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data_processor.py aktualisiert

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Floke
2025-07-18 13:34:26 +00:00
parent c5d30bfa6c
commit 1e3c873f90
1 changed files with 39 additions and 56 deletions
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95
data_processor.py
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@@ -1984,28 +1984,24 @@ class DataProcessor:
self.logger.info(
f"Wikipedia-Verifizierungs-Batch abgeschlossen. {processed_count} Zeilen verarbeitet (in Batch aufgenommen), {skipped_count} Zeilen uebersprungen ({skipped_no_wiki_url} wegen fehlender M-URL).") # <<< GEÄNDERT
def _scrape_raw_text_task(self, task_info, get_website_raw_func):
def _scrape_raw_text_task(self, task_info, scrape_function):
"""
Scrapt den Rohtext einer Website in einem separaten Thread.
Worker-Funktion für das parallele Scrapen.
Passt sich an, um sowohl Rohtext als auch Meta-Details zu liefern.
"""
logger = logging.getLogger(__name__ + ".scrape_worker")
row_num, url = task_info['row_num'], task_info['url']
raw_text, error = "k.A.", None
url = task_info.get('url')
row_num = task_info.get('row_num')
self.logger.debug(f" -> Scrape Task gestartet für Zeile {row_num}: {url}")
try:
raw_text = get_website_raw_func(url)
if isinstance(raw_text, str) and (raw_text.startswith(
"k.A. (Fehler") or raw_text.startswith("FEHLER:")):
error = f"Scraping Fehler: {raw_text[:100]}..."
elif not isinstance(raw_text, str) or not raw_text.strip():
error = "Scraping Task Fehler: Funktion gab keinen gueltigen String zurueck."
raw_text = "k.A. (Extraktion fehlgeschlagen)"
# Wir rufen jetzt zwei Helper-Funktionen auf
raw_text = get_website_raw(url)
meta_details = scrape_website_details(url)
return {'row_num': row_num, 'raw_text': raw_text, 'meta_details': meta_details, 'error': None}
except Exception as e:
error = f"Unerwarteter Fehler im Scraping Task Zeile {row_num}: {e}"
logger.error(error)
raw_text = "k.A. (Unerwarteter Fehler Task)"
return {"row_num": row_num, "raw_text": raw_text, "error": error}
self.logger.error(f"Fehler im Scraping Worker für Zeile {row_num}: {e}")
return {'row_num': row_num, 'raw_text': f'k.A. (Fehler: {e})', 'meta_details': 'k.A.', 'error': True}
def process_website_scraping_batch(
def process_website_scraping(
self,
start_sheet_row=None,
end_sheet_row=None,
@@ -2101,7 +2097,7 @@ class DataProcessor:
"Website Rohtext", "CRM Website", "Version", "Website Scrape Timestamp", "CRM Name"
]
# Erstellen Sie ein Dictionary mit Schluesseln und Indizes
col_indices = {key: COLUMN_MAP.get(key) for key in required_keys}
col_indices = {key: COLUMN_MAP.get(key, {}).get('index') for key in required_keys}
# Pruefen Sie, ob alle benoetigten Schluessel in COLUMN_MAP gefunden
# wurden
@@ -2112,11 +2108,11 @@ class DataProcessor:
return # Beende die Methode bei kritischem Fehler
# Ermitteln Sie die Indizes und Buchstaben fuer Updates (AR, AT, AP)
rohtext_col_idx = col_indices["Website Rohtext"]
website_col_idx = col_indices["CRM Website"]
version_col_idx = col_indices["Version"]
timestamp_col_idx = col_indices["Website Scrape Timestamp"]
name_col_idx = col_indices["CRM Name"]
rohtext_col_idx = col_indices.get("Website Rohtext")
website_col_idx = col_indices.get("CRM Website")
version_col_idx = col_indices.get("Version")
timestamp_col_idx = col_indices.get("Website Scrape Timestamp")
name_col_idx = col_indices.get("CRM Name")
rohtext_col_letter = self.sheet_handler._get_col_letter(
rohtext_col_idx + 1) # Block 14 _get_col_letter
@@ -2312,31 +2308,20 @@ class DataProcessor:
