Add better profanity filtering and filter out empty notifications #74

sourcery-ai-experiments-bot · 2024-05-01T01:12:41Z

No description provided.

sourcery-ai-experiments-bot · 2024-05-01T01:12:43Z

This is a benchmark review for experiment review_of_reviews_20240501.
Run ID: review_of_reviews_20240501/benchmark_2024-05-01T00-17-45_v1-16-0-221-g235b41e01.

This pull request was cloned from https://github.com/jquagga/ttt/pull/98. (Note: the URL is not a link to avoid triggering a notification on the original pull request.)

Experiment configuration

review_config:
  # User configuration for the review
  # - benchmark - use the user config from the benchmark reviews
  # - <value> - use the value directly
  user_config:
    enable_ai_review: true
    enable_rule_comments: false

    enable_complexity_comments: benchmark
    enable_docstring_comments: benchmark
    enable_security_comments: benchmark
    enable_tests_comments: benchmark
    enable_comment_suggestions: benchmark
    enable_functionality_review: benchmark

    enable_approvals: true

  ai_review_config:
    # The model responses to use for the experiment
    # - benchmark - use the model responses from the benchmark reviews
    # - llm - call the language model to generate responses
    model_responses:
      comments_model: benchmark
      comment_validation_model: benchmark
      comment_suggestion_model: benchmark
      complexity_model: benchmark
      docstrings_model: benchmark
      functionality_model: benchmark
      security_model: benchmark
      tests_model: benchmark

# The pull request dataset to run the experiment on
pull_request_dataset:
- https://github.com/strawberry-graphql/strawberry/pull/3478
- https://github.com/agatma/sprint1-http-server/pull/3
- https://github.com/strawberry-graphql/strawberry-django/pull/525
- https://github.com/agatma/sprint1-http-server/pull/4
- https://github.com/strawberry-graphql/strawberry/pull/3480
- https://github.com/Xmaster6y/mulsi/pull/21
- https://github.com/Xmaster6y/mulsi/pull/22
- https://github.com/LLotme/vscode/pull/4
- https://github.com/ignition-api/8.1/pull/271
- https://github.com/ignition-api/8.1/pull/272
- https://github.com/manoelhc/test-actions/pull/33
- https://github.com/tqt97/BlogForJob/pull/3
- https://github.com/tqt97/BlogForJob/pull/2
- https://github.com/tqt97/BlogForJob/pull/4
- https://github.com/ser3n7ty/Nvim/pull/1
- https://github.com/ser3n7ty/Nvim/pull/2
- https://github.com/ser3n7ty/Nvim/pull/3
- https://github.com/okisdev/ChatChat/pull/325
- https://github.com/DevCycleHQ/ios-client-sdk/pull/197
- https://github.com/DevCycleHQ/test-harness/pull/424
- https://github.com/DevCycleHQ/flutter-client-sdk/pull/150
- https://github.com/DevCycleHQ/cli/pull/382
- https://github.com/DevCycleHQ/devcycle-docs/pull/642
- https://github.com/uncscode/particula/pull/449
- https://github.com/nikhilbadyal/docker-py-revanced/pull/518
- https://github.com/ignition-incendium/incendium/pull/199
- https://github.com/rtk-rnjn/algorithms/pull/81
- https://github.com/rtk-rnjn/algorithms/pull/82
- https://github.com/rtk-rnjn/algorithms/pull/83
- https://github.com/rtk-rnjn/algorithms/pull/84
- https://github.com/suttacentral/suttacentral/pull/3151
- https://github.com/EduardSchwarzkopf/pecuny/pull/131
- https://github.com/gdsfactory/cspdk/pull/27
- https://github.com/gdsfactory/cspdk/pull/28
- https://github.com/gdsfactory/cspdk/pull/29
- https://github.com/gdsfactory/ubc/pull/376
- https://github.com/gdsfactory/ubc/pull/377
- https://github.com/gdsfactory/gdsfactory/pull/2709
- https://github.com/gdsfactory/ubc/pull/378
- https://github.com/W-zrd/unishare_mobile/pull/13
- https://github.com/Nuitka/Nuitka/pull/2830
- https://github.com/jquagga/ttt/pull/98
- https://github.com/jquagga/ttt/pull/101
- https://github.com/Idrinth/api-bench/pull/939
- https://github.com/letsdoitnowus/planium-backend/pull/41
- https://github.com/albumentations-team/albumentations.ai/pull/74
- https://github.com/albumentations-team/albumentations/pull/1695
- https://github.com/yashasvi-jain/ucron-models/pull/11
- https://github.com/nbhirud/system_update/pull/24
- https://github.com/supabase-community/postgrest-py/pull/422
review_comment_labels:
- label: correct
  question: Is this comment correct?
- label: helpful
  question: Is this comment helpful?
- label: comment-type
  question: Is the comment type correct?
- label: comment-area
  question: Is the comment area correct?
- label: llm-test
  question: |
    What type of LLM test could this comment become?
    - 👍 - this comment is really good/important and we should always make it
    - 👎 - this comment is really bad and we should never make it
    - no reaction - don't turn this comment into an LLM test

