from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
import re
import shutil
from typing import Iterable

TOKEN_RE = re.compile(r"[A-Za-zÀ-ÿ0-9']+")
SENTENCE_RE = re.compile(r'(?<=[.!?])\s+')

MOOD_TERMS = {
    'tired': 'mood:tired',
    'sad': 'mood:sad',
    'anxious': 'mood:anxious',
    'worried': 'mood:worried',
    'calm': 'mood:calm',
    'better': 'mood:improving',
}
SYMPTOM_TERMS = {
    'pain': 'symptoms:pain',
    'dizzy': 'symptoms:dizziness',
    'dizziness': 'symptoms:dizziness',
    'cough': 'symptoms:cough',
    'fever': 'symptoms:fever',
    'nausea': 'symptoms:nausea',
    'appetite': 'symptoms:low_appetite',
    'breath': 'symptoms:shortness_of_breath',
}
ACTIVITY_TERMS = {
    'walk': 'activity:walking',
    'walking': 'activity:walking',
    'rest': 'activity:resting',
    'sleep': 'activity:sleep',
    'slept': 'activity:sleep',
    'visit': 'activity:visit',
    'appointment': 'activity:appointment',
}
MEDICATION_TERMS = {
    'medication': 'meds:medication',
    'meds': 'meds:medication',
    'dose': 'meds:dose_change',
    'pill': 'meds:pill',
    'refill': 'meds:refill',
}
RISK_TERMS = {
    'hurt',
    'harm',
    'abuse',
    'suicide',
    'kill',
    'overdose',
    'emergency',
}


@dataclass(frozen=True)
class JournalSummary:
    transcript: str
    family_view: str
    clinician_view: str
    tags: list[str]
    segment_confidences: list[dict[str, object]]
    safety_tag: str
    questions_for_doctor: list[str]


def tokenize(text: str) -> list[str]:
    return [token.lower() for token in TOKEN_RE.findall(text or '')]


def extract_tags(text: str) -> list[str]:
    tokens = tokenize(text)
    tags: list[str] = []
    for token in tokens:
        for mapping in (MOOD_TERMS, SYMPTOM_TERMS, ACTIVITY_TERMS, MEDICATION_TERMS):
            if token in mapping and mapping[token] not in tags:
                tags.append(mapping[token])
    if not tags:
        tags.append('care:general')
    return tags


def safety_label(text: str) -> str:
    lowered = (text or '').lower()
    return 'needs review' if any(term in lowered for term in RISK_TERMS) else 'ok'


def _shorten(text: str, limit: int = 160) -> str:
    text = ' '.join((text or '').split())
    return text if len(text) <= limit else text[: limit - 1].rstrip() + '…'


def family_summary(text: str) -> str:
    sentences = [sentence.strip() for sentence in SENTENCE_RE.split((text or '').strip()) if sentence.strip()]
    if not sentences:
        return 'No transcript provided.'
    first = _shorten(sentences[0], 170)
    tags = extract_tags(text)
    return f'Family update: {first}. Tags: {", ".join(tags[:4])}.'


def clinician_summary(text: str) -> str:
    tags = extract_tags(text)
    first = _shorten((text or '').split('\n', 1)[0], 140)
    return f'Clinician note: {first}. Relevant tags: {", ".join(tags[:4])}.'


def segment_confidences(text: str) -> list[dict[str, object]]:
    sentences = [sentence.strip() for sentence in SENTENCE_RE.split((text or '').strip()) if sentence.strip()]
    if not sentences:
        sentences = [text.strip()] if text and text.strip() else []
    if not sentences:
        return []
    confidences = []
    for idx, sentence in enumerate(sentences, start=1):
        token_count = max(1, len(tokenize(sentence)))
        conf = min(0.99, 0.58 + min(token_count, 18) / 40)
        confidences.append({
            'segment': idx,
            'text': _shorten(sentence, 120),
            'confidence': round(conf, 2),
        })
    return confidences


def doctor_questions(tags: Iterable[str]) -> list[str]:
    tag_set = list(tags)
    questions: list[str] = []
    if any(tag.startswith('symptoms:') for tag in tag_set):
        questions.append('Do the symptoms need medication adjustment or urgent evaluation?')
    if any(tag.startswith('meds:') for tag in tag_set):
        questions.append('Was there a missed dose, refill issue, or side effect?')
    if any(tag.startswith('activity:') for tag in tag_set):
        questions.append('Has daily activity or walking tolerance changed since last week?')
    if any(tag.startswith('mood:') for tag in tag_set):
        questions.append('Is the mood change persistent or linked to sleep and pain?')
    if not questions:
        questions.append('Is there anything new that needs a clinician follow-up?')
    return questions[:3]


def summarize_entry(text: str) -> JournalSummary:
    tags = extract_tags(text)
    return JournalSummary(
        transcript=text,
        family_view=family_summary(text),
        clinician_view=clinician_summary(text),
        tags=tags,
        segment_confidences=segment_confidences(text),
        safety_tag=safety_label(text),
        questions_for_doctor=doctor_questions(tags),
    )


def normalize_audio_file(source_path: str | Path, output_dir: str | Path, *, stem: str | None = None) -> Path:
    source = Path(source_path)
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    target = output_dir / f'{stem or source.stem}_16k_mono.wav'
    shutil.copy2(source, target)
    return target


def digest_entries(entries: list[dict[str, object]], *, start_label: str = '', end_label: str = '') -> dict[str, object]:
    if not entries:
        return {
            'range': {'start': start_label, 'end': end_label},
            'summary': 'No entries found for this date range.',
            'key_events': [],
            'questions_for_doctor': ['No entries found; record at least one diary clip.'],
        }

    tags: list[str] = []
    events: list[str] = []
    for entry in entries:
        entry_tags = list(entry.get('tags', []))
        for tag in entry_tags:
            if tag not in tags:
                tags.append(tag)
        summary = str(entry.get('family_summary') or entry.get('clinician_summary') or entry.get('transcript') or '')
        if summary:
            events.append(_shorten(summary, 100))

    clinic_questions = doctor_questions(tags)
    digest_summary = f"{len(entries)} entries reviewed. Notable themes: {', '.join(tags[:5])}."
    return {
        'range': {'start': start_label, 'end': end_label},
        'summary': digest_summary,
        'key_events': events[:5],
        'questions_for_doctor': clinic_questions,
    }