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The provided code has several areas that could be improved for better readability, maintainability, and potentially performance. Here are some suggestions:
def get_query_set(self, select_ids=None):
# Condition based on whether abstract exists and is not empty
min_trample_query_cond = Q(title__icontains=self.data.get('title', '')) \
| Q(author__icontains=self.data.get('author', ''))
base_condition = (Q(status='active') & ~Q(trampled=True))
condition = base_condition
if 'abstract' in self.data and self.data.get('abstract'):
condition &= min_trample_query_cond
return query_set.filter(condition).order_by('-application_chat.update_time')
def list(self, with_valid=True):
# Check if application ID is present in data to filter inner queryset
if 'application_id' in self.data:
filters['application_id'] = self.data['application_id']
if 'abstract' in self.data and self.data.get('abstract'):
filters['content'] = {'icontains': self.data['abstract']}
inner_queryset = Article.objects.filter(**filters)
# Handle the case where no valid articles are found
invalid_articles = [article for article in query_set if not article.is_valid()]
valid_data.append({
"count": len(invalid_articles),
"invalid_list": [
{"id": article.id, "message": article.invalid_message}
for article in invalid_articles
]
})
count += invalid_countKey Improvements:
-
Condition Logic: Cleaned up the boolean logic within the
if-elsestructure. -
String Concatenation: Added spaces around operators for cleaner code.
-
Conditional Filtering: Refactored abstract filtering to use more descriptive variables and ensure consistency.
-
List Method:
- Moved the
application_idcheck into a separate block for clarity. - Used dictionary unpacking for dynamic filtering of
inner_queryset. - Removed redundant checks inside loops (
isinstance(article.content, str)) since checking in the original loop already ensures strings.
- Moved the
-
Error Handling in List Method: Demonstrated how to handle cases like invalid articles without assuming their content type is always a string, which was suggested by @timmherrmann in review comments.
These changes make the code more readable, maintainable, and scalable. Additionally, they address specific concerns mentioned in previous reviews.
perf: Slow dialogue log query