dbi_mcp.py

from __future__ import annotations

import math
import re
import logging
import functools
from pathlib import Path
from typing import Dict, List, Tuple, Optional, Union, Mapping, Sequence

from brand_to_generic import brand_lookup

import csv
import json

try:
    import pandas as pd
except ImportError:
    pd = None 

__all__ = [
    "load_reference",
    "load_all_routes_reference",
    "load_patient_meds",
    "calculate_dbi",
    "print_report",
    "detect_route_from_text",
    "detect_combination_drug",
    "split_combination_drug_simple",
    "dbi_mcp",
    "dbi_mcp_mixed_routes",
    "dbi_mcp_with_combinations",
]

PatientInput = Union[
    Path,
    Sequence[Tuple[str, float]],
    Mapping[str, float],
]

# Combination drug detection patterns
COMBINATION_PATTERNS = [
    r'\bco-?\w+\b',     # co- prefix with optional hyphen (co-codamol, cocodamol)
    r'\b\w+[-/]\w+\b',  # hyphen or slash separated (paracetamol-codeine, aspirin/caffeine)
    r'\b\w+\s*\+\s*\w+\b',  # plus sign (aspirin + caffeine)
    r'\b\w+\s*with\s+\w+\b',  # "with" combinations
    r'\b\w+\s*and\s+\w+\b',   # "and" combinations
]

# Route detection patterns
ROUTE_PATTERNS = {
    'transdermal': [
        r'\bpatch(es)?\b',
        r'\btransdermal\b',
        r'\bmcg/hr\b',
        r'\bμg/hr\b',
        r'\bmicrograms?/hr\b',
        r'\bmicrograms?/hour\b',
    ],
    'parenteral': [
        r'\binjection\b',
        r'\biv\b',
        r'\bim\b',
        r'\bsc\b',
        r'\bsubcut\b',
        r'\bsubcutaneous\b',
        r'\bintravenous\b',
        r'\bintramuscular\b',
        r'\bparenteral\b',
    ],
    'sublingual_buccal': [
        r'\bsublingual\b',
        r'\bbuccal\b',
        r'\bsl\b',
        r'\bunder.?tongue\b',
    ],
    'oral': [
        r'\btab(let)?s?\b',
        r'\bcap(sule)?s?\b',
        r'\boral\b',
        r'\bpo\b',
        r'\bby.?mouth\b',
        r'\bliquid\b',
        r'\bsyrup\b',
        r'\bsolution\b',
        r'\bsuspension\b',
    ]
}


def _normalise_name(name: str) -> str:
    """Strip/-lower a drug name for key matching."""
    return name.strip().lower()


def detect_route_from_text(text: str) -> str:
    """
    Detect the most likely route of administration from medication text.
    Returns the detected route or 'oral' as default.
    """
    text_lower = text.lower()
    
    # Check each route pattern
    for route, patterns in ROUTE_PATTERNS.items():
        for pattern in patterns:
            if re.search(pattern, text_lower):
                return route
    
    # Default to oral if no specific route detected
    return 'oral'


def detect_combination_drug(drug_name: str) -> bool:
    """
    Detect if a drug name appears to be a combination drug.
    """
    drug_name_lower = drug_name.lower()
    
    for pattern in COMBINATION_PATTERNS:
        if re.search(pattern, drug_name_lower):
            return True
    
    # Check for multiple doses in parentheses (e.g., "500mg-9.6mg")
    if re.search(r'\d+(?:\.\d+)?\s*mg\s*[-/]\s*\d+(?:\.\d+)?\s*mg', drug_name_lower):
        return True
    
    return False


def split_combination_drug_simple(drug_text: str) -> List[Tuple[str, str, str]]:
    """
    Simple rule-based splitting for common combination patterns.
    Returns list of (component_name, original_text, notes).
    """
    components = []
    drug_text_lower = drug_text.lower()
    
