Modes e2e tests

tests/test_e2e.py

@@ -1,32 +1,40 @@
 from tests.candidate import complete_interview
 from tests.grader import grade
 from concurrent.futures import ThreadPoolExecutor
-
+import random
 from typing import List
 
 
-def complete_and_grade_interview(interview_type: str) -> float:
+def complete_and_grade_interview(interview_type: str, mode: str = "normal", min_score=0.4) -> float:
     """
     Complete an interview and return the overall score.
 
     :param interview_type: Type of the interview.
+    :param mode: Mode of the interview ("normal", "empty", "gibberish", "repeat").
     :return: Overall score of the interview.
     """
-    file_path, _ = complete_interview(interview_type, "test", model="gpt-3.5-turbo")
-    feedback = grade(file_path, model="gpt-4o")
-    assert feedback["overall_score"] > 0.4
+    file_path, _ = complete_interview(interview_type, "test", model="gpt-3.5-turbo", mode=mode)
+    feedback = grade(file_path, model="gpt-4-turbo")
+    assert feedback["overall_score"] > min_score
     return feedback["overall_score"]
 
 
 def test_complete_interview() -> None:
     """
-    Test the complete interview process for various interview types.
+    Test the complete interview process for various interview types, including edge cases.
     """
     interview_types = ["ml_design", "math", "ml_theory", "system_design", "sql", "coding"]
     scores: List[float] = []
 
     with ThreadPoolExecutor(max_workers=3) as executor:
+        # Test normal interviews
         futures = [executor.submit(complete_and_grade_interview, it) for it in interview_types]
+
+        # Test edge cases: empty, gibberish, repeat for one random interview type each
+        futures.append(executor.submit(complete_and_grade_interview, random.choice(interview_types), mode="empty", min_score=0.0))
+        futures.append(executor.submit(complete_and_grade_interview, random.choice(interview_types), mode="gibberish", min_score=0.0))
+        futures.append(executor.submit(complete_and_grade_interview, random.choice(interview_types), mode="repeat", min_score=0.0))
+
         for future in futures:
             score = future.result()
             scores.append(score)