add scraper source

requirements.txt

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+trafilatura
+datasets
+zstandard

scrape.py

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+from trafilatura import fetch_url, extract, extract_metadata
+from datasets import load_dataset, Features, Value, Sequence
+from typing import Dict, List, Any
+from trafilatura.settings import DEFAULT_CONFIG
+from copy import deepcopy
+
+my_config = deepcopy(DEFAULT_CONFIG)
+my_config["DEFAULT"]["DOWNLOAD_TIMEOUT"] = "3"
+my_config["DEFAULT"]["SLEEP_TIME"] = "0"
+
+
+def is_target(batch: Dict[str, List]) -> List[bool]:
+    result = []
+    for tpe, dead, deleted, url in zip(
+        batch["type"], batch["dead"], batch["deleted"], batch["url"]
+    ):
+        if (
+            tpe == "story"
+            and dead is None
+            and deleted is None
+            and url is not None
+            and len(url) > 0
+        ):
+            result.append(True)
+        else:
+            result.append(False)
+    return result
+
+
+def fetch_one(doc: Dict[str, Any]) -> Dict[str, Any]:
+    downloaded = fetch_url(doc["url"], config=my_config)
+    result = {
+        "id": doc["id"],
+        "title": None,
+        "author": None,
+        "markdown": None,
+        "downloaded": False,
+        "meta_extracted": False,
+        "parsed": False,
+        "description": None,
+        "filedate": None,
+        "date": None,
+        "image": None,
+        "pagetype": None,
+        "hostname": None,
+        "sitename": None,
+        "categories": None,
+        "tags": None,
+    }
+    if downloaded:
+        result["downloaded"] = True
+        try:
+            raw_meta = extract_metadata(downloaded)
+            if raw_meta:
+                result["meta_extracted"] = True
+                meta = raw_meta.as_dict()
+                result["title"] = meta.get("title", None)
+                result["author"] = meta.get("author", None)
+                result["description"] = meta.get("description", None)
+                result["filedate"] = meta.get("filedate", None)
+                result["date"] = meta.get("date", None)
+                result["image"] = meta.get("image", None)
+                result["pagetype"] = meta.get("pagetype", None)
+                result["hostname"] = meta.get("hostname", None)
+                result["sitename"] = meta.get("sitename", None)
+                md = extract(downloaded, output_format="markdown", with_metadata=False)
+                if md:
+                    result["parsed"] = True
+                    result["markdown"] = md
+        except Exception:
+            print("failed to extract metadata")
+    return result
+
+
+if __name__ == "__main__":
+    ds = load_dataset("nixiesearch/hackernews-comments", split="train", num_proc=16)
+    ds = ds.filter(is_target, num_proc=32, batched=True, desc="selecting stories")
+    ds = ds.select_columns(["id", "url"]).shuffle()
+    schema = Features(
+        {
+            "id": Value("int64"),
+            "url": Value("string"),
+            "title": Value("string"),
+            "author": Value("string"),
+            "markdown": Value("string"),
+            "downloaded": Value("bool"),
+            "meta_extracted": Value("bool"),
+            "parsed": Value("bool"),
+            "description": Value("string"),
+            "filedate": Value("string"),
+            "date": Value("string"),
+            "image": Value("string"),
+            "pagetype": Value("string"),
+            "hostname": Value("string"),
+            "sitename": Value("string"),
+            "categories": Sequence(Value("string")),
+            "tags": Sequence(Value("string")),
+        }
+    )
+    ds = ds.map(fetch_one, num_proc=128, desc="downloading", features=schema)
+    ds.save_to_disk("/tmp/hnstories")