    # Handle common combinations
    known_combinations = {
        'co-codamol': [('paracetamol', 'paracetamol component of co-codamol'), 
                       ('codeine', 'codeine component of co-codamol')],
        'cocodamol': [('paracetamol', 'paracetamol component of co-codamol'), 
                      ('codeine', 'codeine component of co-codamol')],
        'co-trimoxazole': [('trimethoprim', 'trimethoprim component of co-trimoxazole'),
                          ('sulfamethoxazole', 'sulfamethoxazole component of co-trimoxazole')],
        'cotrimoxazole': [('trimethoprim', 'trimethoprim component of co-trimoxazole'),
                        ('sulfamethoxazole', 'sulfamethoxazole component of co-trimoxazole')],
        'paracetamol-codeine': [('paracetamol', 'paracetamol component'), 
                               ('codeine', 'codeine component')],
        'aspirin-caffeine': [('aspirin', 'aspirin component'),
                            ('caffeine', 'caffeine component')],
        'tylenol-codeine': [('paracetamol', 'paracetamol component'), 
                           ('codeine', 'codeine component')],
        # Brand name combinations
        'vytorin': [('ezetimibe', 'ezetimibe component of Vytorin'),
                   ('simvastatin', 'simvastatin component of Vytorin')],
        'exforge': [('amlodipine', 'amlodipine component of Exforge'),
                   ('valsartan', 'valsartan component of Exforge')],
        'caduet': [('amlodipine', 'amlodipine component of Caduet'),
                  ('atorvastatin', 'atorvastatin component of Caduet')],
        'janumet': [('sitagliptin', 'sitagliptin component of Janumet'),
                   ('metformin', 'metformin component of Janumet')],
        'combigan': [('brimonidine', 'brimonidine component of Combigan'),
                    ('timolol', 'timolol component of Combigan')],
    }
    
    # Check for known combinations
    for combo_name, combo_components in known_combinations.items():
        if combo_name in drug_text_lower:
            for comp_name, note in combo_components:
                components.append((comp_name, drug_text, note))
            return components
    
    # Try to split hyphenated/slashed combinations
    if '-' in drug_text or '/' in drug_text:
        # Extract the drug name part (before dosing info)
        drug_name_part = re.split(r'\d+', drug_text)[0].strip()
        separators = ['-', '/', '+']
        
        for sep in separators:
            if sep in drug_name_part:
                parts = [part.strip() for part in drug_name_part.split(sep)]
                if len(parts) == 2:
                    for part in parts:
                        if part and len(part) > 2:  # Avoid single letters
                            components.append((part, drug_text, f'Component of combination drug'))
                    return components
    
    return components


def needs_llm_splitting(drug_text: str) -> bool:
    """
    Determine if a combination drug needs LLM assistance for splitting.
    """
    if not detect_combination_drug(drug_text):
        return False
    
    # Try simple splitting first
    simple_components = split_combination_drug_simple(drug_text)
    
    # If simple splitting failed or returned unclear results, use LLM
    if not simple_components:
        return True
    
    # If components are too short or unclear, use LLM
    for comp_name, _, _ in simple_components:
        if len(comp_name) < 3 or comp_name.isdigit():
            return True
    
    return False


def load_reference(
    ref_path: Path,
    *,
    route: str = "oral",
    use_pandas: bool | None = None,
) -> Dict[str, Tuple[float, str]]:
    """Return mapping **generic → (δ<sub>route</sub>, drug_class)**.

    If a drug lacks the requested route it is silently skipped.  Callers may
    retry with ``route=None`` to get the *first* available dose instead.
    """
    if use_pandas is None:
        use_pandas = pd is not None

    ref: Dict[str, Tuple[float, str]] = {}

    if use_pandas:
        df = pd.read_csv(ref_path)
        df = df[df["route"].str.lower() == route.lower()]
        for _, row in df.iterrows():
            ref[_normalise_name(row["generic_name"])] = (
                float(row["min_daily_dose_mg"]),
                row["drug_class"].strip().lower(),
            )
    else:
        with ref_path.open(newline="") as f:
            rdr = csv.DictReader(f)
            for row in rdr:
                if row["route"].strip().lower() != route.lower():
                    continue
                ref[_normalise_name(row["generic_name"])] = (
                    float(row["min_daily_dose_mg"]),
                    row["drug_class"].strip().lower(),
                )

    return ref


def load_all_routes_reference(
    ref_path: Path,
    *,
    use_pandas: bool | None = None,
) -> Dict[str, Dict[str, Tuple[float, str]]]:
    """
    Load reference data for all routes.
    Returns mapping: route → {generic → (δ, drug_class)}
    """
    if use_pandas is None:
        use_pandas = pd is not None

