import json
import logging

import litellm

from app.config import settings

logger = logging.getLogger(__name__)


def _proxy_model(model_id: str) -> str:
    """Prefix model with openai/ if using a LiteLLM proxy and no provider is specified."""
    if settings.LITELLM_API_BASE and "/" not in model_id:
        return f"openai/{model_id}"
    return model_id

EXTRACTION_PROMPT = """You are extracting questions from a PREP (Pediatric Review and Education Program) exam PDF.

These PDFs follow a strict format:
1. A numbered question with a clinical vignette (patient scenario)
2. Five answer options labeled A, B, C, D, E
3. A line "Correct Answer: X" or "Preferred Response: X" where X is the letter of the correct option
4. An explanation paragraph
5. A "Critique:" section with detailed reasoning
6. A "Content Specifications:" section listing the learning objectives

Your task: extract every question and return ONLY a JSON object in this exact format:

{{"questions": [
  {{
    "item_number": "<item number digits only, e.g. '193' — null if not found>",
    "question_text": "<full question stem including any patient vignette>",
    "question_type": "mcq",
    "options": ["<option A text>", "<option B text>", "<option C text>", "<option D text>", "<option E text>"],
    "correct_answer": "<full text of the correct option, NOT the letter>",
    "explanation": "<ALL text verbatim from after the correct answer line to before the next question — explanation, Critique, Learning Points, Content Specifications, Suggested Reading, everything>",
    "has_figure": false,
    "page_reference": {page_ref}
  }}
]}}

CRITICAL RULES — follow exactly:
1. CORRECT ANSWER: Find "Correct Answer: X" or "Preferred Response: X" after each question.
   X is a letter (A–E). Look up the full text of that option and store it as correct_answer.
   NEVER store just the letter. NEVER guess. If not found, set correct_answer to null.
   Example: options=["alpha","beta","gamma","delta","epsilon"] and "Correct Answer: C" → correct_answer="gamma"

2. EXPLANATION: Copy EVERYTHING that appears after the correct answer line and before the next question,
   verbatim and in full — including the explanation paragraph, any Critique section, Learning Points,
   Content Specifications, American Board of Pediatrics specifications, Suggested Readings, and any
   other content. Do NOT summarize, shorten, skip, or paraphrase a single word.
   Keep all section headers (e.g. "Critique:", "Content Specifications:", "Suggested Reading:").
   The explanation field should be the complete, unaltered text block.

3. QUESTIONS: Extract ALL questions, even if partially cut off.
   A new question starts with "Item NNN" or "ltem NNN" (OCR artifact — lowercase l instead of I).
   Content after an image reference (e.g. "Item C123A", figure captions) is NOT a new question.

4. OPTIONS: Extract just the text for each option, without the letter prefix (A. B. C. D. E.).

5. HAS_FIGURE: Set to true ONLY if the question text explicitly references an image, figure, photograph,
   radiograph, X-ray, ECG, rash photo, growth chart, or similar visual that is essential to answering.
   Do NOT set true for decorative logos, page headers, publisher icons, or repeated branding images.
   If the question can be answered from text alone, set false.

6. Return ONLY the JSON — no markdown fences, no explanation, no preamble.

Content from page(s) {page_info}:
{content}"""

ANSWER_KEY_PROMPT = """Extract the answer key from this PREP exam content.

The answer key lists items with their correct answer letters, like:
  "Item 193  Preferred Response: D"
  "Item 194  Preferred Response: A"

Return ONLY a JSON object mapping item numbers to correct letters:
{{"answers": {{"193": "D", "194": "A", "211": "C"}}}}

Rules:
- "Preferred Response: X" or "Correct Answer: X" — X is the letter.
- Item numbers may appear as "ltemXXX" (OCR artifact — l is actually I).
- Only include items where you find a clear correct answer letter.
- Return ONLY the JSON — no markdown, no preamble.

