"""
MedScribe AI -- Unified Inference Client.

Multi-backend inference abstraction (4-tier):
  Tier 0: Local vLLM / Ollama  -- LOCAL_VLLM_URL env var (air-gapped hospitals)
  Tier 1: HF Serverless Inference API -- HAI-DEF models via huggingface_hub.
  Tier 2: GenAI SDK -- compatible Gemma models via google-genai client.
  Tier 3: Demo mode -- deterministic clinical extraction (no API calls).

Environment variables:
  LOCAL_VLLM_URL  -- Local vLLM/Ollama base URL, e.g. http://localhost:8000 (Tier 0)
                     Example with Ollama: http://localhost:11434
                     Model name passed as-is; for Ollama use "medgemma:4b" or similar.
  HF_TOKEN        -- Hugging Face token for HAI-DEF model access (Tier 1)
                     Get yours at: https://huggingface.co/settings/tokens
                     Must have read access to google/medgemma-4b-it (gated model).
  GOOGLE_API_KEY  -- GenAI SDK key (Tier 2, optional)
                     Get yours at: https://aistudio.google.com/app/apikey
  GEMINI_API_KEY  -- Alias for GOOGLE_API_KEY (Tier 2, optional)

The InferenceClient abstraction ensures agents are fully agnostic to
the serving backend. Adding a new backend (Vertex AI, Ollama, vLLM)
requires implementing a single adapter function -- zero agent code changes.

Air-gapped deployment: Set LOCAL_VLLM_URL to point to an on-premise
vLLM server or Ollama instance. PHI never leaves the hospital network.

Public API (import these -- do not call private functions directly):
  generate_text(prompt, model_id, system_prompt, max_new_tokens) -> str
  analyze_image_text(image_bytes, prompt, model_id, ...) -> str
  classify_image(image_bytes, candidate_labels, model_id) -> list[dict]
  transcribe_audio(audio_bytes, model_id) -> str
  get_inference_backend() -> str  # Returns active tier name
  pil_to_bytes(image, format) -> bytes  # PIL Image helper
"""

from __future__ import annotations

__all__ = [
    "generate_text",
    "analyze_image_text",
    "classify_image",
    "transcribe_audio",
    "get_inference_backend",
    "pil_to_bytes",
]

import base64
import io
import logging
import os
from typing import Any

log = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Keys
# ---------------------------------------------------------------------------

def _get_local_vllm_url() -> str | None:
    """Return local vLLM/Ollama base URL (Tier 0 — air-gapped deployment)."""
    return os.environ.get("LOCAL_VLLM_URL") or None


def _get_genai_key() -> str | None:
    return os.environ.get("GOOGLE_API_KEY") or os.environ.get("GEMINI_API_KEY") or None


def _get_hf_token() -> str | None:
    return os.environ.get("HF_TOKEN") or None


# ---------------------------------------------------------------------------
# Backend detection
# ---------------------------------------------------------------------------

def get_inference_backend() -> str:
    """Return the active inference backend name."""
    if _get_local_vllm_url():
        return "local_vllm"
    if _get_hf_token():
        return "hf_inference_api"
    if _get_genai_key():
        return "genai_sdk"
    return "demo_fallback"


# ---------------------------------------------------------------------------
# Text generation
# ---------------------------------------------------------------------------

def generate_text(
    prompt: str,
    model_id: str = "google/medgemma-4b-it",
    system_prompt: str | None = None,
    max_new_tokens: int = 2048,
) -> str:
    """
    Generate text from a medical prompt.

    Tries Tier 0 (local vLLM) → Tier 1 (HF API) → Tier 2 (GenAI) → raises.
    """
    # --- Tier 0: Local vLLM / Ollama (air-gapped deployment) ---
    local_url = _get_local_vllm_url()
    if local_url:
        try:
            return _local_generate_text(prompt, model_id, system_prompt, max_new_tokens, local_url)
        except Exception as exc:
            log.warning(f"[LocalVLLM] text generation failed: {exc} -- trying HF API")

    # --- Tier 1: HF Inference API ---
    hf_token = _get_hf_token()
    if hf_token:
        try:
            return _hf_generate_text(prompt, model_id, system_prompt, max_new_tokens, hf_token)
        except Exception as exc:
            log.warning(
                f"[HF] text generation failed for {model_id}: {exc} -- trying GenAI fallback"
            )