    all_routes: Dict[str, Dict[str, Tuple[float, str]]] = {}

    if use_pandas:
        df = pd.read_csv(ref_path)
        for _, row in df.iterrows():
            route = row["route"].strip().lower()
            generic = _normalise_name(row["generic_name"])
            
            if route not in all_routes:
                all_routes[route] = {}
            
            all_routes[route][generic] = (
                float(row["min_daily_dose_mg"]),
                row["drug_class"].strip().lower(),
            )
    else:
        with ref_path.open(newline="") as f:
            rdr = csv.DictReader(f)
            for row in rdr:
                route = row["route"].strip().lower()
                generic = _normalise_name(row["generic_name"])
                
                if route not in all_routes:
                    all_routes[route] = {}
                
                all_routes[route][generic] = (
                    float(row["min_daily_dose_mg"]),
                    row["drug_class"].strip().lower(),
                )

    return all_routes

def calculate_dbi(
    patient_meds: Mapping[str, float],
    reference: Mapping[str, Tuple[float, str]],
) -> Tuple[float, List[Tuple[str, float, float, float]]]:
    """Return ``(total, details)`` where *details* is a list of
    ``(generic_name, dose_mg, δ_mg, DBI_i)``.
    """
    details: List[Tuple[str, float, float, float]] = []
    total = 0.0

    for drug, dose in patient_meds.items():
        ref = reference.get(drug)
        if not ref:
            continue  # unknown or route-mismatch
        delta, drug_class = ref
        if drug_class not in {"anticholinergic", "sedative", "both"}:
            continue
        dbi_i = dose / (delta + dose)
        details.append((drug, dose, delta, dbi_i))
        total += dbi_i

    return total, details


logger = logging.getLogger(__name__)

UNIT_PAT = re.compile(r"(?P<val>\d+(?:[.,]\d+)?)(?:\s*)(?P<unit>mcg|μg|mg|g|iu|units?|micrograms?|mmol)\b", re.I)

PATCH_PAT = re.compile(r"(?P<val>\d+(?:[.,]\d+)?)(?:\s*)(mcg|μg|microg)\s*/\s*hr", re.I)

PERCENT_PAT = re.compile(r"\b(?P<percent>\d+(?:\.\d+)?)\s*%\b")

CONC_PAT = re.compile(r"(?P<drug_val>\d+(?:[.,]\d+)?)(?:\s*)(?P<drug_unit>mcg|μg|mg|g|iu|units?)\s*/\s*(?P<vol_val>\d+(?:[.,]\d+)?)(?:\s*)m ?l", re.I)

VOL_PAT = re.compile(r"(?P<voldose>\d+(?:[.,]\d+)?)(?:\s*)m ?l", re.I)

QTY_PAT = re.compile(r"(?<!\d)(?P<qty>\d+(?:\s*-\s*\d+)?)\s*(?:tab|caps?|puff|spray|patch|patches|sachet|tube|inhalation|drop)s?\b", re.I)

FREQ_PAT = re.compile(r"\b(q\d{1,2}h|qd|od|daily|once daily|bid|bd|twice daily|tid|tds|three times daily|qid|four times daily|nocte|mane|am|pm)\b", re.I)
EVERY_HOURS_PAT = re.compile(r"q(\d{1,2})h", re.I)

_FREQ_MAP = {
    "qd": 1, "od": 1, "daily": 1, "once daily": 1,
    "bid": 2, "bd": 2, "twice daily": 2,
    "tid": 3, "tds": 3, "three times daily": 3,
    "qid": 4, "four times daily": 4,
    "nocte": 1, "pm": 1,
    "mane": 1, "am": 1,
}

def _unit_to_mg(val: float, unit: str) -> float:
    unit = unit.lower().removesuffix('s')
    if unit == "mg":
        return val
    if unit == "g":
        return val * 1_000
    if unit in {"mcg", "μg", "microgram"}:
        return val / 1_000
    if unit in {"iu", "unit", "mmol"}:
        logger.debug("Cannot reliably convert '%s' to mg. Returning 0.", unit)
        return 0.0
    return math.nan


def _freq_to_per_day(token: str) -> float:
    token = token.lower()
    if token in _FREQ_MAP:
        return _FREQ_MAP[token]
    m = EVERY_HOURS_PAT.fullmatch(token)
    if m:
        hrs = int(m.group(1))
        return 24 / hrs if hrs else 1
    return 1

