File size: 6,365 Bytes
7a8b33f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import requests
from bs4 import BeautifulSoup
import json
import json5
import argparse
from pathlib import Path
import multiprocessing as mp
from zsvision.zs_multiproc import starmap_with_kwargs
from pipeline_paths import PIPELINE_PATHS
from datetime import datetime
import urllib.robotparser
import urllib.parse
from utils import get_google_search_results
import time
from random import randint
from fake_useragent import UserAgent
from newspaper import Article, Config
def can_scrape(url, user_agent="*"):
rp = urllib.robotparser.RobotFileParser()
rp.set_url(f"{url.scheme}://{url.netloc}/robots.txt")
# be conservative - if we can't find robots.txt, don't scrapes
try:
rp.read()
ok_to_scrape = rp.can_fetch(user_agent, url.geturl())
except urllib.error.URLError:
ok_to_scrape = False
return ok_to_scrape
def fetch_search_results_to_gather_evidence(
args,
idx: int,
total: int,
search_results_dest_path: Path,
queryset: dict,
):
user_agent = UserAgent()
config = Config()
config.fetch_images = False
print(f"Query {idx}/{total}")
search_results_dest_path.parent.mkdir(exist_ok=True, parents=True)
# check if we already have search_results for this title
if search_results_dest_path.exists() and not args.refresh:
print(f"Found existing search results at {search_results_dest_path}, skipping")
return 0
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.5",
"DNT": "1",
"Connection": "keep-alive",
"Upgrade-Insecure-Requests": "1",
}
# we assume some sites won't permit scraping, so we'll skip these
num_results = args.num_search_results_to_keep + 5
results = {}
for item in queryset:
if item["search_query"] == "no suitable query":
item["search_results"] = []
continue
search_results = get_google_search_results(
query_str=item["search_query"], num_results=num_results
)
if search_results == [{"Result": "No good Google Search Result was found"}]:
item["search_results"] = []
continue
parsed_results = []
for search_result in search_results:
if not can_scrape(
urllib.parse.urlparse(search_result["link"]), user_agent="MyScraper"
):
print(
f"Skipping {search_result['link']} because it doesn't permit scraping"
)
continue
try:
config.browser_user_agent = user_agent.random
article = Article(search_result["link"], language="en", config=config)
article.download()
article.parse()
text = article.text
except Exception as e:
print(f"Error parsing article: {e}, trying with requests.get...")
try:
response = requests.get(
search_result["link"], timeout=15, headers=headers
)
html = response.text
soup = BeautifulSoup(html, features="html.parser")
text = soup.get_text()
except Exception as exception:
print(f"Error parsing article: {exception}")
raise exception
search_result["text"] = text
parsed_results.append(search_result)
if len(parsed_results) == args.num_search_results_to_keep:
break
item["search_results"] = parsed_results
# update the queryset with new information
date_str = datetime.now().strftime("%Y-%m-%d")
results = {"documents": queryset, "dates": {"search_results_fetched": date_str}}
print(
f"Writing web pages for search results for {len(queryset)} queries to {search_results_dest_path}"
)
with open(search_results_dest_path, "w") as f:
f.write(json.dumps(results, indent=4, sort_keys=True))
def main():
args = parse_args()
search_query_paths = list(
PIPELINE_PATHS["search_queries_for_evidence"].glob("**/*.json")
)
if args.limit:
print(f"Limited to {args.limit} search querysets")
search_query_paths = search_query_paths[: args.limit]
kwarg_list = []
for idx, search_query_path in enumerate(search_query_paths):
rel_path = search_query_path.relative_to(
PIPELINE_PATHS["search_queries_for_evidence"]
)
dest_path = PIPELINE_PATHS["google_search_results_evidence"] / rel_path
if dest_path.exists() and not args.refresh:
print(f"For {search_query_path}, found results at {dest_path}, skipping")
continue
with open(search_query_path, "r") as f:
queryset = json.load(f)
kwarg_list.append(
{
"idx": idx,
"total": len(search_query_paths),
"search_results_dest_path": dest_path,
"args": args,
"queryset": queryset,
}
)
# provide the total number of queries to each process
for kwargs in kwarg_list:
kwargs["total"] = len(kwarg_list)
# single process
if args.processes == 1:
cost = 0
for kwargs in kwarg_list:
fetch_search_results_to_gather_evidence(**kwargs)
else: # multiprocess
func = fetch_search_results_to_gather_evidence
with mp.Pool(processes=args.processes) as pool:
starmap_with_kwargs(pool=pool, func=func, kwargs_iter=kwarg_list)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", default="gpt-3.5-turbo", choices=["gpt-4", "gpt-3.5-turbo"]
)
parser.add_argument("--limit", default=0, type=int)
parser.add_argument("--refresh", action="store_true")
parser.add_argument("--num_search_results_to_keep", type=int, default=3)
parser.add_argument("--processes", type=int, default=1)
return parser.parse_args()
if __name__ == "__main__":
main()
|