    # --- Tier 2: GenAI SDK ---
    genai_key = _get_genai_key()
    if genai_key:
        try:
            return _genai_generate_text(prompt, system_prompt, max_new_tokens, genai_key)
        except Exception as exc:
            log.warning(f"[GenAI] text generation failed: {exc}")

    raise RuntimeError(
        "No inference backend available. "
        "To fix: set one of the following environment variables:\n"
        "  • LOCAL_VLLM_URL=http://localhost:8000  (Tier 0 — local vLLM/Ollama)\n"
        "  • HF_TOKEN=hf_...  (Tier 1 — HF Inference API, get at hf.co/settings/tokens)\n"
        "  • GOOGLE_API_KEY=AIza...  (Tier 2 — Google GenAI SDK)\n"
        "Or run in demo mode (no env vars needed) — demo mode always works."
    )


def _local_generate_text(
    prompt: str,
    model_id: str,
    system_prompt: str | None,
    max_new_tokens: int,
    base_url: str,
) -> str:
    """Call local vLLM/Ollama OpenAI-compatible API (Tier 0)."""
    import json as _json
    import urllib.request

    # Strip trailing slash
    base_url = base_url.rstrip("/")

    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    messages.append({"role": "user", "content": prompt})

    payload = _json.dumps({
        "model": model_id,
        "messages": messages,
        "max_tokens": max_new_tokens,
        "temperature": 0.1,
    }).encode("utf-8")

    req = urllib.request.Request(
        f"{base_url}/v1/chat/completions",
        data=payload,
        headers={"Content-Type": "application/json"},
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=60) as resp:
        data = _json.loads(resp.read().decode("utf-8"))

    result = data["choices"][0]["message"]["content"]
    log.info(f"[LocalVLLM] {model_id} generated {len(result)} chars (offline)")
    return result


def _genai_generate_text(
    prompt: str,
    system_prompt: str | None,
    max_new_tokens: int,
    api_key: str,
) -> str:
    """Call GenAI SDK with Gemma model."""
    from google import genai

    client = genai.Client(api_key=api_key)

    config = genai.types.GenerateContentConfig(
        system_instruction=system_prompt or "You are an expert clinical documentation specialist.",
        max_output_tokens=max_new_tokens,
        temperature=0.1,
    )

    response = client.models.generate_content(
        model="gemma-3-4b-it",
        contents=prompt,
        config=config,
    )
    result = response.text
    log.info(f"[GenAI] generated {len(result)} chars")
    return result


def _hf_generate_text(
    prompt: str,
    model_id: str,
    system_prompt: str | None,
    max_new_tokens: int,
    token: str,
) -> str:
    """Call HF Serverless Inference API."""
    from huggingface_hub import InferenceClient

    client = InferenceClient(token=token)
    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    messages.append({"role": "user", "content": prompt})

    response = client.chat_completion(
        model=model_id,
        messages=messages,
        max_tokens=max_new_tokens,
        temperature=0.1,
    )
    result = response.choices[0].message.content
    log.info(f"[HF] {model_id} generated {len(result)} chars")
    return result


# ---------------------------------------------------------------------------
# Image + Text (multimodal)
# ---------------------------------------------------------------------------

def analyze_image_text(
    image_bytes: bytes,
    prompt: str,
    model_id: str = "google/medgemma-4b-it",
    system_prompt: str | None = None,
    max_new_tokens: int = 1024,
) -> str:
    """
    Analyse a medical image with a text prompt.

    Tries Tier 0 (local vLLM) → Tier 1 (HF API) → Tier 2 (GenAI) → raises.
    """
    # --- Tier 0: Local vLLM with base64 image ---
    local_url = _get_local_vllm_url()
    if local_url:
        try:
            return _local_analyze_image(
                image_bytes, prompt, model_id, system_prompt, max_new_tokens, local_url
            )
        except Exception as exc:
            log.warning(f"[LocalVLLM] image analysis failed: {exc}")

    # --- Tier 1: HF Inference API ---
    hf_token = _get_hf_token()
    if hf_token:
        try:
            return _hf_analyze_image(
                image_bytes, prompt, model_id, system_prompt, max_new_tokens, hf_token
            )
        except Exception as exc:
            log.warning(f"[HF] image analysis failed for {model_id}: {exc}")

    # --- Tier 2: GenAI SDK ---
    genai_key = _get_genai_key()
    if genai_key:
        try:
            return _genai_analyze_image(
                image_bytes, prompt, system_prompt, max_new_tokens, genai_key
            )
        except Exception as exc:
            log.warning(f"[GenAI] image analysis failed: {exc}")

    raise RuntimeError(
        "No inference backend available for image analysis. "
        "Set LOCAL_VLLM_URL, HF_TOKEN, or GOOGLE_API_KEY — see module docstring."
    )


def _local_analyze_image(
    image_bytes: bytes,
    prompt: str,
    model_id: str,
    system_prompt: str | None,
    max_new_tokens: int,
    base_url: str,
) -> str:
    """Call local vLLM multimodal endpoint with base64-encoded image (Tier 0)."""
    import json as _json
    import urllib.request

    base_url = base_url.rstrip("/")
    b64 = base64.b64encode(image_bytes).decode("utf-8")
    image_url = f"data:image/jpeg;base64,{b64}"

