add max questions and passing grade functionality

app.py

@@ -1,5 +1,6 @@
 import os
 from datetime import datetime
+import random
 
 import gradio as gr
 from datasets import load_dataset, Dataset
@@ -7,11 +8,18 @@ from huggingface_hub import whoami
 
 
 EXAM_DATASET_ID = os.getenv("EXAM_DATASET_ID") or "agents-course/unit_1_quiz"
+EXAM_MAX_QUESTIONS = os.getenv("EXAM_MAX_QUESTIONS") or 10
+EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.7
 
 ds = load_dataset(EXAM_DATASET_ID, split="train")
 
-# Convert dataset to a list of dicts so we can iterate similarly to quiz_data
-quiz_data = ds.to_pandas().to_dict("records")  # or use a for-loop if you prefer
+# Convert dataset to a list of dicts and randomly sort
+quiz_data = ds.to_pandas().to_dict("records")
+random.shuffle(quiz_data)
+
+# Limit to max questions if specified
+if EXAM_MAX_QUESTIONS:
+    quiz_data = quiz_data[: int(EXAM_MAX_QUESTIONS)]
 
 
 def on_user_logged_in(token: gr.OAuthToken | None):
@@ -29,25 +37,39 @@ def on_user_logged_in(token: gr.OAuthToken | None):
 def push_results_to_hub(user_answers, token: gr.OAuthToken | None):
     """
     Create a new dataset from user_answers and push it to the Hub.
-    We use the user's HF token to determine the correct repo.
-    If no one is logged in, we'll return an error message.
+    Calculates grade and checks against passing threshold.
     """
     if token is None:
         gr.Warning("Please log in to Hugging Face before pushing!")
         return
-    else:
-        gr.Info("Submitting answers to the Hub. Please wait...", duration=2)
+
+    # Calculate grade
+    correct_count = sum(1 for answer in user_answers if answer["is_correct"])
+    total_questions = len(user_answers)
+    grade = correct_count / total_questions if total_questions > 0 else 0
+
+    if grade < float(EXAM_PASSING_SCORE):
+        gr.Warning(
+            f"Score {grade:.1%} below passing threshold of {float(EXAM_PASSING_SCORE):.1%}"
+        )
+        return f"You scored {grade:.1%}. Please try again to achieve at least {float(EXAM_PASSING_SCORE):.1%}"
+
+    gr.Info("Submitting answers to the Hub. Please wait...", duration=2)
 
     user_info = whoami(token=token.token)
-    repo_id = f"{EXAM_DATASET_ID}_student_responses"  # e.g. 'myUsername/quiz-responses'
+    repo_id = f"{EXAM_DATASET_ID}_student_responses"
     submission_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
     new_ds = Dataset.from_list(user_answers)
     new_ds = new_ds.map(
-        lambda x: {"username": user_info["name"], "datetime": submission_time}
+        lambda x: {
+            "username": user_info["name"],
+            "datetime": submission_time,
+            "grade": grade,
+        }
     )
     new_ds.push_to_hub(repo_id)
-    gr.Success("Your responses have been submitted to the Hub!")
+    return f"Your responses have been submitted to the Hub! Final grade: {grade:.1%}"
 
 
 def handle_quiz(question_idx, user_answers, selected_answer, is_start):
@@ -83,15 +105,22 @@ def handle_quiz(question_idx, user_answers, selected_answer, is_start):
 
     # If we've reached the end, show final results
     if question_idx >= len(quiz_data):
-        final_text = f"**All questions answered!**\n\nHere are your selections:\n\n{user_answers}"
+        correct_count = sum(1 for answer in user_answers if answer["is_correct"])
+        grade = correct_count / len(user_answers)
+        results_text = (
+            f"**Quiz Complete!**\n\n"
+            f"Your score: {grade:.1%}\n"
+            f"Passing score: {float(EXAM_PASSING_SCORE):.1%}\n\n"
+            f"Your answers:\n\n{user_answers}"
+        )
         return (
             "",  # question_text becomes blank
             gr.update(choices=[], visible=False),
-            "",  # status_text (can clear or reuse)
+            f"{'✅ Passed!' if grade >= float(EXAM_PASSING_SCORE) else '❌ Did not pass'}",
             question_idx,
             user_answers,
             start_btn_update,
-            gr.update(value=final_text, visible=True),  # show final_markdown
+            gr.update(value=results_text, visible=True),  # show final_markdown
         )
     else:
         # Otherwise, show the next question