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Just use mtime and keep it fresh on every train

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shamoon 2025-02-02 23:56:14 -08:00
parent a8d46196d1
commit b735a0841c
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2 changed files with 6 additions and 22 deletions
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5
src/documents/classifier.py
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@ -1,6 +1,8 @@
import logging import logging
import os
import pickle import pickle
import re import re
import time
import warnings import warnings
from collections.abc import Iterator from collections.abc import Iterator
from hashlib import sha256 from hashlib import sha256
@ -229,6 +231,9 @@ class DocumentClassifier:
and self.last_doc_change_time >= latest_doc_change and self.last_doc_change_time >= latest_doc_change
) and self.last_auto_type_hash == hasher.digest(): ) and self.last_auto_type_hash == hasher.digest():
logger.info("No updates since last training") logger.info("No updates since last training")
# Update the modification time of the file to mark it as fresh
new_mtime = time.time()
os.utime(settings.MODEL_FILE, (new_mtime, new_mtime))
# Set the classifier information into the cache # Set the classifier information into the cache
# Caching for 50 minutes, so slightly less than the normal retrain time # Caching for 50 minutes, so slightly less than the normal retrain time
cache.set( cache.set(

23
src/documents/views.py
View File

@ -15,7 +15,6 @@ from urllib.parse import quote
from urllib.parse import urlparse from urllib.parse import urlparse
import pathvalidate import pathvalidate
from django.apps import apps
from django.conf import settings from django.conf import settings
from django.contrib.auth.models import Group from django.contrib.auth.models import Group
from django.contrib.auth.models import User from django.contrib.auth.models import User
@ -2174,33 +2173,13 @@ class SystemStatusView(PassUserMixin):
classifier_status = "WARNING" classifier_status = "WARNING"
raise FileNotFoundError(classifier_error) raise FileNotFoundError(classifier_error)
classifier_status = "OK" classifier_status = "OK"
task_result_model = apps.get_model("django_celery_results", "taskresult") classifier_last_trained = (
result = (
task_result_model.objects.filter(
task_name="documents.tasks.train_classifier",
status="SUCCESS",
)
.order_by(
"-date_done",
)
.first()
)
classifier_last_auto_trained = result.date_done if result else None
classifier_last_modified = (
make_aware( make_aware(
datetime.fromtimestamp(settings.MODEL_FILE.stat().st_mtime), datetime.fromtimestamp(settings.MODEL_FILE.stat().st_mtime),
) )
if settings.MODEL_FILE.exists() if settings.MODEL_FILE.exists()
else None else None
) )
classifier_last_trained = (
max(
classifier_last_auto_trained,
classifier_last_modified,
)
if classifier_last_auto_trained and classifier_last_modified
else classifier_last_auto_trained or classifier_last_modified
)
except Exception as e: except Exception as e:
if classifier_status is None: if classifier_status is None:
classifier_status = "ERROR" classifier_status = "ERROR"