# Benchmark reviews generated by running
#   python -m scripts.experiment benchmark <experiment_name>
benchmark_reviews: []

SourceryAI

Hey @sourcery-ai-experiments-bot - I've reviewed your changes and they look great!

Here's what I looked at during the review

🟡 General issues: 3 issues found
🟢 Security: all looks good
🟡 Testing: 2 issues found
🟢 Complexity: all looks good

LangSmith trace

_{Help me be more useful! Please click 👍 or 👎 on each comment to tell me if it was helpful.}

SourceryAI · 2024-05-01T01:14:15Z

ttt.py

@@ -76,6 +76,7 @@
        torch_dtype=torch_dtype,
        device=device,
    )
+profanity.load_censor_words()


suggestion (code_refinement): Consider initializing profanity filter setup in a dedicated initialization function.

Placing the initialization of the profanity filter directly in the global scope of the script might lead to issues with maintainability and testing. It's generally a good practice to encapsulate setup logic in a function.

Suggested change

profanity.load_censor_words()

def initialize_profanity_filter():

profanity.load_censor_words()

if __name__ == "__main__":

initialize_profanity_filter()

Is this comment correct?

Is this comment helpful?

Is the comment type correct?

Is the comment area correct?

What type of LLM test could this comment become?

👍 - this comment is really good/important and we should always make it

👎 - this comment is really bad and we should never make it

no reaction - don't turn this comment into an LLM test

SourceryAI · 2024-05-01T01:14:15Z

ttt.py

-    scrubber.remove_detector("email")
-    body = scrubber.clean(calljson["text"])
+    # Run ai text through profanity filter
+    body = profanity.censor(calljson["text"])


suggestion (testing): Missing test for the new profanity filter functionality.

Ensure there is a test that verifies the profanity filter correctly censors inappropriate language and handles edge cases, such as mixed case, special characters, and multiple languages if applicable.

Is this comment correct?

Is this comment helpful?

Is the comment type correct?

Is the comment area correct?

What type of LLM test could this comment become?

👍 - this comment is really good/important and we should always make it

👎 - this comment is really bad and we should never make it

no reaction - don't turn this comment into an LLM test

SourceryAI · 2024-05-01T01:14:15Z

ttt.py

+            if calljson["text"] != "you":
+                send_notifications(calljson, audiofile, destinations)


suggestion (testing): Missing test for the new condition to filter out empty notifications.

It's important to add a test to verify that notifications are not sent when the transcript is just 'you'. This test should cover scenarios where the transcript is exactly 'you', contains 'you' with additional text, and completely different text to ensure the condition works as expected.

Is this comment correct?

Is this comment helpful?

Is the comment type correct?

Is the comment area correct?

What type of LLM test could this comment become?

👍 - this comment is really good/important and we should always make it

👎 - this comment is really bad and we should never make it

no reaction - don't turn this comment into an LLM test

Add better profanity filtering and filter out empty notifications

3d84ff8

SourceryAI approved these changes May 1, 2024

View reviewed changes

-profanity.load_censor_words()
+def initialize_profanity_filter():
+    profanity.load_censor_words()
+if __name__ == "__main__":
+    initialize_profanity_filter()

		if calljson["text"] != "you":
		send_notifications(calljson, audiofile, destinations)

Add better profanity filtering and filter out empty notifications #74

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Add better profanity filtering and filter out empty notifications #74

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sourcery-ai-experiments-bot commented May 1, 2024

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