vault backup: 2025-12-23 10:55:37

content/.obsidian/workspace.json

@@ -30,7 +30,7 @@
               "state": {
                 "file": "Anatomi & Histologi 2/Statistik.md",
                 "mode": "source",
-                "source": true,
+                "source": false,
                 "backlinks": false
               },
               "icon": "lucide-file",

content/Anatomi & Histologi 2/Statistik.md

@@ -1,11 +1,11 @@
 ### Tentor
 
-| Datum      | OCR  | Split  | Bilder | Hotspot | Taggad  | Svar | Granska |
+| Datum      | OCR | Split | Bilder | Hotspot | Taggad | Svar | Granska |
-| ---------- | :-:  | :---:  | :----: | :-----: | :----:  | :--: | ------- |
+| ---------- | :-: | :---: | :----: | :-----: | :----: | :--: | ------- |
-| 2022-01-15 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅  |         |
+| 2022-01-15 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅   |         |
-| 2022-06-01 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅  |         |
+| 2022-06-01 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅   |         |
-| 2023-01-11 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅  |         |
+| 2023-01-11 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅   |         |
-| 2023-05-31 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅  |         |
+| 2023-05-31 |  ✅  |   ✅   |   ✅    |         |   ✅    |  ✅   |         |
 | 2024-01-10 |  ✅  |   ✅   |   ✅    |         |   ✅    |      |         |
 | 2024-05-29 |  ✅  |   ✅   |   ✅    |         |   ✅    |      |         |
 | 2025-01-15 |  ✅  |   ✅   |   ✅    |         |   ✅    |      |         |

quiz/MATCHING_FORMAT.md

@@ -1,18 +0,0 @@
-# Matching Questions Format Analysis
-
-Based on reviewing the 17 matching questions:
-
-## Key Finding:
-Only **1 question has an answer** (2023-05-31/3.md), the rest have TODO.
-
-**That question uses this format:**
-- Two separate bullet lists
-- Answer: "ItemName: MatchName" format
-
-## Proposed Implementation:
-1. Support two-list format (most flexible)
-2. Parse answer as "Item: Match" pairs
-3. Store as JSON with 0-indexed pairs
-4. Render as n×n table with radio buttons
-
-## Next: Implement based on this one working example.

quiz/parse-markdown.py

@@ -1,15 +0,0 @@
-import pathlib
-
-import mistune
-markdown = mistune.create_markdown(renderer=None)
-
-root = pathlib.Path(__file__).parent.parent
-exams = root / "content" / "Anatomi & Histologi 2" / "Gamla tentor"
-print(exams.absolute())
-for file in sorted(exams.glob("*/*.md")):
-    if len(file.stem) > 2:
-        continue
-    print(f"Parsing {file}")
-    tokens = markdown(file.read_text(encoding="utf-8"))
-    import pprint
-    pprint.pprint(tokens)

quiz/pytest.ini

@@ -13,5 +13,6 @@ markers =
     admin: Admin interface tests
     import: Import and parsing tests
     import_tests: Import and parsing tests
+    parser: Question parser tests
     slow: Slow running tests
 