Content from page(s) {page_info}:
{content}"""


def get_model_for_task(db, task: str = "extraction") -> tuple[str, str | None]:
    """Get the configured model for a specific task from DB, or fall back to settings."""
    try:
        from app.models.ai_model_config import AIModelConfig
        config = db.query(AIModelConfig).filter(
            AIModelConfig.task == task,
            AIModelConfig.is_active == True,
            AIModelConfig.is_default == True,
        ).first()
        if config:
            return config.model_id, config.api_key
    except Exception as e:
        logger.warning(f"Failed to load AI model config for task '{task}', using fallback: {e}")
    return settings.LITELLM_MODEL, settings.LITELLM_API_KEY or None


def _truncate_content(content: str, max_chars: int = 100000) -> str:
    if len(content) <= max_chars:
        return content
    half = max_chars // 2
    return content[:half] + "\n\n... [content truncated] ...\n\n" + content[-half:]


def extract_questions(
    content: str,
    page_info: str = "unknown",
    page_ref: int | None = None,
    model_id: str | None = None,
    api_key: str | None = None,
) -> list[dict]:
    """Extract quiz questions from PDF content using LiteLLM."""
    content = _truncate_content(content)

    prompt = EXTRACTION_PROMPT.format(
        content=content,
        page_info=page_info,
        page_ref=page_ref if page_ref else "null",
    )

    use_model = _proxy_model(model_id or settings.LITELLM_MODEL)
    use_key = api_key or settings.LITELLM_API_KEY

    last_error = None
    for attempt in range(3):
        try:
            kwargs = {
                "model": use_model,
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.1,  # low temp for faithful extraction
            }
            if use_key:
                kwargs["api_key"] = use_key
            if settings.LITELLM_API_BASE:
                kwargs["api_base"] = settings.LITELLM_API_BASE

            # Don't force JSON mode — let the model respond naturally and we parse it
            response = litellm.completion(**kwargs)

            response_text = response.choices[0].message.content
            logger.info(f"Model raw response (first 500 chars): {response_text[:500]!r}")

            # Try to parse JSON, handle markdown code blocks
            text = response_text.strip()
            if text.startswith("```"):
                text = text.split("\n", 1)[1] if "\n" in text else text[3:]
                if text.endswith("```"):
                    text = text[:-3]
                text = text.strip()

            data = json.loads(text)

            # Handle all common response shapes
            if isinstance(data, list):
                questions = data
            elif isinstance(data, dict):
                # Try common keys
                for key in ("questions", "items", "results", "data"):
                    if isinstance(data.get(key), list):
                        questions = data[key]
                        break
                else:
                    # Maybe the dict itself is a single question
                    if "question_text" in data:
                        questions = [data]
                    else:
                        raise ValueError(f"Unexpected response shape: {list(data.keys())}")
            else:
                raise ValueError("Response is not a list of questions")

            validated = []
            skipped = []
            for q in questions:
                if "question_text" not in q:
                    continue
                correct = q.get("correct_answer")
                if not correct:
                    skipped.append(q.get("question_text", "")[:120])
                    continue
                qtype = q.get("question_type", "mcq")
                if qtype not in ("mcq", "true_false", "fill_blank"):
                    qtype = "mcq"
                validated.append({
                    "question_text": q["question_text"],
                    "question_type": qtype,
                    "options": q.get("options"),
                    "correct_answer": correct,
                    "explanation": q.get("explanation", ""),
                    "page_reference": q.get("page_reference"),
                    "skipped": [],
                })

            # Attach skipped list to first question so caller can surface it
            if validated and skipped:
                validated[0]["skipped"] = skipped

            if validated:
                return validated

            raise ValueError("No valid questions extracted from content")

        except Exception as e:
            last_error = e
            logger.warning(f"Extraction attempt {attempt + 1} failed: {e!r}")

    raise RuntimeError(f"Failed to extract questions after 3 attempts: {last_error}")