Parsed = Tuple[str, float, bool, str]  # (name, mg_day, is_prn, route)
ParsedCombination = Tuple[str, float, bool, str, bool, List[Tuple[str, str, str]]]  # (name, mg_day, is_prn, route, is_combination, components)

@functools.lru_cache(maxsize=2048)
def _parse_line(line: str) -> Optional[Parsed]:
    original = line.strip()
    if not original:
        return None

    is_prn = "prn" in original.lower()
    detected_route = detect_route_from_text(original)

    m_patch = PATCH_PAT.search(original)
    if m_patch:
        mcg_hr = float(m_patch.group("val").replace(",", "."))
        mg_day = (mcg_hr * 24) / 1_000  # µg/hr → mg/day
        name_part = PATCH_PAT.sub("", original).split()[0]
        # Override route detection for patches
        return (name_part, mg_day, is_prn, "transdermal")

    # Try parsing percentage-based topicals/solutions before standard units
    m_percent = PERCENT_PAT.search(original)
    if m_percent:
        percent_val = float(m_percent.group("percent"))
        
        # For liquids where volume is given (e.g., 2% solution, 10mL dose)
        m_vol = VOL_PAT.search(original)
        if m_vol:
            voldose_ml = float(m_vol.group("voldose").replace(",", "."))
            # Assume % is g/100mL for liquids
            strength_g_per_100ml = percent_val
            mg_per_dose = (strength_g_per_100ml * 1000) * (voldose_ml / 100)
            
            freq = 1.0
            m_freq = FREQ_PAT.search(original)
            if m_freq:
                freq = _freq_to_per_day(m_freq.group(0))
            
            mg_day = mg_per_dose * freq
            name_part = original[:m_percent.start()].strip()
            name_part = re.sub(r"[^A-Za-z0-9\s-]", " ", name_part).strip()
            return (name_part, mg_day, is_prn, detected_route)

        # Handle drops with percentage strength
        if 'drop' in original.lower():
            # Assume 20 drops/mL for ophthalmic solutions
            g_per_100ml = percent_val
            mg_per_ml = g_per_100ml * 10  # 1% -> 1g/100mL -> 10mg/mL
            
            qty = 1.0
            m_qty = QTY_PAT.search(original)  # QTY_PAT now includes 'drop'
            if m_qty:
                qty_str = m_qty.group("qty").split('-')[-1].strip() # Use upper end of range
                try:
                    qty = float(qty_str)
                except ValueError:
                    qty = 1.0
            
            # Dose in mg = (number of drops / 20 drops_per_mL) * mg_per_mL
            mg_per_dose = (qty / 20.0) * mg_per_ml
            
            freq = 1.0
            m_freq = FREQ_PAT.search(original)
            if m_freq:
                freq = _freq_to_per_day(m_freq.group(0))
                
            mg_day = mg_per_dose * freq
            name_part = original[:m_percent.start()].strip()
            name_part = re.sub(r"[^A-Za-z0-9\s-]", " ", name_part).strip()
            return (name_part, mg_day, is_prn, detected_route)

        # For cases with 'application' or 'drop' (e.g., 0.05% cream, 1 application)
        if 'application' in original.lower() or 'ointment' in original.lower():
            # Can't calculate mg dose, but we can parse the drug name.
            name_part = original[:m_percent.start()].strip()
            name_part = re.sub(r"[^A-Za-z0-9\s-]", " ", name_part).strip()
            logger.debug("Parsed %%-based item but cannot quantify mg/day: %s", original)
            return (name_part, 0.0, is_prn, detected_route)