quiz/quiz/tests/test_question_parser.py

@@ -0,0 +1,537 @@
+"""
+Comprehensive test suite for the question_parser module.
+
+This test suite uses pytest's parametrize decorator to test multiple scenarios
+with minimal code duplication. It covers:
+
+1. Node class:
+   - Initialization with different token types
+   - Attribute handling
+   - Children node processing
+   - String representation (__repr__)
+   - Text extraction from nested structures
+
+2. parse_question function:
+   - Metadata parsing (tags, dates, etc.)
+   - Raw content extraction
+   - Different question types (MCQ, SCQ, text field, matching)
+   - Questions with images
+   - Edge cases (empty content, missing frontmatter)
+   - Document structure preservation
+
+3. ParsedQuestion dataclass:
+   - Default values
+   - Initialization with custom values
+
+4. Real exam questions:
+   - Parsing actual exam questions from the content directory
+   - Validation of all short-named question files
+
+Test execution:
+    pytest tests/test_question_parser.py -v              # Verbose output
+    pytest tests/test_question_parser.py -k "mcq"        # Run only MCQ tests
+    pytest tests/test_question_parser.py --collect-only  # List all tests
+"""
+
+import pathlib
+import tempfile
+import pytest
+from quiz.utils.question_parser import Node, ParsedQuestion, parse_question
+
+
+@pytest.fixture
+def temp_dir():
+    """Create a temporary directory for test files"""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield pathlib.Path(tmpdir)
+
+
+@pytest.fixture
+def create_question_file(temp_dir):
+    """Factory fixture to create question files"""
+    def _create_file(filename: str, content: str) -> pathlib.Path:
+        file_path = temp_dir / filename
+        file_path.write_text(content, encoding="utf-8")
+        return file_path
+    return _create_file
+
+
+class TestNode:
+    """Test the Node class"""
+
+    @pytest.mark.parametrize("token,expected_type,expected_raw", [
+        ({"type": "paragraph"}, "paragraph", ""),
+        ({"type": "heading", "raw": "Test Heading"}, "heading", "Test Heading"),
+        ({"type": "text", "raw": "Some text"}, "text", "Some text"),
+        ({"type": "list"}, "list", ""),
+    ])
+    def test_node_initialization(self, token, expected_type, expected_raw):
+        """Test Node initialization with different token types"""
+        node = Node(token)
+        assert node.type == expected_type
+        assert node.raw == expected_raw
+
+    @pytest.mark.parametrize("token,expected_attrs", [
+        ({"type": "block_code", "attrs": {"info": "spoiler-block:"}}, {"info": "spoiler-block:"}),
+        ({"type": "paragraph"}, {}),
+        ({"type": "heading", "attrs": {"level": 2}}, {"level": 2}),
+    ])
+    def test_node_attributes(self, token, expected_attrs):
+        """Test Node attributes handling"""
+        node = Node(token)
+        assert node.attrs == expected_attrs
+
+    def test_node_children(self):
+        """Test Node children handling"""
+        token = {
+            "type": "paragraph",
+            "children": [
+                {"type": "text", "raw": "Hello "},
+                {"type": "text", "raw": "World"},
+            ]
+        }
+        node = Node(token)
+        assert len(node.children) == 2
+        assert node.children[0].type == "text"
+        assert node.children[0].raw == "Hello "
+        assert node.children[1].type == "text"
+        assert node.children[1].raw == "World"
+
+    @pytest.mark.parametrize("token,expected_repr_contains", [
+        ({"type": "text", "raw": "test"}, "Text(raw='test')"),
+        ({"type": "paragraph"}, "Paragraph()"),
+        ({"type": "block_code", "attrs": {"info": "python"}}, "BlockCode(attrs={'info': 'python'})"),
+    ])
+    def test_node_repr(self, token, expected_repr_contains):
+        """Test Node __repr__ method"""
+        node = Node(token)
+        assert repr(node) == expected_repr_contains
+
+    @pytest.mark.parametrize("token,expected_text", [
+        ({"type": "text", "raw": "Simple text"}, "Simple text"),
+        (
+            {
+                "type": "paragraph",
+                "children": [
+                    {"type": "text", "raw": "Hello "},
+                    {"type": "text", "raw": "World"},
+                ]
+            },
+            "Hello World"
+        ),
+        (
+            {
+                "type": "paragraph",
+                "children": [
+                    {"type": "text", "raw": "Nested "},
+                    {
+                        "type": "strong",
+                        "children": [{"type": "text", "raw": "bold"}]
+                    },
+                    {"type": "text", "raw": " text"},
+                ]
+            },
+            "Nested bold text"
+        ),
+    ])
+    def test_node_text_property(self, token, expected_text):
+        """Test Node text property extraction"""
+        node = Node(token)
+        assert node.text == expected_text
+
+
+class TestParseQuestion:
+    """Test the parse_question function"""
+
+    @pytest.mark.parametrize("content,expected_tags", [
+        (
+            """---
+tags: [ah2, provfråga, frågetyp/mcq]
+date: 2022-01-15
+---
+Question content""",
+            ["ah2", "provfråga", "frågetyp/mcq"]
+        ),
+        (
+            """---
+tags:
+  - ah2
+  - provfråga
+  - frågetyp/scq
+date: 2023-05-31
+---
+Question content""",
+            ["ah2", "provfråga", "frågetyp/scq"]
+        ),
+    ])
+    def test_parse_metadata_tags(self, create_question_file, content, expected_tags):
+        """Test parsing of metadata tags in different formats"""