def _call_model(prompt: str, model_id: str | None, api_key: str | None) -> str:
    """Call the configured LLM and return raw text response."""
    use_model = _proxy_model(model_id or settings.LITELLM_MODEL)
    use_key = api_key or settings.LITELLM_API_KEY
    kwargs = {
        "model": use_model,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.1,
    }
    if use_key:
        kwargs["api_key"] = use_key
    if settings.LITELLM_API_BASE:
        kwargs["api_base"] = settings.LITELLM_API_BASE
    response = litellm.completion(**kwargs)
    return response.choices[0].message.content


def extract_questions_no_answers(
    content: str,
    page_info: str = "unknown",
    page_ref: int | None = None,
    model_id: str | None = None,
    api_key: str | None = None,
) -> list[dict]:
    """Extract questions allowing null correct_answer — for PDFs where answers are at the end."""
    content = _truncate_content(content)
    prompt = EXTRACTION_PROMPT.format(
        content=content,
        page_info=page_info,
        page_ref=page_ref if page_ref else "null",
    )
    last_error = None
    for attempt in range(3):
        try:
            text = _call_model(prompt, model_id, api_key)
            text = text.strip()
            if text.startswith("```"):
                text = text.split("\n", 1)[1] if "\n" in text else text[3:]
                if text.endswith("```"):
                    text = text[:-3]
                text = text.strip()
            data = json.loads(text)
            questions = []
            if isinstance(data, list):
                questions = data
            elif isinstance(data, dict):
                for key in ("questions", "items", "results", "data"):
                    if isinstance(data.get(key), list):
                        questions = data[key]
                        break
                else:
                    if "question_text" in data:
                        questions = [data]

            result = []
            for q in questions:
                if "question_text" not in q:
                    continue
                qtype = q.get("question_type", "mcq")
                if qtype not in ("mcq", "true_false", "fill_blank"):
                    qtype = "mcq"
                result.append({
                    "item_number": str(q.get("item_number") or "").strip().lstrip("0") or None,
                    "question_text": q["question_text"],
                    "question_type": qtype,
                    "options": q.get("options"),
                    "correct_answer": q.get("correct_answer"),  # may be null
                    "explanation": q.get("explanation", ""),
                    "page_reference": q.get("page_reference"),
                })
            if result:
                return result
            raise ValueError("No questions found in content")
        except Exception as e:
            last_error = e
            logger.warning(f"No-answer extraction attempt {attempt + 1} failed: {e!r}")
    raise RuntimeError(f"Failed after 3 attempts: {last_error}")


def extract_answer_key(
    content: str,
    page_info: str = "unknown",
    model_id: str | None = None,
    api_key: str | None = None,
) -> dict[str, str]:
    """Extract answer key from end-of-document content. Returns {item_number: letter}."""
    content = _truncate_content(content, max_chars=80000)
    prompt = ANSWER_KEY_PROMPT.format(content=content, page_info=page_info)
    last_error = None
    for attempt in range(3):
        try:
            text = _call_model(prompt, model_id, api_key)
            text = text.strip()
            if text.startswith("```"):
                text = text.split("\n", 1)[1] if "\n" in text else text[3:]
                if text.endswith("```"):
                    text = text[:-3]
                text = text.strip()
            data = json.loads(text)
            answers = data.get("answers", data) if isinstance(data, dict) else {}
            # Normalize: strip leading zeros, uppercase letters
            return {str(k).strip().lstrip("0"): str(v).strip().upper() for k, v in answers.items() if v}
        except Exception as e:
            last_error = e
            logger.warning(f"Answer key extraction attempt {attempt + 1} failed: {e!r}")
    logger.warning(f"Answer key extraction failed: {last_error}")
    return {}


def generate_tts_audio(
    text: str,
    model_id: str | None = None,
    api_key: str | None = None,
) -> bytes | None:
    """Generate TTS audio. Supports OpenAI, ElevenLabs, Google Cloud TTS, and AWS Polly.

    model_id conventions:
      tts-1:alloy          → OpenAI TTS (voice after colon)
      tts-1-hd:nova        → OpenAI TTS HD
      elevenlabs/<voice>   → ElevenLabs
      google/<voice_name>  → Google Cloud TTS (e.g. google/en-US-Wavenet-D)
      polly/<VoiceId>      → AWS Polly Neural (e.g. polly/Joanna)
    """
    import httpx, base64

    use_model = model_id or "tts-1:alloy"

