# 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}"