+        file_path = create_question_file("test.md", content)
+        question = parse_question(file_path)
+        assert question.metadata["tags"] == expected_tags
+
+    @pytest.mark.parametrize("content,expected_date", [
+        (
+            """---
+tags: [ah2]
+date: 2022-01-15
+---
+Content""",
+            "2022-01-15"
+        ),
+        (
+            """---
+tags: [ah2]
+date: 2023-05-31
+---
+Content""",
+            "2023-05-31"
+        ),
+    ])
+    def test_parse_metadata_date(self, create_question_file, content, expected_date):
+        """Test parsing of metadata date"""
+        file_path = create_question_file("test.md", content)
+        question = parse_question(file_path)
+        assert str(question.metadata["date"]) == expected_date
+
+    @pytest.mark.parametrize("content,expected_raw", [
+        (
+            """---
+tags: [ah2]
+---
+Simple question""",
+            "Simple question"
+        ),
+        (
+            """---
+tags: [ah2]
+---
+Question with **bold** text""",
+            "Question with **bold** text"
+        ),
+    ])
+    def test_parse_raw_content(self, create_question_file, content, expected_raw):
+        """Test parsing of raw content"""
+        file_path = create_question_file("test.md", content)
+        question = parse_question(file_path)
+        assert question.raw_content.strip() == expected_raw
+
+    def test_parse_mcq_question(self, create_question_file):
+        """Test parsing a complete MCQ question"""
+        content = """---
+tags: [ah2, provfråga, frågetyp/mcq, cerebrum]
+date: 2022-01-15
+---
+Vilka av följande räknas till storhjärnans basala kärnor?
+
+**Välj två alternativ**
+- A: Putamen
+- B: Nucleus Ruber
+- C: Substantia nigra
+- D: Nucleus caudatus
+
+```spoiler-block:
+A och D
+```
+"""
+        file_path = create_question_file("mcq.md", content)
+        question = parse_question(file_path)
+
+        assert question.metadata["tags"] == ["ah2", "provfråga", "frågetyp/mcq", "cerebrum"]
+        assert len(question.nodes) > 0
+
+        # Find paragraph nodes
+        paragraphs = [n for n in question.nodes if n.type == "paragraph"]
+        assert len(paragraphs) > 0
+
+        # Find list nodes
+        lists = [n for n in question.nodes if n.type == "list"]
+        assert len(lists) > 0
+
+        # Find spoiler block
+        code_blocks = [n for n in question.nodes if n.type == "block_code"]
+        assert len(code_blocks) > 0
+        spoiler = code_blocks[0]
+        assert spoiler.attrs.get("info") == "spoiler-block:"
+        assert "A och D" in spoiler.raw
+
+    def test_parse_scq_question(self, create_question_file):
+        """Test parsing a single choice question"""
+        content = """---
+tags: [ah2, provfråga, frågetyp/scq, histologi]
+date: 2022-06-01
+---
+Vilken del av CNS syns i bild?
+- A: Cerebellum
+- B: Diencephalon
+- C: Medulla spinalis
+- D: Cerebrum
+- E: Pons
+
+```spoiler-block:
+A
+```
+"""
+        file_path = create_question_file("scq.md", content)
+        question = parse_question(file_path)
+
+        assert "frågetyp/scq" in question.metadata["tags"]
+        lists = [n for n in question.nodes if n.type == "list"]
+        assert len(lists) > 0
+
+    def test_parse_text_field_question(self, create_question_file):
+        """Test parsing a text field question"""
+        content = """---
+tags: [ah2, provfråga, frågetyp/textfält, öga, anatomi]
+date: 2022-01-15
+---
+![[image-2.png|301x248]]
+**Fyll i rätt siffra!**
+
+(0.5p per rätt svar, inga avdrag för fel svar):
+
+a) Vilken siffra pekar på gula fläcken?
+b) Vilken siffra pekar på choroidea?
+
+```spoiler-block:
+a) 7
+b) 6
+```
+"""
+        file_path = create_question_file("textfield.md", content)
+        question = parse_question(file_path)
+
+        assert "frågetyp/textfält" in question.metadata["tags"]
+        assert len(question.nodes) > 0
+
+    def test_parse_matching_question(self, create_question_file):
+        """Test parsing a matching question"""
+        content = """---
+tags: [ah2, provfråga, frågetyp/matching, histologi]
+date: 2023-05-31
+---
+Vilka av följande stödjeceller finns i CNS? Markera JA eller NEJ för varje angiven celltyp:
+(1p för alla rätt, inga delpoäng)
+
+- a) oligodendrocyter
+- b) Astrocyter
+- c) satellitceller
+- d) ependymceller
+- e) mikroglia
+- f) Schwannceller
+
+- JA, finn i CNS
+- NEJ, finns inte i CNS 
+
+```spoiler-block:
+a) JA, finn i CNS
+b) JA, finn i CNS
+c) NEJ, finns inte i CNS
+d) JA, finn i CNS
+e) JA, finn i CNS
+f) NEJ, finns inte i CNS
+```
+"""
+        file_path = create_question_file("matching.md", content)
+        question = parse_question(file_path)
+
+        assert "frågetyp/matching" in question.metadata["tags"]
+        lists = [n for n in question.nodes if n.type == "list"]
+        assert len(lists) > 0
+
+    def test_parse_question_with_image(self, create_question_file):
+        """Test parsing a question with embedded images"""
+        content = """---
+tags: [ah2, provfråga, frågetyp/textfält, öra, anatomi, bild]
+date: 2022-01-15
+---
+![[image-4.png|292x316]]
+**Fyll i rätt siffra !**
+
+(0.5p per rätt svar, inga avdrag för fel svar):
+
+a) Vilken siffra pekar på incus? (1..19)
+b) Vilken siffra pekar på tuba auditiva? (1..19)
+
+```spoiler-block:
+a) 7
+b) 18
+```
+"""
+        file_path = create_question_file("image_q.md", content)
+        question = parse_question(file_path)
+
+        assert "bild" in question.metadata["tags"]
+        assert "![[image-4.png" in question.raw_content
+        embed = question.nodes[0].children[0]
+        assert embed.type == "embed"
