # Check if the sheet has only column names        # Replace NaN values with a placeholder        announcements_nse.replace({np.nan: ''}, inplace=True)        for index, row in announcements_nse.iterrows():            # Construct the URL with the symbol            url = f'https://www.nseindia.com/get-quotes/equity?symbol={row["Symbol"]}'            # Insert the URL into the appropriate cell in the sheet            sheet.insert_row([url] + row.tolist()[1:], index + 2)    else:        # Get existing data in the sheet        existing_data = sheet.get_all_records(expected_headers=expected_headers)        # Convert existing data to DataFrame        existing_df = pd.DataFrame(existing_data)        # Filter out existing announcements from new NSE announcements        new_announcements_nse = announcements_nse.merge(existing_df, on=['Symbol', 'Name', 'Announcement_Date'],                                                        how='left', indicator=True)        new_announcements_nse = new_announcements_nse[new_announcements_nse['_merge'] == 'left_only'].drop(            columns=['_merge'])        # Replace NaN values with a placeholder        new_announcements_nse.replace({np.nan: ''}, inplace=True)        # Append new NSE announcements to the top of the sheet        for index, row in new_announcements_nse.iterrows():            # Construct the URL with the symbol            url = f'https://www.nseindia.com/get-quotes/equity?symbol={row["Symbol"]}'            # Insert the URL into the appropriate cell in the sheet            sheet.insert_row([url] + row.tolist()[1:], index + 2)    print("NSE data updated successfully!")# Schedule the job to run every 120 secondsschedule.every(120).seconds.do(update_google_sheet)while True:    schedule.run_pending()    time.sleep(1)

The timeout occurs consistently on PythonAnywhere, even after adjusting timeout settings in the script. What could be causing this timeout issue on PythonAnywhere, and how can I troubleshoot or resolve it? Are there any specific configurations or settings I need to adjust on PythonAnywhere to prevent the timeout?

Any insights or suggestions would be greatly appreciated! Thank you.

I tried increasing the time also to 300, but still its giving the timeout error. I am expecting to run this wihtout error same as it was running in my local computer.