Update src/core/QuizEngine.py

added short answer-type questions

src/core/QuizEngine.py

@@ -1,20 +1,19 @@
 import random
+import os
+import logging
 from core.AcronymManager import AcronymManager
 
 class QuizEngine:
-    def __init__(self):
-        # reuse the existing manager to load the JSON
+    def __init__(self, source_dir="source_documents"):
         self.acronym_mgr = AcronymManager()
+        self.source_dir = source_dir
+        self.logger = logging.getLogger(__name__)
 
+    # --- MODE 1: ACRONYMS ---
     def get_random_acronym(self):
-        """
-        Fetches a random acronym-definition pair.
-        Returns: dict or None (if empty)
-        """
         if not self.acronym_mgr.acronyms:
             return None
         
-        # Pick a random key
         acronym = random.choice(list(self.acronym_mgr.acronyms.keys()))
         definition = self.acronym_mgr.acronyms[acronym]
         
@@ -25,21 +24,82 @@ class QuizEngine:
             "question": f"What does **{acronym}** stand for?"
         }
 
-    def construct_grading_prompt(self, term, correct_definition, user_answer):
+    # --- MODE 2: DOCUMENTS (NEW) ---
+    def get_document_context(self, username):
         """
-        Builds the prompt for the Board Examiner persona.
-        We return the string here so app.py can send it to whichever LLM is active.
+        Picks a random file, reads it, and selects a random segment.
+        Returns the segment to be used as the 'Answer Key'.
         """
+        user_dir = os.path.join(self.source_dir, username)
+        if not os.path.exists(user_dir):
+            return None
+
+        # 1. Get list of text/md files
+        files = [f for f in os.listdir(user_dir) if f.lower().endswith(('.txt', '.md'))]
+        if not files:
+            return None
+
+        # 2. Pick a random file
+        selected_file = random.choice(files)
+        file_path = os.path.join(user_dir, selected_file)
+
+        try:
+            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
+                text = f.read()
+
+            # 3. Split into rough paragraphs (double line break)
+            # Filter out short/empty paragraphs to avoid bad questions
+            paragraphs = [p.strip() for p in text.split('\n\n') if len(p.strip()) > 200]
+            
+            if not paragraphs:
+                return None
+
+            # 4. Pick a random paragraph
+            selected_context = random.choice(paragraphs)
+            
+            return {
+                "type": "document",
+                "source_file": selected_file,
+                "context_text": selected_context
+            }
+
+        except Exception as e:
+            self.logger.error(f"Error fetching quiz context: {e}")
+            return None
+
+    # --- PROMPT CONSTRUCTORS ---
+    def construct_question_generation_prompt(self, context_text):
+        """Asks the LLM to write the question for us."""
         return (
-            f"You are a strict US Navy Engineering Duty Officer Board Examiner.\n"
-            f"I am a candidate. You asked me to define the acronym: {term}\n\n"
-            f"The Official Definition is: {correct_definition}\n"
-            f"My Answer was: {user_answer}\n\n"
-            f"INSTRUCTIONS:\n"
-            f"1. Grade my answer as PASS (Correct expansion) or FAIL (Incorrect expansion).\n"
-            f"2. If I got the words right but missed the full context, give me a 'PASS with Comments'.\n"
-            f"3. Keep your feedback short and military-professional.\n\n"
+            f"Act as a US Navy Engineering Duty Officer Board Examiner.\n"
+            f"Read the following technical excerpt from a Navy reference:\n\n"
+            f"'{context_text}'\n\n"
+            f"TASK: Generate a single, tough, short-answer board question based ONLY on this text.\n"
+            f"The question should test recall of specific details (e.g., 'What are the 3 documents required for...').\n"
+            f"OUTPUT: Just the question text. No preamble."
+        )
+
+    def construct_grading_prompt(self, question, answer, context_text):
+        """Grades the deep dive answer."""
+        return (
+            f"You are a Board Examiner.\n"
+            f"Reference Material: '{context_text}'\n\n"
+            f"Question: {question}\n"
+            f"Candidate Answer: {answer}\n\n"
+            f"TASK: Grade the answer based strictly on the Reference Material.\n"
+            f"1. If the answer is factually correct according to the text, grade PASS.\n"
+            f"2. If it misses key details mentioned in the text, grade FAIL or PASS with Comments.\n"
             f"OUTPUT FORMAT:\n"
             f"**GRADE:** [PASS/FAIL]\n"
-            f"**CRITIQUE:** [Your brief feedback]"
+            f"**FEEDBACK:** [Brief correction or confirmation]"
+        )
+    
+    def construct_acronym_grading_prompt(self, term, correct_definition, user_answer):
+        """Grades the acronym answer."""
+        return (
+            f"Term: {term}\n"
+            f"Official Definition: {correct_definition}\n"
+            f"User Answer: {user_answer}\n\n"
+            f"Grade as PASS (correct expansion) or FAIL. If close, PASS with comment.\n"
+            f"Output: **GRADE:** [Status]\n**FEEDBACK:** [Details]"
         )