+        assert embed.attrs == {
+            "filename": "image-4.png",
+            "width": 292,
+            "height": 316
+        }
+
+    @pytest.mark.parametrize("invalid_content", [
+        "",  # Empty content
+        "No frontmatter",  # No frontmatter
+        "---\n---\n",  # Empty frontmatter
+    ])
+    def test_parse_edge_cases(self, create_question_file, invalid_content):
+        """Test parsing edge cases"""
+        file_path = create_question_file("edge.md", invalid_content)
+        question = parse_question(file_path)
+        assert isinstance(question, ParsedQuestion)
+
+    def test_parse_question_preserves_structure(self, create_question_file):
+        """Test that parsing preserves the document structure"""
+        content = """---
+tags: [ah2]
+---
+# Heading
+
+Paragraph text
+
+- List item 1
+- List item 2
+
+```spoiler-block:
+Answer
+```
+"""
+        file_path = create_question_file("structure.md", content)
+        question = parse_question(file_path)
+
+        node_types = [n.type for n in question.nodes]
+        assert "heading" in node_types
+        assert "paragraph" in node_types
+        assert "list" in node_types
+        assert "block_code" in node_types
+
+
+class TestParsedQuestionDataclass:
+    """Test the ParsedQuestion dataclass"""
+
+    def test_parsed_question_defaults(self):
+        """Test ParsedQuestion default values"""
+        question = ParsedQuestion()
+        assert question.metadata == {}
+        assert question.raw_content == ""
+        assert question.nodes == []
+
+    def test_parsed_question_initialization(self):
+        """Test ParsedQuestion initialization with values"""
+        metadata = {"tags": ["test"], "date": "2022-01-15"}
+        content = "Test content"
+        nodes = [Node({"type": "paragraph"})]
+
+        question = ParsedQuestion(
+            metadata=metadata,
+            raw_content=content,
+            nodes=nodes
+        )
+
+        assert question.metadata == metadata
+        assert question.raw_content == content
+        assert question.nodes == nodes
+
+
+class TestRealQuestions:
+    """Test parsing real questions from the exam files"""
+
+    @pytest.fixture
+    def exam_dir(self):
+        """Get the real exam directory"""
+        root = pathlib.Path(__file__).parent.parent.parent
+        exam_path = root / "content" / "Anatomi & Histologi 2" / "Gamla tentor"
+        if exam_path.exists():
+            return exam_path
+        pytest.skip("Exam directory not found")
+
+    @pytest.mark.parametrize("exam_date,question_num", [
+        ("2022-01-15", "1"),
+        ("2022-01-15", "2"),
+        ("2022-01-15", "3"),
+        ("2022-01-15", "4"),
+        ("2022-06-01", "8"),
+    ])
+    def test_parse_real_exam_questions(self, exam_dir, exam_date, question_num):
+        """Test parsing real exam questions"""
+        file_path = exam_dir / exam_date / f"{question_num}.md"
+        if not file_path.exists():
+            pytest.skip(f"Question file {file_path} not found")
+
+        question = parse_question(file_path)
+
+        # Verify metadata exists and has required fields
+        assert "tags" in question.metadata
+        assert isinstance(question.metadata["tags"], list)
+        assert "ah2" in question.metadata["tags"]
+        assert "provfråga" in question.metadata["tags"]
+
+        # Verify content was parsed
+        assert len(question.raw_content) > 0
+        assert len(question.nodes) > 0
+
+    def test_parse_all_short_named_questions(self, exam_dir):
+        """Test parsing all questions with short filenames (1-2 chars)"""
+        questions_found = 0
+
+        for file in sorted(exam_dir.glob("*/*.md")):
+            if len(file.stem) <= 2 and file.stem.isdigit():
+                question = parse_question(file)
+                assert isinstance(question, ParsedQuestion)
+                assert "tags" in question.metadata
+                questions_found += 1
+
+        # Ensure we found at least some questions
+        assert questions_found > 0, "No exam questions found to test"
+
+
+class TestNodeTextExtraction:
+    """Test text extraction from complex node structures"""
+
+    @pytest.mark.parametrize("token,expected_text", [
+        # Simple text
+        ({"type": "text", "raw": "Hello"}, "Hello"),
+
+        # Paragraph with multiple text children
+        (
+            {
+                "type": "paragraph",
+                "children": [
+                    {"type": "text", "raw": "A "},
+                    {"type": "text", "raw": "B "},
+                    {"type": "text", "raw": "C"},
+                ]
+            },
+            "A B C"
+        ),
+
+        # Nested formatting
+        (
+            {
+                "type": "paragraph",
+                "children": [
+                    {"type": "text", "raw": "Normal "},
+                    {
+                        "type": "emphasis",
+                        "children": [{"type": "text", "raw": "italic"}]
+                    },
+                    {"type": "text", "raw": " "},
+                    {
+                        "type": "strong",
+                        "children": [{"type": "text", "raw": "bold"}]
+                    },
+                ]
+            },
+            "Normal italic bold"
+        ),
+
+        # Empty node
+        ({"type": "paragraph", "children": []}, ""),
+    ])
+    def test_complex_text_extraction(self, token, expected_text):
+        """Test text extraction from complex nested structures"""
+        node = Node(token)
+        assert node.text == expected_text
+