    # ── ElevenLabs ─────────────────────────────────────────────
    if use_model.startswith("elevenlabs/") or use_model.startswith("eleven_labs/"):
        voice = use_model.split("/", 1)[1]
        key = api_key or settings.ELEVENLABS_API_KEY
        if not key:
            logger.error("ElevenLabs API key not configured")
            return None
        try:
            resp = httpx.post(
                f"https://api.elevenlabs.io/v1/text-to-speech/{voice}",
                headers={"xi-api-key": key, "Content-Type": "application/json"},
                json={"text": text, "model_id": "eleven_turbo_v2_5"},
                timeout=30,
            )
            resp.raise_for_status()
            return resp.content
        except Exception as e:
            logger.error(f"ElevenLabs TTS failed: {e}")
            return None

    # ── Google Cloud TTS ────────────────────────────────────────
    if use_model.startswith("google/"):
        voice_name = use_model[len("google/"):]
        key = api_key or settings.GOOGLE_TTS_API_KEY
        if not key:
            logger.error("Google TTS API key not configured (GOOGLE_TTS_API_KEY)")
            return None
        # Parse language code from voice name (e.g. "en-US-Wavenet-D" → "en-US")
        parts = voice_name.split("-")
        lang_code = f"{parts[0]}-{parts[1]}" if len(parts) >= 2 else "en-US"
        try:
            resp = httpx.post(
                f"https://texttospeech.googleapis.com/v1/text:synthesize?key={key}",
                json={
                    "input": {"text": text},
                    "voice": {"languageCode": lang_code, "name": voice_name},
                    "audioConfig": {"audioEncoding": "MP3"},
                },
                timeout=30,
            )
            resp.raise_for_status()
            return base64.b64decode(resp.json()["audioContent"])
        except Exception as e:
            logger.error(f"Google TTS failed: {e}")
            return None

    # ── AWS Polly ───────────────────────────────────────────────
    if use_model.startswith("polly/"):
        voice_id = use_model[len("polly/"):]
        access_key = api_key or settings.AWS_ACCESS_KEY_ID
        secret_key = settings.AWS_SECRET_ACCESS_KEY
        region = settings.AWS_REGION or "us-east-1"
        if not access_key or not secret_key:
            logger.error("AWS credentials not configured (AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY)")
            return None
        try:
            import boto3
            polly = boto3.client(
                "polly",
                aws_access_key_id=access_key,
                aws_secret_access_key=secret_key,
                region_name=region,
            )
            response = polly.synthesize_speech(
                Text=text,
                OutputFormat="mp3",
                VoiceId=voice_id,
                Engine="neural",
            )
            return response["AudioStream"].read()
        except Exception as e:
            logger.error(f"AWS Polly TTS failed: {e}")
            return None

    # ── OpenAI (default) ────────────────────────────────────────
    # model_id may encode voice as "tts-1:nova", "tts-1-hd:alloy", etc.
    clean_model = use_model.replace("openai/", "")
    oai_voice = "alloy"
    if ":" in clean_model:
        clean_model, oai_voice = clean_model.split(":", 1)

    # Per-model key > OPENAI_API_KEY (direct) > LITELLM_API_KEY (proxy)
    if api_key:
        key = api_key
        base = (settings.LITELLM_API_BASE or "https://api.openai.com").rstrip("/")
    elif settings.OPENAI_API_KEY:
        key = settings.OPENAI_API_KEY
        base = "https://api.openai.com"
    else:
        key = settings.LITELLM_API_KEY
        base = (settings.LITELLM_API_BASE or "https://api.openai.com").rstrip("/")

    try:
        resp = httpx.post(
            f"{base}/v1/audio/speech",
            headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
            json={"model": clean_model, "input": text, "voice": oai_voice},
            timeout=60,
        )
        resp.raise_for_status()
        return resp.content
    except Exception as e:
        logger.error(f"OpenAI TTS failed: {e}")
        return None