Trying to appease linter

dalm/datasets/reading_comprehension_generation/utils.py

@@ -172,7 +172,7 @@ def extract_question_or_answer(text: str, extract_type: str = "question") -> Tup
     extraction_regex = rf".*\[?{extract_type}[:\]]*(?:.*?\])?\s*(.*)"
 
     match = re.match(extraction_regex, text, re.IGNORECASE)
-    extracted_text = match.group(1) if match else None
+    extracted_text = match.group(1) if match else ""
     found_extracted = True if extracted_text else False
     return found_extracted, extracted_text
 

tests/datasets/reading_comprehension_generation/test_utils.py

@@ -1,7 +1,8 @@
 from dalm.datasets.reading_comprehension_generation.utils import _raw_question_and_answer_extractor, question_and_answer_extractor
-import pdb
+from typing import Dict, Iterator, List, Optional, Tuple
 
-def test_question_and_answer_extractor():
+
+def test_question_and_answer_extractor() -> None:
     chat_completions = question_and_answer_extractor(
         whole_text=""" 
 1. QUESTION: Can you summarize the purpose of population imaging studies and how they contribute to preventing or treating disease?
@@ -22,6 +23,8 @@ def test_question_and_answer_extractor():
     )
     print(chat_completions) 
 
+    assert chat_completions is not None
+
     # The first chat completion item should be a user prompt, and it should start with "Based on the following text:"
     assert chat_completions[0]["content"].startswith("Based on the following text:")
     assert chat_completions[0]["role"] == "user"
@@ -46,9 +49,9 @@ def test_question_and_answer_extractor():
 
 
 
-def test_raw_question_and_answer_extractor():
+def test_raw_question_and_answer_extractor() -> None:
 
-    inputs = [
+    test_cases = [
         {
             "whole_text": """
                             QUESTION: What is the focus?
@@ -182,14 +185,28 @@ def test_raw_question_and_answer_extractor():
 
     ]
 
-    for input in inputs:
-        result_qa_pairs = _raw_question_and_answer_extractor(whole_text=input["whole_text"])
-        expected_qa_pairs = input["expected_output"]
-        for result, expected in zip(result_qa_pairs, expected_qa_pairs):
-            result_question = result["question"].strip().lower()
-            expected_question = expected["question"].strip().lower()
-            result_answer = result["answer"].strip().lower()
-            expected_answer = expected["answer"].strip().lower()
+    for test_case in test_cases:
+
+        result_qa_pairs = _raw_question_and_answer_extractor(
+            whole_text=str(test_case["whole_text"])
+        )
+
+        expected_qa_pairs = test_case["expected_output"]
+
+        assert result_qa_pairs is not None
+        assert expected_qa_pairs is not None
+
+        assert len(result_qa_pairs) == len(expected_qa_pairs)
+
+        for i, result_qa_pair in enumerate(result_qa_pairs):
+            expected_qa_pair = expected_qa_pairs[i]
+            assert result_qa_pair is not None
+            assert expected_qa_pair is not None
+
+            result_question = result_qa_pair["question"].strip().lower()
+            expected_question = expected_qa_pair["question"].strip().lower()
+            result_answer = result_qa_pair["answer"].strip().lower()
+            expected_answer = expected_qa_pair["answer"].strip().lower()
             assert result_question == expected_question, f"result_question: {result_question} != expected_question: {expected_question}"
             assert result_answer == expected_answer, f"result_answer: {result_answer} != expected_answer: {expected_answer}"