quiz/quiz/utils/importer.py

@@ -1,8 +1,13 @@
 import re
-from pathlib import Path
 from collections import defaultdict
+from datetime import datetime
+from pathlib import Path
 from typing import Tuple
-from quiz.models import Question, Option
+
+from django.conf import settings
+
+from quiz.models import Course, Exam, Question, Option
+from quiz.utils.question_parser import parse_question_from_content, Node
 
 
 class ImportStats:
@@ -70,175 +75,10 @@ class ImportStats:
         return "\n".join(lines)
 
 
-def parse_matching_question(content: str) -> Tuple[bool, dict]:
-    """
-    Parse matching question from markdown.
-    
-    Expected format:
-    - Two consecutive bullet lists (with "- " prefix)
-    - First list = left column items (rows)
-    - Second list = top row items (columns)
-    - Answer format: "LeftItem: TopItem" pairs
-    
-    Returns:
-        (is_matching, question_data) where question_data contains:
-        - text: question text
-        - left_items: list of left column items
-        - top_items: list of top row items  
-        - correct_pairs: list of [left_idx, top_idx] pairs (0-indexed)
-        - has_answer: whether it has an answer (not TODO)
-        - question_type: 'matching'
-    """
-    lines = content.split('\n')
-    
-    # Extract question text (first non-empty line after frontmatter)
-    question_text = None
-    in_frontmatter = False
-    frontmatter_done = False
-    
-    for line in lines:
-        if line.strip() == '---':
-            if not in_frontmatter:
-                in_frontmatter = True
-            else:
-                in_frontmatter = False
-                frontmatter_done = True
-            continue
-            
-        if frontmatter_done and line.strip() and not line.startswith('![['):
-            if not line.startswith('-') and not line.startswith('```'):
-                question_text = line.strip().replace('**', '')
-                break
-    
-    if not question_text:
-        return True, {
-            'text': None,
-            'left_items': [],
-            'top_items': [],
-            'correct_pairs': [],
-            'has_answer': False,
-            'question_type': 'matching'
-        }
-    
-    # Extract two consecutive bullet lists
-    left_items = []
-    top_items = []
-    in_first_list = False
-    in_second_list = False
-    in_frontmatter = False
-    frontmatter_done = False
-    found_question_text = False
-    
-    for line in lines:
-        # Track frontmatter
-        if line.strip() == '---':
-            if not in_frontmatter:
-                in_frontmatter = True
-            else:
-                in_frontmatter = False
-                frontmatter_done = True
-            continue
-            
-        if in_frontmatter or not frontmatter_done:
-            continue
-        
-        # Skip spoiler blocks
-        if line.strip().startswith('```'):
-            break
-            
-        # Found question text
-        if not found_question_text and question_text in line:
-            found_question_text = True
-            continue
-            
-        if not found_question_text:
-            continue
-        
-        # Look for bullet lists
-        if line.strip().startswith('- '):
-            item = line.strip()[2:].strip()
-            if not item:  # Empty bullet
-                continue
-                
-            if not in_first_list and not in_second_list:
-                in_first_list = True
-                left_items.append(item)
-            elif in_first_list:
-                left_items.append(item)
-            elif in_second_list:
-                top_items.append(item)
-        elif line.strip() == '':
-            # Empty line - transition from first list to second
-            if in_first_list and left_items:
-                in_first_list = False
-                in_second_list = True
-        elif not line.strip().startswith('-') and (in_first_list or in_second_list):
-            # Non-bullet line after starting lists - end of lists
-            break
-    
-    # Parse answer from spoiler block
-    correct_pairs = []
-    has_answer = False
-    in_spoiler = False
-    answer_lines = []
-    
-    for line in lines:
-        if line.strip().startswith('```spoiler-block'):
-            in_spoiler = True
-            continue
-        if in_spoiler:
-            if line.strip() == '```':
-                break
-            stripped = line.strip()
-            if stripped:
-                answer_lines.append(stripped)
-    
-    if answer_lines:
-        full_answer = ' '.join(answer_lines)
-        
-        # Check for TODO
-        if 'TODO' in full_answer.upper():
-            has_answer = False
-        else:
-            has_answer = True
-            # Parse "Item: Match" format
-            # Example: "Smak: Lobus Insularis"
-            for line in answer_lines:
-                if ':' in line:
-                    left_part, top_part = line.split(':', 1)
-                    left_part = left_part.strip()
-                    top_part = top_part.strip()
-                    
-                    # Find indices
-                    left_idx = None
-                    top_idx = None
-                    
-                    for idx, item in enumerate(left_items):
-                        if left_part.lower() in item.lower() or item.lower() in left_part.lower():
-                            left_idx = idx
-                            break
-                    
-                    for idx, item in enumerate(top_items):
-                        if top_part.lower() in item.lower() or item.lower() in top_part.lower():
-                            top_idx = idx
-                            break
-                    
-                    if left_idx is not None and top_idx is not None:
-                        correct_pairs.append([left_idx, top_idx])
-    
-    return True, {
-        'text': question_text,
-        'left_items': left_items,
-        'top_items': top_items,
-        'correct_pairs': correct_pairs,
-        'has_answer': has_answer,
-        'question_type': 'matching'
-    }
-
 
 def parse_markdown_question(file_path: Path, content: str) -> Tuple[bool, dict]:
     """
-    Parse a markdown file and extract question data.
+    Parse a markdown file and extract question data using the new question_parser.
 
     Returns:
         (is_mcq, question_data) where question_data contains:
@@ -247,84 +87,67 @@ def parse_markdown_question(file_path: Path, content: str) -> Tuple[bool, dict]:
         - correct_answer: the correct answer letter(s)
         - has_answer: whether it has an answer (not TODO)
         - tags: list of tag strings
+        - question_type: type of question (mcq, scq, matching, etc.)
     """
-    lines = content.split('\n')
+    # Parse from content string (works for both test cases and real files)
+    parsed = parse_question_from_content(content)
 
-    # Check for question tags in frontmatter
+    # Extract metadata
-    # Accept: frågetyp/mcq, frågetyp/scq, frågetyp/textalternativ, frågetyp/textfält
+    metadata = parsed.metadata
-    is_question = False
+    tags = metadata.get('tags', [])
+
+    # Check for question type in tags
     question_type = None
-    in_frontmatter = False
+    is_question = False
-
-    for line in lines:
-        if line.strip() == '---':
-            if in_frontmatter:
-                # End of frontmatter
-                in_frontmatter = False
-                break
-            else:
-                in_frontmatter = True
-            continue
-
-        if in_frontmatter:
-            if 'frågetyp/' in line:
-                is_question = True
-                # Extract question type
-                if 'frågetyp/mcq' in line:
-                    question_type = 'mcq'
-                elif 'frågetyp/scq' in line:
-                    question_type = 'scq'
-                elif 'frågetyp/matching' in line:
-                    question_type = 'matching'
-                elif 'frågetyp/textalternativ' in line:
-                    question_type = 'textalternativ'
-                elif 'frågetyp/textfält' in line:
-                    question_type = 'textfält'
-            
-            if line.strip().lower().startswith('tags:'):
-                # Extract tags
-                # Handle: tags: [tag1, tag2] or tags: tag1, tag2
-                tag_content = line.split(':', 1)[1].strip()
-                # Remove brackets if present
-                tag_content = tag_content.strip('[]')
-                # Split by comma
-                tags = [t.strip() for t in tag_content.split(',') if t.strip()]
-
-    # If it's a matching question, use the matching parser
-    if question_type == 'matching':
-        is_matching, matching_data = parse_matching_question(content)
-        if is_matching:
-            # Add tags to the data
-            matching_data['tags'] = tags if 'tags' in locals() else []
-            return True, matching_data
 