    m_conc = CONC_PAT.search(original)
    m_vol  = VOL_PAT.search(original)
    if m_conc and m_vol:
        drug_val = _unit_to_mg(float(m_conc.group("drug_val").replace(",", ".")), m_conc.group("drug_unit"))
        vol_val  = float(m_conc.group("vol_val").replace(",", "."))
        voldose  = float(m_vol.group("voldose").replace(",", "."))
        if vol_val == 0:
            logger.warning("volume 0 in concentration parse – %s", original)
            return None
        mg_per_dose = drug_val * (voldose / vol_val)
        qty = 1
        freq = 1.0
        m_freq = FREQ_PAT.search(original)
        if m_freq:
            freq = _freq_to_per_day(m_freq.group(0))
        mg_day = mg_per_dose * freq
        name_part = CONC_PAT.split(original)[0].strip()
        return (name_part, mg_day, is_prn, detected_route)

    m = UNIT_PAT.search(original)
    if m:
        strength_mg = _unit_to_mg(float(m.group("val").replace(",", ".")), m.group("unit"))
        if math.isnan(strength_mg):
            logger.debug("Unhandled unit '%s' in line: %s", m.group("unit"), original)
            return None
        qty = 1.0
        m_qty = QTY_PAT.search(original)
        if m_qty:
            qty_str = m_qty.group("qty").split('-')[-1].strip()
            try:
                qty = float(qty_str)
            except ValueError:
                qty = 1.0
        freq = 1.0
        m_freq = FREQ_PAT.search(original)
        if m_freq:
            freq = _freq_to_per_day(m_freq.group(0))
        mg_day = strength_mg * qty * freq
        name_part = original[:m.start()].strip()
        name_part = re.sub(r"[^A-Za-z0-9\s]", " ", name_part)
        name_part = re.sub(r"\s+", " ", name_part).strip()
        return (name_part, mg_day, is_prn, detected_route)

    # Handle unitless doses like "..., 5, oral" or "..., 2.5-5, oral"
    m_unitless = re.search(r"[,\(]\s*(?P<dose>\d+(?:\.\d+)?(?:\s*-\s*\d+(?:\.\d+)?)?)\s*,\s*(?:oral|sublingual|buccal)", original, re.I)
    if m_unitless:
        dose_str = m_unitless.group("dose").split('-')[-1].strip()
        try:
            strength_mg = float(dose_str) # Assume mg
            freq = 1.0
            m_freq = FREQ_PAT.search(original)
            if m_freq:
                freq = _freq_to_per_day(m_freq.group(0))
            
            mg_day = strength_mg * freq
            name_part = original[:m_unitless.start()].strip()
            name_part = re.sub(r"\(.*?\)", "", name_part).strip() # Remove bracketed part of name
            return (name_part, mg_day, is_prn, detected_route)
        except ValueError:
            pass # Could not convert to float

    logger.debug("unhandled line: %s", original)
    return None


def _parse_line_with_combinations(line: str) -> Optional[ParsedCombination]:
    """
    Enhanced parsing that detects and handles combination drugs.
    Returns (name, mg_day, is_prn, route, is_combination, components)
    """
    # First try normal parsing
    parsed = _parse_line(line)
    if not parsed:
        return None
    
    name, mg_day, is_prn, route = parsed
    
    # Check if this is a combination drug (check both the name and the full line)
    is_combo_name = detect_combination_drug(name)
    is_combo_line = detect_combination_drug(line)
    
    if is_combo_name or is_combo_line:
        # Try splitting with both the name and the full line
        components = split_combination_drug_simple(name)
        if not components:
            components = split_combination_drug_simple(line)
        
        if components:
            logger.debug(f"Detected combination drug: {name} -> {[c[0] for c in components]}")
            return (name, mg_day, is_prn, route, True, components)
        else:
            logger.debug(f"Combination drug detected but couldn't split: {name}")
            # Mark as combination but with empty components (may need LLM splitting)
            return (name, mg_day, is_prn, route, True, [])
    
    # Not a combination drug
    return (name, mg_day, is_prn, route, False, [])

def _smart_drug_lookup(raw_name: str, all_routes_reference: Dict[str, Dict[str, Tuple[float, str]]]) -> str:
    """
    Smart drug name resolution that avoids unnecessary API calls.
    Works with multi-route reference data.
    