# Sammle Sheet Updates (AR, AT, AP) fuer diesen Batch.
# Dies geschieht jetzt nach der parallelen Verarbeitung.
if scraping_results:
# Aktueller Zeitstempel und Version
current_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
current_version = getattr(
Config, 'VERSION', 'unknown') # Block 1 Config Attribut
batch_sheet_updates = [] # Updates fuer DIESEN spezifischen Batch von Zeilen
# Iteriere ueber die Zeilennummern im Batch, fuer die Ergebnisse vorliegen.
# Ergebnisse koennen Fehlerwerte enthalten.
for row_num, raw_text_res in scraping_results.items():
# Fuege Updates fuer AR, AT und AP hinzu (nutzt interne Helfer)
# AR: Roh extrahierter Text (kann auch Fehlerwert sein)
batch_sheet_updates.append({'range': f'{rohtext_col_letter}{row_num}', 'values': [
[raw_text_res]]}) # Block 1 Column Map
# AT: Timestamp des Scraping-Versuchs (immer setzen,
# wenn versucht wurde)
batch_sheet_updates.append({'range': f'{timestamp_col_letter}{row_num}', 'values': [
[current_timestamp]]}) # Block 1 Column Map
# AP: Version des Skripts
batch_sheet_updates.append({'range': f'{version_col_letter}{row_num}', 'values': [
[current_version]]}) # Block 1 Column Map
# Sammle diese Batch-Updates fuer das groessere Batch-Update am Ende oder bei Limit.
# update_batch_row_limit wird aus Config geholt (Block 1).
all_sheet_updates.extend(batch_sheet_updates)
if scraping_results:
current_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
current_version = getattr(Config, 'VERSION', 'unknown')
batch_sheet_updates = []
# Iteriere über die Ergebnisse des finalen Batches
for row_num, result_dict in scraping_results.items():
# Füge Updates für Rohtext, Meta-Details, Timestamp und Version hinzu
# Wir verwenden hier die zuvor ermittelten col_indices
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Rohtext"] + 1)}{row_num}', 'values': [[result_dict.get('raw_text', 'k.A.')]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Meta-Details"] + 1)}{row_num}', 'values': [[result_dict.get('meta_details', 'k.A.')]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Scrape Timestamp"] + 1)}{row_num}', 'values': [[current_timestamp]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Version"] + 1)}{row_num}', 'values': [[current_version]]})
all_sheet_updates.extend(batch_sheet_updates)
# Leere den Scraping-Batch fuer die naechste Iteration
tasks_for_processing_batch = []
@@ -2424,14 +2409,12 @@ class DataProcessor:
batch_sheet_updates = [] # Updates fuer diesen spezifischen Batch
# Iteriere ueber die Zeilennummern im Batch, fuer die
# Ergebnisse vorliegen.
for row_num, raw_text_res in scraping_results.items():
# Fuege Updates fuer AR, AT und AP hinzu
batch_sheet_updates.append({'range': f'{rohtext_col_letter}{row_num}', 'values': [
[raw_text_res]]}) # Block 1 Column Map
batch_sheet_updates.append({'range': f'{timestamp_col_letter}{row_num}', 'values': [
[current_timestamp]]}) # Block 1 Column Map
batch_sheet_updates.append({'range': f'{version_col_letter}{row_num}', 'values': [
[current_version]]}) # Block 1 Column Map
for row_num, result_dict in scraping_results.items():
# Füge Updates für Rohtext, Meta-Details, Timestamp und Version hinzu
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Rohtext"] + 1)}{row_num}', 'values': [[result_dict['raw_text']]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Meta-Details"] + 1)}{row_num}', 'values': [[result_dict['meta_details']]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Website Scrape Timestamp"] + 1)}{row_num}', 'values': [[current_timestamp]]})
batch_sheet_updates.append({'range': f'{self.sheet_handler._get_col_letter(col_indices["Version"] + 1)}{row_num}', 'values': [[current_version]]}) # Block 1 Column Map
# Fuege diese Updates zur globalen Liste hinzu (wird dann nur
# noch einmal gesendet)
all_sheet_updates.extend(batch_sheet_updates)