+    for tag in tags:
+        if 'frågetyp/' in tag:
+            is_question = True
+            if 'frågetyp/mcq' in tag:
+                question_type = 'mcq'
+            elif 'frågetyp/scq' in tag:
+                question_type = 'scq'
+            elif 'frågetyp/matching' in tag:
+                question_type = 'matching'
+            elif 'frågetyp/textalternativ' in tag:
+                question_type = 'textalternativ'
+            elif 'frågetyp/textfält' in tag:
+                question_type = 'textfält'
 
     if not is_question:
         return False, {}
 
-    # Extract question text (first non-empty line after frontmatter)
+    # Handle matching questions separately
-    question_text = None
+    if question_type == 'matching':
-    in_frontmatter = False
+        return parse_matching_question_from_nodes(parsed.nodes, tags)
-    frontmatter_done = False
 
-    for line in lines:
+    # Extract question text from first paragraph (skip images and special instructions)
-        if line.strip() == '---':
+    question_text = None
-            if not in_frontmatter:
+    for node in parsed.nodes:
-                in_frontmatter = True
+        if node.type != "paragraph":
-            else:
+            continue
-                in_frontmatter = False
+        text = node.text.strip()
-                frontmatter_done = True
+        # Skip empty paragraphs
+        if not text:
             continue
 
-        if frontmatter_done and line.strip() and not line.startswith('![['):
+        # Remove inline images from text first
-            # Skip "Välj ett/två alternativ:" lines
+        text = re.sub(r'!\[\[.*?\]\]', '', text).strip()
-            if 'Välj' in line and 'alternativ' in line:
+
-                continue
+        # Skip if paragraph was only an image reference
-            if not line.startswith('-') and not line.startswith('```'):
+        if not text:
-                question_text = line.strip().replace('**', '')
+            continue
-                break
+
+        # Skip "Välj X alternativ" instructions
+        if 'Välj' in text and 'alternativ' in text:
+            continue
+
+        # Clean up bold markers
+        text = text.replace('**', '')
+        if text:
+            question_text = text
+            break
 
-    # Return early if no question text found, but include has_answer field
     if not question_text:
         return True, {
             'text': None,
@@ -332,62 +155,38 @@ def parse_markdown_question(file_path: Path, content: str) -> Tuple[bool, dict]:
             'correct_answer': '',
             'has_answer': False,
             'question_type': question_type,
-            'tags': tags if 'tags' in locals() else []
+            'tags': tags
         }
 
-
+    # Extract options from list nodes
-    # Extract options (pattern: "- A:" or "- A" for MCQ, or text for textalternativ)
     options_data = []
-    in_frontmatter = False
-    frontmatter_done = False
-    in_spoiler = False
 
-    for line in lines:
+    for node in parsed.nodes:
-        # Track frontmatter to skip it
+        if node.type != "list":
-        if line.strip() == '---':
-            if not in_frontmatter:
-                in_frontmatter = True
-            else:
-                in_frontmatter = False
-                frontmatter_done = True
             continue
+        for item in node.children:
+            # Get the text of the list item
+            if item.type != "list_item":
+                continue
+            item_text = item.text.strip()
 
-        # Skip frontmatter and spoiler blocks
+            # Match "A: text" or just "A"
-        if in_frontmatter or not frontmatter_done:
+            match = re.match(r'^([A-Z]):\s*(.*)$', item_text)
-            continue
+            if match:
-
+                letter = match.group(1)
-        if line.strip().startswith('```spoiler-block:'):
+                text = match.group(2).strip()
-            in_spoiler = True
+                options_data.append((letter, text))
-            continue
+            elif re.match(r'^([A-Z])$', item_text):
-        if in_spoiler:
+                letter = item_text
-            if line.strip() == '```':
+                options_data.append((letter, ''))
-                in_spoiler = False
+            elif question_type in ['textalternativ', 'textfält']:
-            continue
+                # For text-based questions, use incrementing letters
-
+                if not re.match(r'^[a-z]\)', item_text):  # Skip sub-question markers
-        # Match "- A: text" or "- A: " or just "- A"
-        match = re.match(r'^-\s*([A-Z]):\s*(.*)$', line.strip())
-        if not match:
-            # Also try "- A" without colon
-            match = re.match(r'^-\s*([A-Z])$', line.strip())
-
-        if match:
-            letter = match.group(1)
-            text = match.group(2) if len(match.groups()) > 1 else ""
-            options_data.append((letter, text.strip()))
-        else:
-            # For textalternativ, options might be plain text items
-            if question_type in ['textalternativ', 'textfält'] and line.strip().startswith('-') and not line.strip().startswith('--'):
-                # Extract text after dash
-                option_text = line.strip()[1:].strip()
-                # Skip if it's a sub-question marker like "a)" or "b)"
-                if option_text and not re.match(r'^[a-z]\)', option_text):
-                    # Use incrementing letters for text options
                     letter = chr(ord('A') + len(options_data))
-                    options_data.append((letter, option_text))
+                    options_data.append((letter, item_text))
 
     # For text-based questions, options are optional
     if not options_data:
-        # At least return something for single-option questions
         options_data = [('A', '')]
     elif len(options_data) < 2 and question_type in ['mcq', 'scq']:
         return True, {
@@ -395,44 +194,34 @@ def parse_markdown_question(file_path: Path, content: str) -> Tuple[bool, dict]:
             'options': options_data,
             'correct_answer': '',
             'has_answer': False,
-            'question_type': question_type
+            'question_type': question_type,
+            'tags': tags
         }
 