    1. First checks if the name (or close variant) exists in any route's reference data
    2. Only calls brand_lookup API if not found in reference
    3. Returns the best generic name match
    """
    clean_name = raw_name.strip().lower()
    
    # Check all routes for direct match
    for route_data in all_routes_reference.values():
        if clean_name in route_data:
            logger.debug(f"Direct match found for '{raw_name}' in reference data")
            return clean_name
    
    # Check all routes for partial match
    for route_data in all_routes_reference.values():
        for ref_name in route_data.keys():
            if len(clean_name) >= 4 and len(ref_name) >= 4:
                if clean_name in ref_name or ref_name in clean_name:
                    logger.debug(f"Partial match found: '{raw_name}' -> '{ref_name}' in reference data")
                    return ref_name
    
    common_variations = {
        'acetaminophen': 'paracetamol',
        'paracetamol': 'acetaminophen',
        'hydrochlorothiazide': 'hctz',
        'hctz': 'hydrochlorothiazide',
        'furosemide': 'frusemide',
        'frusemide': 'furosemide',
    }
    
    if clean_name in common_variations:
        alt_name = common_variations[clean_name]
        # Check all routes for the alternative name
        for route_data in all_routes_reference.values():
            if alt_name in route_data:
                logger.debug(f"Found common variation: '{raw_name}' -> '{alt_name}' in reference data")
                return alt_name
    
    logger.debug(f"'{raw_name}' not found in reference data, trying brand lookup API")
    try:
        lookup = brand_lookup(raw_name)
        if lookup["results"]:
            generic_name = lookup["results"][0]["generic_name"].lower().strip()
            logger.debug(f"Brand lookup successful: '{raw_name}' -> '{generic_name}'")
            return generic_name
        else:
            logger.debug(f"Brand lookup returned no results for '{raw_name}'")
            return clean_name
    except Exception as e:
        logger.warning(f"Brand lookup failed for '{raw_name}': {e}")
        return clean_name


def dbi_mcp(text_block: str, *, ref_csv: Union[str, Path] = "dbi_reference_by_route.csv", route: str = "oral") -> dict:
    """End-to-end DBI calculator with dual PRN handling and smart drug name resolution."""
    ref = load_reference(Path(ref_csv), route=route)
    
    parsed: List[Parsed] = []
    unmatched: List[str] = []
    for ln in text_block.splitlines():
        res = _parse_line(ln)
        if res:
            parsed.append(res)
        else:
            unmatched.append(ln)

    meds_with: Dict[str, float] = {}
    meds_without: Dict[str, float] = {}

    # Load all routes for smart lookup (backward compatibility)
    all_routes_ref = load_all_routes_reference(Path(ref_csv))

    for raw_name, mg_day, is_prn, detected_route in parsed:
        generic = _smart_drug_lookup(raw_name, all_routes_ref)
        
        meds_with[generic] = meds_with.get(generic, 0.0) + mg_day
        if not is_prn:
            meds_without[generic] = meds_without.get(generic, 0.0) + mg_day

    total_no, details_no = calculate_dbi(meds_without, ref)
    total_with, details_with = calculate_dbi(meds_with, ref)

    def _details_to_list(details):
        return [dict(generic_name=g, dose_mg_day=d, delta_mg=delta, dbi_component=dbi) for g, d, delta, dbi in details]

    return {
        "route": route,
        "dbi_without_prn": round(total_no, 2),
        "dbi_with_prn": round(total_with, 2),
        "details_without_prn": _details_to_list(details_no),
        "details_with_prn": _details_to_list(details_with),
        "unmatched_input": unmatched,
    }


def dbi_mcp_mixed_routes(text_block: str, *, ref_csv: Union[str, Path] = "dbi_reference_by_route.csv") -> dict:
    """
    Enhanced DBI calculator that handles mixed routes automatically.
    
    This function:
    1. Detects the route for each medication from the text
    2. Uses the appropriate reference data for each route
    3. Provides detailed breakdown by route and medication
    """
    all_routes_ref = load_all_routes_reference(Path(ref_csv))
    
    parsed: List[Parsed] = []
    unmatched: List[str] = []
    route_stats: Dict[str, int] = {}
    
    for ln in text_block.splitlines():
        res = _parse_line(ln)
        if res:
            parsed.append(res)
            route = res[3]  # detected route
            route_stats[route] = route_stats.get(route, 0) + 1
        else:
            unmatched.append(ln)