    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    messages.append({
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": image_url}},
            {"type": "text", "text": prompt},
        ],
    })

    payload = _json.dumps({
        "model": model_id,
        "messages": messages,
        "max_tokens": max_new_tokens,
        "temperature": 0.1,
    }).encode("utf-8")

    req = urllib.request.Request(
        f"{base_url}/v1/chat/completions",
        data=payload,
        headers={"Content-Type": "application/json"},
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=60) as resp:
        data = _json.loads(resp.read().decode("utf-8"))

    result = data["choices"][0]["message"]["content"]
    log.info(f"[LocalVLLM] {model_id} image analysis: {len(result)} chars (offline)")
    return result


def _genai_analyze_image(
    image_bytes: bytes,
    prompt: str,
    system_prompt: str | None,
    max_new_tokens: int,
    api_key: str,
) -> str:
    """Call GenAI SDK with image + text (multimodal)."""
    from google import genai
    from google.genai import types as gtypes

    client = genai.Client(api_key=api_key)

    # Build content parts
    image_part = gtypes.Part.from_bytes(data=image_bytes, mime_type="image/jpeg")
    text_part = gtypes.Part.from_text(text=prompt)

    config = gtypes.GenerateContentConfig(
        system_instruction=system_prompt or "You are an expert medical image analyst.",
        max_output_tokens=max_new_tokens,
        temperature=0.1,
    )

    response = client.models.generate_content(
        model="gemma-3-4b-it",
        contents=[image_part, text_part],
        config=config,
    )
    result = response.text
    log.info(f"[GenAI] image analysis: {len(result)} chars")
    return result


def _hf_analyze_image(
    image_bytes: bytes,
    prompt: str,
    model_id: str,
    system_prompt: str | None,
    max_new_tokens: int,
    token: str,
) -> str:
    """Call HF Inference API with image + text."""
    from huggingface_hub import InferenceClient

    client = InferenceClient(token=token)
    b64 = base64.b64encode(image_bytes).decode("utf-8")
    image_url = f"data:image/jpeg;base64,{b64}"

    messages: list[dict] = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    messages.append({
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": image_url}},
            {"type": "text", "text": prompt},
        ],
    })

    response = client.chat_completion(
        model=model_id,
        messages=messages,
        max_tokens=max_new_tokens,
        temperature=0.1,
    )
    result = response.choices[0].message.content
    log.info(f"[HF] {model_id} image analysis: {len(result)} chars")
    return result


# ---------------------------------------------------------------------------
# Image Classification (MedSigLIP zero-shot)
# ---------------------------------------------------------------------------

def classify_image(
    image_bytes: bytes,
    candidate_labels: list[str],
    model_id: str = "google/medsiglip-448",
) -> list[dict]:
    """
    Run zero-shot image classification.

    Tries Tier 0 (local vLLM) → Tier 1 (HF MedSigLIP) → Tier 2 (GenAI) → raises.
    """
    # --- Tier 0: Local vLLM structured prompting ---
    local_url = _get_local_vllm_url()
    if local_url:
        try:
            return _local_classify_image(image_bytes, candidate_labels, local_url)
        except Exception as exc:
            log.warning(f"[LocalVLLM] image classification failed: {exc}")

    # --- Tier 1: HF Inference API ---
    hf_token = _get_hf_token()
    if hf_token:
        try:
            from huggingface_hub import InferenceClient
            client = InferenceClient(token=hf_token)
            result = client.zero_shot_image_classification(
                image=image_bytes,
                candidate_labels=candidate_labels,
                model=model_id,
            )
            return [{"label": r.label, "score": r.score} for r in result]
        except Exception as exc:
            log.warning(f"[HF] zero-shot classification failed: {exc}")

