Upload trendsscraper_public.py

trendsscraper_public.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov 28 11:04:34 2024
+
+@author: aleksandra urman
+"""
+
+#this is a single iteration of the scraper, to run daily we have a cron job set up
+
+import asyncio
+from playwright.async_api import async_playwright
+import os
+import pandas as pd
+import random
+import time
+
+# Get the current working directory
+current_dir = os.getcwd()
+
+#if wrong, set to where it should be
+#os.chdir('')
+
+
+#read in the trends master list (available as part of the released dataset)
+df = pd.read_csv('Trends_LocationList.csv', encoding='utf-8')
+
+# Function to scrape data for a specific tag
+async def scrape_data(playwright, tag):
+    # Launch the browser in non-headless mode
+    #for testing purposes, one might want to first run this with headless=False
+    browser = await playwright.chromium.launch(headless=True)
+
+    # Define the folder path for the tag
+    base_dir = os.getcwd()  # Current working directory
+    tag_dir = os.path.join(base_dir, "data", str(tag))
+    os.makedirs(tag_dir, exist_ok=True)  # Ensure the directory exists
+
+    # Use the tag directory as the download directory
+    context = await browser.new_context(accept_downloads=True)
+    page = await context.new_page()
+
+    # Replace 'US' in the URL with the tag value
+    url = f"https://trends.google.com/trending?geo={tag}&hours=24"
+    await page.goto(url, wait_until="networkidle")
+    
+    random_sleep = random.randint(1, 5)
+    await asyncio.sleep(random_sleep)
+
+    # Interact with the page elements
+    await page.locator("button", has_text="Export").click()
+    random_sleep = random.randint(1, 5)
+    await asyncio.sleep(random_sleep)  # Adjust if less time is sufficient
+
+    # Handle the download using async context manager
+    async with page.expect_download() as download_info:
+        await page.get_by_role("menuitem", name="Download CSV").click()
+    download = await download_info.value
+
+    # Save the downloaded file to the tag directory
+    save_path = os.path.join(tag_dir, download.suggested_filename)
+    await download.save_as(save_path)
+
+    # Close the context and browser
+    await context.close()
+    await browser.close()
+    print(f"Downloaded data for tag: {tag} into {save_path}")
+
+"""
+# FOR TESTS ONLY to iterate through the first 3 tags
+async def main():
+    async with async_playwright() as playwright:
+        # Get the first 3 tags
+        first_three_tags = df['tag'][:1]
+
+        # Iterate through these tags and scrape data
+        for tag in first_three_tags:
+            try:
+                await scrape_data(playwright, tag)
+            except Exception as e:
+                print(f"Error scraping data for tag {tag}: {e}")
+
+"""
+
+# Main function to iterate through tags
+async def main():
+    async with async_playwright() as playwright:
+        for tag in df['tag']:
+            try:
+                await scrape_data(playwright, tag)
+            except Exception as e:
+                print(f"Error scraping data for tag {tag}: {e}")
+
+
+#Some helpers, comment or uncomment if needed
+# Measure the total execution time
+#start_time = time.time()  # Start timing
+#asyncio.run(main())  # Run the main function
+#end_time = time.time()  # End timing
+
+# Calculate the total time taken
+#total_time = end_time - start_time
+
+# Save the total execution time to a text file in the working directory
+#time_file_path = os.path.join(current_dir, "execution_time.txt")
+#with open(time_file_path, "w") as time_file:
+#    time_file.write(f"Total execution time: {total_time:.2f} seconds\n")
+
+#print(f"Total execution time: {total_time:.2f} seconds. Saved to 'execution_time.txt'.")