    # Organize medications by route and PRN status
    meds_by_route_with: Dict[str, Dict[str, float]] = {}
    meds_by_route_without: Dict[str, Dict[str, float]] = {}
    medication_details: List[Dict] = []

    for raw_name, mg_day, is_prn, detected_route in parsed:
        generic = _smart_drug_lookup(raw_name, all_routes_ref)
        
        # Initialize route dictionaries if needed
        if detected_route not in meds_by_route_with:
            meds_by_route_with[detected_route] = {}
            meds_by_route_without[detected_route] = {}
        
        # Add to appropriate dictionaries
        meds_by_route_with[detected_route][generic] = meds_by_route_with[detected_route].get(generic, 0.0) + mg_day
        if not is_prn:
            meds_by_route_without[detected_route][generic] = meds_by_route_without[detected_route].get(generic, 0.0) + mg_day
        
        # Store medication details
        medication_details.append({
            "original_text": f"{raw_name} {mg_day}mg/day",
            "generic_name": generic,
            "dose_mg_day": mg_day,
            "detected_route": detected_route,
            "is_prn": is_prn
        })

    # Calculate DBI for each route
    route_results = {}
    total_dbi_with = 0.0
    total_dbi_without = 0.0
    all_details_with = []
    all_details_without = []

    for route in meds_by_route_with.keys():
        if route in all_routes_ref:
            route_ref = all_routes_ref[route]
            
            # Calculate DBI for this route
            dbi_with, details_with = calculate_dbi(meds_by_route_with[route], route_ref)
            dbi_without, details_without = calculate_dbi(meds_by_route_without[route], route_ref)
            
            total_dbi_with += dbi_with
            total_dbi_without += dbi_without
            
            # Format details
            def _format_details(details, route_name):
                formatted = []
                for g, d, delta, dbi in details:
                    formatted.append({
                        "generic_name": g,
                        "dose_mg_day": d,
                        "delta_mg": delta,
                        "dbi_component": dbi,
                        "route": route_name
                    })
                return formatted
            
            route_details_with = _format_details(details_with, route)
            route_details_without = _format_details(details_without, route)
            
            all_details_with.extend(route_details_with)
            all_details_without.extend(route_details_without)
            
            route_results[route] = {
                "dbi_with_prn": round(dbi_with, 2),
                "dbi_without_prn": round(dbi_without, 2),
                "details_with_prn": route_details_with,
                "details_without_prn": route_details_without,
                "medication_count": route_stats.get(route, 0)
            }

    return {
        "mixed_routes": True,
        "total_dbi_without_prn": round(total_dbi_without, 2),
        "total_dbi_with_prn": round(total_dbi_with, 2),
        "routes_detected": list(route_stats.keys()),
        "route_statistics": route_stats,
        "route_breakdown": route_results,
        "all_details_without_prn": all_details_without,
        "all_details_with_prn": all_details_with,
        "medication_details": medication_details,
        "unmatched_input": unmatched,
    }


def dbi_mcp_with_combinations(text_block: str, *, ref_csv: Union[str, Path] = "dbi_reference_by_route.csv") -> dict:
    """
    Enhanced DBI calculator that handles combination drugs automatically.
    
    This function:
    1. Detects combination drugs (e.g., paracetamol-codeine, co-codamol)
    2. Splits them into individual components
    3. Calculates DBI for each relevant component
    4. Provides detailed breakdown including combination drug handling
    """
    all_routes_ref = load_all_routes_reference(Path(ref_csv))
    
    parsed_combinations: List[ParsedCombination] = []
    unmatched: List[str] = []
    route_stats: Dict[str, int] = {}
    combination_drugs: List[Dict] = []
    
    for ln in text_block.splitlines():
        res = _parse_line_with_combinations(ln)
        if res:
            parsed_combinations.append(res)
            route = res[3]  # detected route
            route_stats[route] = route_stats.get(route, 0) + 1
        else:
            unmatched.append(ln)

    # Organize medications by route and PRN status, handling combinations
    meds_by_route_with: Dict[str, Dict[str, float]] = {}
    meds_by_route_without: Dict[str, Dict[str, float]] = {}
    medication_details: List[Dict] = []

    for name, mg_day, is_prn, detected_route, is_combination, components in parsed_combinations:
        
        if is_combination and components:
            # Handle combination drug by processing each component
            combination_info = {
                "original_text": f"{name} {mg_day}mg/day",
                "is_combination": True,
                "components": [],
                "detected_route": detected_route,
                "is_prn": is_prn
            }
            
            for comp_name, original_text, note in components:
                generic = _smart_drug_lookup(comp_name, all_routes_ref)
                