     # Extract answer from spoiler block
     correct_answer = None
     has_answer = False
-    in_spoiler = False
-    answer_lines = []
 
-    for line in lines:
+    for node in parsed.nodes:
-        if line.strip().startswith('```spoiler-block:'):
+        if node.type == "block_code" and node.attrs.get("info") == "spoiler-block:":
-            in_spoiler = True
+            answer_text = node.raw.strip()
-            continue
-        if in_spoiler:
-            if line.strip() == '```':
-                break
-            stripped = line.strip()
-            if stripped:
-                answer_lines.append(stripped)
 
-    # Process collected answer lines
+            # Check for TODO
-    if answer_lines:
+            if 'TODO' in answer_text.upper():
-        full_answer = ' '.join(answer_lines)
+                has_answer = False
-
-        # Check for TODO
-        if 'TODO' in full_answer.upper():
-            has_answer = False
-        else:
-            has_answer = True
-
-            # For MCQ/SCQ: Extract capital letters
-            if question_type in ['mcq', 'scq']:
-                letters = re.findall(r'\b([A-Z])\b', full_answer)
-                if letters:
-                    correct_answer = ','.join(sorted(set(letters)))
             else:
-                # For text-based questions: Store the full answer text
+                has_answer = True
-                correct_answer = full_answer[:200]  # Limit to 200 chars for database field
+
+                # For MCQ/SCQ: Extract capital letters
+                if question_type in ['mcq', 'scq']:
+                    letters = re.findall(r'\b([A-Z])\b', answer_text)
+                    if letters:
+                        correct_answer = ','.join(sorted(set(letters)))
+                else:
+                    # For text-based questions: Store the full answer text
+                    correct_answer = answer_text[:200]  # Limit to 200 chars for database field
+
+            break
 
     return True, {
         'text': question_text,
@@ -440,7 +229,112 @@ def parse_markdown_question(file_path: Path, content: str) -> Tuple[bool, dict]:
         'correct_answer': correct_answer,
         'has_answer': has_answer,
         'question_type': question_type,
-        'tags': tags if 'tags' in locals() else []
+        'tags': tags
+    }
+
+
+def parse_matching_question_from_nodes(nodes: list[Node], tags: list) -> Tuple[bool, dict]:
+    """
+    Parse matching question from parsed nodes.
+
+    Expected format:
+    - Two consecutive bullet lists
+    - First list = left column items (rows)
+    - Second list = top row items (columns)
+    - Answer format: "LeftItem: TopItem" pairs
+
+    Returns:
+        (is_matching, question_data)
+    """
+    # Extract question text
+    question_text = None
+    for node in nodes:
+        if node.type == "paragraph":
+            text = node.text.strip()
+            # Remove inline images
+            text = re.sub(r'!\[\[.*?\]\]', '', text).strip()
+            # Skip if empty after removing images
+            if not text:
+                continue
+            question_text = text.replace('**', '')
+            break
+
+    if not question_text:
+        return True, {
+            'text': None,
+            'left_items': [],
+            'top_items': [],
+            'correct_pairs': [],
+            'has_answer': False,
+            'question_type': 'matching',
+            'tags': tags
+        }
+
+    # Extract two consecutive lists
+    left_items = []
+    top_items = []
+    list_nodes = [node for node in nodes if node.type == "list"]
+
+    if len(list_nodes) >= 2:
+        # First list = left items
+        for item in list_nodes[0].children:
+            if item.type == "list_item":
+                left_items.append(item.text.strip())
+
+        # Second list = top items
+        for item in list_nodes[1].children:
+            if item.type == "list_item":
+                top_items.append(item.text.strip())
+
+    # Parse answer from spoiler block
+    correct_pairs = []
+    has_answer = False
+
+    for node in nodes:
+        if node.type == "block_code" and node.attrs.get("info") == "spoiler-block:":
+            answer_text = node.raw.strip()
+
+            # Check for TODO
+            if 'TODO' in answer_text.upper():
+                has_answer = False
+                break
+            has_answer = True
+            # Parse "Item: Match" format
+            answer_lines = answer_text.split('\n')
+            for line in answer_lines:
+                line = line.strip()
+                if ':' not in line:
+                    continue
+                left_part, top_part = line.split(':', 1)
+                left_part = left_part.strip()
+                top_part = top_part.strip()
+
+                # Find indices
+                left_idx = None
+                top_idx = None
+
+                for idx, item in enumerate(left_items):
+                    if left_part.lower() in item.lower() or item.lower() in left_part.lower():
+                        left_idx = idx
+                        break
+
+                for idx, item in enumerate(top_items):
+                    if top_part.lower() in item.lower() or item.lower() in top_part.lower():
+                        top_idx = idx
+                        break
+
+                if left_idx is not None and top_idx is not None:
+                    correct_pairs.append([left_idx, top_idx])
+            break
+
+    return True, {
+        'text': question_text,
+        'left_items': left_items,
+        'top_items': top_items,
+        'correct_pairs': correct_pairs,
+        'has_answer': has_answer,
+        'question_type': 'matching',
+        'tags': tags
     }
 
 
@@ -460,7 +354,6 @@ def import_question_file(file_path: Path, base_path: Path, stats: ImportStats, f
         file_mtime = file_path.stat().st_mtime
 
