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from typing import Any, Dict, Optional
import requests
from grobid_tei_xml import parse_document_xml
from .ia import WaybackClient
from .misc import gen_file_metadata
from .workers import SandcrawlerFetchWorker, SandcrawlerWorker
class GrobidClient(object):
def __init__(self, host_url: str = "http://grobid.qa.fatcat.wiki", **kwargs):
self.host_url = host_url
self.consolidate_mode = int(kwargs.get("consolidate_mode", 0))
def process_fulltext(
self, blob: bytes, consolidate_mode: Optional[int] = None
) -> Dict[str, Any]:
"""
Returns dict with keys:
- status_code
- status (slug)
- error_msg (if status == 'error')
- tei_xml (if status is 200)
TODO: persist connection for performance?
"""
assert blob
if consolidate_mode is None:
consolidate_mode = self.consolidate_mode
assert consolidate_mode is not None
try:
grobid_response = requests.post(
self.host_url + "/api/processFulltextDocument",
files={
"input": blob,
"consolidateHeader": consolidate_mode,
"consolidateCitations": 0, # too expensive for now
"includeRawCitations": 1,
},
timeout=180.0,
)
except requests.Timeout:
return {
"status": "error-timeout",
"status_code": -4, # heritrix3 "HTTP timeout" code
"error_msg": "GROBID request (HTTP POST) timeout",
}
info: Dict[str, Any] = dict(status_code=grobid_response.status_code)
if grobid_response.status_code == 200:
info["status"] = "success"
info["tei_xml"] = grobid_response.text
if len(info["tei_xml"]) > 12000000:
# XML is larger than Kafka message size, and much larger than
# an article in general; bail out
info["status"] = "error"
info["error_msg"] = "response XML too large: {} bytes".format(
len(info["tei_xml"])
)
info.pop("tei_xml")
else:
# response.text is .content decoded as utf-8
info["status"] = "error"
info["error_msg"] = grobid_response.text[:10000]
return info
def metadata(self, result: Dict[str, Any]) -> Optional[Dict[str, Any]]:
if result["status"] != "success":
return None
tei_doc = parse_document_xml(result["tei_xml"])
tei_doc.remove_encumbered()
tei_json = tei_doc.to_legacy_dict()
meta = dict()
biblio = dict()
for k in (
"title",
"authors",
"journal",
"date",
"doi",
):
if tei_json.get(k):
biblio[k] = tei_json[k]
meta["biblio"] = biblio
for k in ("grobid_version", "grobid_timestamp", "fatcat_release", "language_code"):
if tei_json.get(k):
meta[k] = tei_json[k]
return meta
class GrobidWorker(SandcrawlerFetchWorker):
def __init__(
self,
grobid_client: GrobidClient,
wayback_client: Optional[WaybackClient] = None,
sink: Optional[SandcrawlerWorker] = None,
**kwargs
):
super().__init__(wayback_client=wayback_client)
self.grobid_client = grobid_client
self.sink = sink
self.consolidate_mode = 0
def timeout_response(self, task: Any) -> Any:
default_key = task["sha1hex"]
return dict(
status="error-timeout",
error_msg="internal GROBID worker timeout",
source=task,
key=default_key,
)
def process(self, record: Any, key: Optional[str] = None) -> Any:
fetch_result = self.fetch_blob(record)
if fetch_result["status"] != "success":
return fetch_result
blob: bytes = fetch_result["blob"]
assert blob and isinstance(blob, bytes)
result = self.grobid_client.process_fulltext(
blob, consolidate_mode=self.consolidate_mode
)
result["file_meta"] = gen_file_metadata(blob)
result["source"] = record
result["key"] = result["file_meta"]["sha1hex"]
return result
class GrobidBlobWorker(SandcrawlerWorker):
"""
This is sort of like GrobidWorker, except it receives blobs directly,
instead of fetching blobs from some remote store.
"""
def __init__(
self, grobid_client: GrobidClient, sink: Optional[SandcrawlerWorker] = None, **kwargs
):
super().__init__()
self.grobid_client = grobid_client
self.sink = sink
self.consolidate_mode = 0
def process(self, blob: Any, key: Optional[str] = None) -> Any:
if not blob:
return None
result = self.grobid_client.process_fulltext(
blob, consolidate_mode=self.consolidate_mode
)
result["file_meta"] = gen_file_metadata(blob)
result["key"] = result["file_meta"]["sha1hex"]
return result
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