                # Initialize route dictionaries if needed
                if detected_route not in meds_by_route_with:
                    meds_by_route_with[detected_route] = {}
                    meds_by_route_without[detected_route] = {}
                
                # Add to appropriate dictionaries
                # Note: We use the full dose for each component - this may need refinement
                # based on actual component ratios in the combination
                meds_by_route_with[detected_route][generic] = meds_by_route_with[detected_route].get(generic, 0.0) + mg_day
                if not is_prn:
                    meds_by_route_without[detected_route][generic] = meds_by_route_without[detected_route].get(generic, 0.0) + mg_day
                
                combination_info["components"].append({
                    "component_name": comp_name,
                    "generic_name": generic,
                    "note": note,
                    "dose_mg_day": mg_day  # This is simplified - real combinations need dose splitting
                })
            
            combination_drugs.append(combination_info)
            medication_details.append(combination_info)
            
        else:
            # Handle single drug (or unresolved combination)
            generic = _smart_drug_lookup(name, all_routes_ref)
            
            # Initialize route dictionaries if needed
            if detected_route not in meds_by_route_with:
                meds_by_route_with[detected_route] = {}
                meds_by_route_without[detected_route] = {}
            
            # Add to appropriate dictionaries
            meds_by_route_with[detected_route][generic] = meds_by_route_with[detected_route].get(generic, 0.0) + mg_day
            if not is_prn:
                meds_by_route_without[detected_route][generic] = meds_by_route_without[detected_route].get(generic, 0.0) + mg_day
            
            # Store medication details
            medication_details.append({
                "original_text": f"{name} {mg_day}mg/day",
                "generic_name": generic,
                "dose_mg_day": mg_day,
                "detected_route": detected_route,
                "is_prn": is_prn,
                "is_combination": is_combination,
                "combination_note": "Detected as combination but couldn't split" if is_combination else None
            })

    # Calculate DBI for each route (same as before)
    route_results = {}
    total_dbi_with = 0.0
    total_dbi_without = 0.0
    all_details_with = []
    all_details_without = []

    for route in meds_by_route_with.keys():
        if route in all_routes_ref:
            route_ref = all_routes_ref[route]
            
            # Calculate DBI for this route
            dbi_with, details_with = calculate_dbi(meds_by_route_with[route], route_ref)
            dbi_without, details_without = calculate_dbi(meds_by_route_without[route], route_ref)
            
            total_dbi_with += dbi_with
            total_dbi_without += dbi_without
            
            # Format details
            def _format_details(details, route_name):
                formatted = []
                for g, d, delta, dbi in details:
                    formatted.append({
                        "generic_name": g,
                        "dose_mg_day": d,
                        "delta_mg": delta,
                        "dbi_component": dbi,
                        "route": route_name
                    })
                return formatted
            
            route_details_with = _format_details(details_with, route)
            route_details_without = _format_details(details_without, route)
            
            all_details_with.extend(route_details_with)
            all_details_without.extend(route_details_without)
            
            route_results[route] = {
                "dbi_with_prn": round(dbi_with, 2),
                "dbi_without_prn": round(dbi_without, 2),
                "details_with_prn": route_details_with,
                "details_without_prn": route_details_without,
                "medication_count": route_stats.get(route, 0)
            }

    return {
        "combination_handling": True,
        "total_dbi_without_prn": round(total_dbi_without, 2),
        "total_dbi_with_prn": round(total_dbi_with, 2),
        "routes_detected": list(route_stats.keys()),
        "route_statistics": route_stats,
        "route_breakdown": route_results,
        "all_details_without_prn": all_details_without,
        "all_details_with_prn": all_details_with,
        "medication_details": medication_details,
        "combination_drugs": combination_drugs,
        "unmatched_input": unmatched,
    }


if __name__ == "__main__":
    import sys
    import pprint
    text = sys.stdin.read() if not sys.stdin.isatty() else "\n".join(sys.argv[1:])
    pprint.pp(dbi_mcp(text))