Create CDRM.py

CDRM.py

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+# CDRM.py
+# PLACEHOLDER FILE
+# Contains all data, scoring logic, and setup specific to the
+# Clinical Dementia Rating plus NACC FTLD (CDR Memory) assessment.
+
+import time
+from gtts import gTTS
+import utils
+
+# --- CDRM Specific Data (Placeholder) ---
+now = utils.now
+GROUPED_QUESTIONS = {
+    "Question 1: Memory": {
+        "Can you please tell me what the date is today?": {"answer": f"{now.strftime('%A')}, {now.strftime('%B')} {now.day}, {now.year}", "instruction": "Score 1 for fully correct, 0 for any error.", "max_points": 1},
+        "I will give you a name and address to remember. Please repeat it. John Brown, 42 Market Street, Boston.": {"answer": "john brown 42 market street boston", "instruction": "Repeat up to 3 times if necessary. This is for registration, not scoring.", "max_points": 0}
+    },
+    "Question 2: Orientation": {
+        "Who is the current president?": {"answer": "joe biden", "instruction": "Score 1 for correct, 0 for incorrect.", "max_points": 1}
+    }
+}
+
+# --- Derived Data Structures ---
+STRUCTURED_QUESTIONS = []
+main_num = 1
+for section, questions in GROUPED_QUESTIONS.items():
+    main_cat_name = section.split(":", 1)[1].strip()
+    sub_q_idx = 0
+    for question, data in questions.items():
+        STRUCTURED_QUESTIONS.append({
+            "main_cat": main_cat_name, "main_num": main_num, "sub_letter": chr(ord('a') + sub_q_idx),
+            "question": question, "answer": data["answer"], "instruction": data.get("instruction", ""),
+            "max_points": data.get("max_points", 1)
+        })
+        sub_q_idx += 1
+    main_num += 1
+
+TOTAL_QUESTIONS = len(STRUCTURED_QUESTIONS)
+QUESTION_CHOICES = [f"Q{q['main_num']}{q['sub_letter']}: {q['question']}" for q in STRUCTURED_QUESTIONS]
+
+# --- CDRM Specific Audio Handling ---
+AUDIO_FILE_MAP = {}
+def pregenerate_audio():
+    """Pre-generates all TTS audio for CDRM questions."""
+    print("Pre-generating CDRM TTS audio...")
+    # This is a placeholder. In a real scenario, you'd generate audio like in MMSE.py
+    print("CDRM TTS audio pre-generation complete (Placeholder).")
+
+def speak_question(current_index):
+    """Placeholder for speaking a CDRM question."""
+    # In a real implementation, this would return a file path.
+    print(f"Speaking CDRM Question {current_index}: {STRUCTURED_QUESTIONS[current_index]['question']}")
+    return None
+
+# --- Main Evaluation Logic (Placeholder) ---
+def evaluate_CDRM(answers_list, user_drawing_path=None):
+    """
+    Evaluates all CDRM answers and returns the results.
+    This is a dummy function and returns a fixed score.
+    """
+    total_score = 1
+    total_possible_score = 2
+    results = [
+        "Q1a: Can you please tell me what the date is today?\n  - Score: 1 / 1 | Your Answer: '[User Answer]' | Expected: '[Correct Date]'",
+        "Q2a: Who is the current president?\n  - Score: 0 / 1 | Your Answer: '[User Answer]' | Expected: 'joe biden'"
+    ]
+    return "\n\n".join(results), f"{total_score} / {total_possible_score}"
+
+