         # Calculate path relative to project root
-        from django.conf import settings
         project_root = settings.BASE_DIR.parent
         try:
             file_path_str = str(file_path.relative_to(project_root))
@@ -518,9 +411,6 @@ def import_question_file(file_path: Path, base_path: Path, stats: ImportStats, f
             # Try to parse as date
             if exam_folder and '-' in exam_folder:
                 try:
-                    from datetime import datetime
-                    from quiz.models import Course, Exam
-
                     exam_date = datetime.strptime(exam_folder, '%Y-%m-%d').date()
 
                     # Get or create course (default to "Anatomi & Histologi 2")
@@ -610,17 +500,6 @@ def import_question_file(file_path: Path, base_path: Path, stats: ImportStats, f
 
 
 def import_questions(folder_path: Path, base_path: Path = None, force: bool = False) -> ImportStats:
-    """
-    Import all questions from a folder.
-
-    Args:
-        folder_path: Path to the folder containing question markdown files
-        base_path: Base path for relative path calculations (defaults to folder_path)
-        force: If True, import all files regardless of mtime (for initial import)
-
-    Returns:
-        ImportStats object with import statistics
-    """
     if base_path is None:
         base_path = folder_path
 
@@ -634,9 +513,7 @@ def import_questions(folder_path: Path, base_path: Path = None, force: bool = Fa
 
 
 def delete_question_by_path(file_path: Path):
-    """Delete a question from the database by file path"""
     try:
-        from django.conf import settings
         project_root = settings.BASE_DIR.parent
         file_path_str = str(file_path.relative_to(project_root))
         deleted_count, _ = Question.objects.filter(file_path=file_path_str).delete()

quiz/quiz/utils/obsidian_embed_plugin.py

@@ -0,0 +1,38 @@
+__all__ = ["obsidian_embed"]
+
+# https://help.obsidian.md/embeds
+
+# Supported:
+# ![[image-4.png|292x316]]
+def parse_embed(inline, match, state):
+    filename = match.group("filename")
+    attrs = {}
+    if "|" in filename:
+        filename, size = filename.split("|", 1)
+    else:
+        size = None
+    attrs["filename"] = filename
+    if size:
+        if "x" in size:
+            width, height = size.split("x", 1)
+            if width:
+                attrs["width"] = int(width)
+            if height:
+                attrs["height"] = int(height)
+        else:
+            attrs["width"] = int(size)
+    state.append_token({"type": "embed", "attrs": attrs})
+    return match.end()
+
+
+INLINE_EMBED_PATTERN = (
+    r'!\[\[' # begins with ![
+    r'(?!\s)'  # not whitespace
+    r'(?P<filename>.+?)'  # content between `![[xx]]`
+    r'(?!\s)'  # not whitespace
+    r'\]\]' # closing ]
+)
+
+
+def obsidian_embed(md: "Markdown") -> None:
+    md.inline.register('embed', INLINE_EMBED_PATTERN, parse_embed, before="link")

quiz/quiz/utils/question_parser.py

@@ -0,0 +1,89 @@
+import dataclasses
+import pathlib
+
+import frontmatter
+import mistune
+
+from quiz.utils.obsidian_embed_plugin import obsidian_embed
+
+markdown = mistune.create_markdown(renderer="ast", plugins=[obsidian_embed])
+
+
+class Node:
+    def __init__(self, token):
+        self.type = token["type"]
+        self.raw = token.get("raw", "")
+        self.attrs = token.get("attrs", {})
+        self.children = [Node(token=child) for child in token.get("children", [])]
+
+    def __repr__(self) -> str:
+        attrs = []
+        if self.raw:
+            attrs.append(f"raw={self.raw!r}")
+        if self.attrs:
+            attrs.append(f"attrs={self.attrs!r}")
+        if self.children:
+            attrs.append(f"children={self.children!r}")
+        # block_text -> BlockText
+        pretty = self.type.replace("_", " ").title().replace(" ", "")
+        return f"{pretty}(" + ", ".join(attrs) + ")"
+
+    @property
+    def text(self) -> str:
+        if self.type == "text":
+            return self.raw
+        texts = []
+        for child in self.children:
+            texts.append(child.text)
+        return "".join(texts)
+
+
+@dataclasses.dataclass
+class ParsedQuestion:
+    metadata: dict = dataclasses.field(default_factory=dict)
+    raw_content: str = ""
+    nodes: list[Node] = dataclasses.field(default_factory=list)
+
+
+def parse_question(path: pathlib.Path):
+    raw = path.read_text(encoding="utf-8")
+    return parse_question_from_content(raw)
+
+
+def parse_question_from_content(content_str: str):
+    """Parse question from a content string instead of a file."""
+    metadata, content = frontmatter.parse(content_str)
+    tokens = markdown(content)
+    question = ParsedQuestion(
+        metadata=metadata,
+        raw_content=content,
+        nodes=[Node(token=token) for token in tokens],
+    )
+    return question
+
+
+def main():
+    root = pathlib.Path(__file__).parent.parent.parent.parent
+    print(root)
+    exams = root / "content" / "Anatomi & Histologi 2" / "Gamla tentor"
+    for file in sorted(exams.glob("*/*.md")):
+        if len(file.stem) > 2:
+            continue
+        question = parse_question(file)
+        print(question.metadata, repr(question.raw_content))
+        continue
+        for node in question.nodes:
+            match node.type:
+                case "heading":
+                    print("Heading:", repr(node.text))
+                case "paragraph":
+                    print("Paragraph:", repr(node.text))
+                case "list":
+                    print("List:")
+                    for child in node.children:
+                        print(" - List item:", repr(child.text))
+                case "block_code" if node.attrs["info"] == "spoiler-block:":
+                    print("Spoiler:", repr(node.raw.rstrip()))
+
+if __name__ == "__main__":
+    main()