scraper.py

import os
import io
import time
import math
import traceback
from datetime import datetime

import pandas as pd
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import (
    NoSuchElementException,
    TimeoutException,
    StaleElementReferenceException,
)
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service

from huggingface_hub import HfApi, HfFolder

# Configuration: set via Space secrets
HF_REPO_ID = os.environ.get("HF_DATASET_REPO", "your-username/thingiverse-data")
HF_TOKEN   = HfFolder.get_token()


def upload_df_to_hf(df: pd.DataFrame, filename: str):
    """
    Upload a pandas DataFrame directly to HF dataset without writing to disk.
    """
    buffer = io.StringIO()
    df.to_csv(buffer, index=False)
    buffer.seek(0)
    api = HfApi()
    api.upload_file(
        path_or_fileobj=buffer,
        path_in_repo=filename,
        repo_id=HF_REPO_ID,
        repo_type="dataset",
        token=HF_TOKEN,
        create_pr=False,
    )
    print(f"✅ Uploaded {filename} to {HF_REPO_ID}")


def safe_get_text(driver, xpath, retries=1, delay=0.5):
    for _ in range(retries):
        try:
            return driver.find_element(By.XPATH, xpath).text
        except (StaleElementReferenceException, NoSuchElementException):
            time.sleep(delay)
    return ""


def initialize_driver():
    # path to the chromedriver you installed in Dockerfile
    service = Service("/usr/local/bin/chromedriver")

    options = Options()
    # tell Selenium exactly where Chrome itself lives
    options.binary_location = "/usr/bin/google-chrome-stable"
    options.add_argument("--headless=new")
    options.add_argument("--disable-gpu")
    options.add_argument("--no-sandbox")
    options.add_argument("--disable-dev-shm-usage")
    options.add_argument("--window-size=1920,1080")

    # launch Chrome via the Service
    driver = webdriver.Chrome(service=service, options=options)
    return driver


def process_batch(start_id, end_id, worker_id):
    print(f"Worker {worker_id} processing IDs {start_id} to {end_id}")
    try:
        driver = initialize_driver()
        wait = WebDriverWait(driver, 10)
        results = []

        total = end_id - start_id + 1
        count = 0
        for thing_id in range(end_id, start_id - 1, -1):
            count += 1
            url = f"https://www.thingiverse.com/thing:{thing_id}"
            print(f"[{worker_id}] ({count}/{total}) {url}")
            try:
                driver.get(url)
            except Exception:
                continue
            time.sleep(1)

            # skip error pages
            try:
                driver.find_element(By.XPATH, "//*[contains(@class,'Layout__errorPageCard')]")
                continue
            except NoSuchElementException:
                pass

            # wait for title link
            try:
                wait.until(EC.presence_of_element_located((
                    By.XPATH, "//a[contains(@class,'DetailPageTitle__thingTitleLink')]"
                )))
            except TimeoutException:
                continue

            title = safe_get_text(driver, "//*[contains(@class,'DetailPageTitle__thingTitleName')]")
            author = safe_get_text(driver, "//a[contains(@class,'DetailPageTitle__thingTitleLink')]")
            date_posted = safe_get_text(
                driver,
                "//a[contains(@class,'DetailPageTitle__thingTitleLink')]/following-sibling::div"
            )

            def extract_aria(label):
                try:
                    return driver.find_element(By.XPATH, f"//*[@aria-label='{label}']").text
                except NoSuchElementException:
                    return ""

            remixes  = extract_aria("Remixes")
            files    = extract_aria("Files")
            makes    = extract_aria("Makes")
            comments = extract_aria("Comments")

            tags = []
            try:
                tags_el = driver.find_element(By.XPATH, "//*[contains(@class,'TagList__tagList')]")
                tags = [a.text for a in tags_el.find_elements(By.TAG_NAME, "a")]
            except NoSuchElementException:
                pass

            results.append({
                "URL": url,
                "Title": title,
                "Author": author,
                "Date": date_posted,
                "Remixes": remixes,
                "Files": files,
                "Makes": makes,
                "Comments": comments,
                "Tags": tags
            })

            # checkpoint every 10 items
            if len(results) % 10 == 0:
                df_chk = pd.DataFrame(results)
                chk_name = f"worker_{worker_id}_{start_id}_{end_id}_chk_{len(results)}.csv"
                upload_df_to_hf(df_chk, chk_name)

        # final batch upload
        if results:
            df_final = pd.DataFrame(results)
            final_name = f"worker_{worker_id}_{start_id}_{end_id}_final.csv"
            upload_df_to_hf(df_final, final_name)

        driver.quit()
        return results

    except Exception as e:
        print(f"Worker {worker_id} error: {e}")
        traceback.print_exc()
        return []


def main():
    # configure your range & parallelism
    start_thing = 6993281
    end_thing   = 7003281
    num_workers = 5

    # split work
    total = end_thing - start_thing + 1
    per   = math.ceil(total / num_workers)
    batches = []
    for i in range(num_workers):
        s = start_thing + i * per
        e = min(s + per - 1, end_thing)
        batches.append((s, e, i+1))

    all_results = []
    from concurrent.futures import ThreadPoolExecutor, as_completed
    with ThreadPoolExecutor(max_workers=num_workers) as ex:
        futures = {ex.submit(process_batch, s, e, wid): (s,e,wid)
                   for s,e,wid in batches}
        for fut in as_completed(futures):
            all_results.extend(fut.result())

    # upload combined file
    if all_results:
        df_all = pd.DataFrame(all_results)
        upload_df_to_hf(df_all, f"thingiverse_{start_thing}_{end_thing}_all.csv")


if __name__ == "__main__":
    main()