    # --- Tier 2: GenAI SDK ---
    genai_key = _get_genai_key()
    if genai_key:
        try:
            return _genai_classify_image(image_bytes, candidate_labels, genai_key)
        except Exception as exc:
            log.warning(f"[GenAI] image classification failed: {exc}")

    raise RuntimeError(
        "No inference backend available for image classification. "
        "Set LOCAL_VLLM_URL, HF_TOKEN, or GOOGLE_API_KEY — see module docstring."
    )


def _local_classify_image(
    image_bytes: bytes,
    candidate_labels: list[str],
    base_url: str,
) -> list[dict]:
    """Zero-shot classify via local vLLM (Tier 0) using structured JSON prompt."""
    import json as _json
    import urllib.request

    base_url = base_url.rstrip("/")
    b64 = base64.b64encode(image_bytes).decode("utf-8")
    image_url = f"data:image/jpeg;base64,{b64}"
    labels_str = ", ".join(candidate_labels)

    prompt = (
        f"Classify this medical image into exactly ONE of these categories: [{labels_str}]. "
        f'Respond with ONLY JSON: {{"label": "<chosen_category>", "confidence": <0.0-1.0>}}'
    )

    payload = _json.dumps({
        "model": "medgemma-4b-it",
        "messages": [{
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": image_url}},
                {"type": "text", "text": prompt},
            ],
        }],
        "max_tokens": 64,
        "temperature": 0.0,
    }).encode("utf-8")

    req = urllib.request.Request(
        f"{base_url}/v1/chat/completions",
        data=payload,
        headers={"Content-Type": "application/json"},
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=30) as resp:
        data = _json.loads(resp.read().decode("utf-8"))

    text = data["choices"][0]["message"]["content"].strip()
    if "```" in text:
        text = text.split("```")[1].lstrip("json").strip()
    try:
        parsed = _json.loads(text)
        chosen = parsed.get("label", candidate_labels[0])
        confidence = float(parsed.get("confidence", 0.75))
    except (ValueError, KeyError):
        chosen = candidate_labels[0]
        confidence = 0.6

    remaining = (1.0 - confidence) / max(len(candidate_labels) - 1, 1)
    results = [
        {"label": lbl, "score": confidence if lbl == chosen else round(remaining, 3)}
        for lbl in candidate_labels
    ]
    results.sort(key=lambda x: x["score"], reverse=True)
    log.info(f"[LocalVLLM] classified as '{chosen}' ({confidence:.2f}) (offline)")
    return results


def _genai_classify_image(
    image_bytes: bytes,
    candidate_labels: list[str],
    api_key: str,
) -> list[dict]:
    """Simulate zero-shot classification using GenAI SDK multimodal."""
    import json

    from google import genai
    from google.genai import types as gtypes

    client = genai.Client(api_key=api_key)

    labels_str = ", ".join(candidate_labels)
    prompt = (
        f"Classify this medical image into exactly ONE of these categories: [{labels_str}]. "
        f"Respond with ONLY a JSON object in this format: "
        f'{{"label": "<chosen_category>", "confidence": <0.0-1.0>}}. '
        f"No other text."
    )

    image_part = gtypes.Part.from_bytes(data=image_bytes, mime_type="image/jpeg")
    text_part = gtypes.Part.from_text(text=prompt)

    config = gtypes.GenerateContentConfig(
        system_instruction="You are a medical image classification system. Respond with JSON only.",
        max_output_tokens=128,
        temperature=0.0,
    )

    response = client.models.generate_content(
        model="gemma-3-4b-it",
        contents=[image_part, text_part],
        config=config,
    )

    # Parse the JSON response
    text = response.text.strip()
    # Try to extract JSON from potential markdown wrapping
    if "```" in text:
        text = text.split("```")[1]
        if text.startswith("json"):
            text = text[4:]
        text = text.strip()

    try:
        data = json.loads(text)
        chosen_label = data.get("label", candidate_labels[0])
        confidence = float(data.get("confidence", 0.5))
    except (json.JSONDecodeError, ValueError):
        # Fallback: check which label appears in the response
        chosen_label = candidate_labels[0]
        confidence = 0.5
        for label in candidate_labels:
            if label.lower() in response.text.lower():
                chosen_label = label
                confidence = 0.7
                break

    # Build full results list with the chosen label at top
    results = []
    remaining_score = 1.0 - confidence
    per_other = remaining_score / max(len(candidate_labels) - 1, 1)
    for label in candidate_labels:
        if label == chosen_label:
            results.append({"label": label, "score": confidence})
        else:
            results.append({"label": label, "score": round(per_other, 3)})

    results.sort(key=lambda x: x["score"], reverse=True)
    log.info(f"[GenAI] image classified as '{chosen_label}' ({confidence:.2f})")
    return results


# ---------------------------------------------------------------------------
# ASR (MedASR)
# ---------------------------------------------------------------------------

def transcribe_audio(
    audio_bytes: bytes,
    model_id: str = "google/medasr",
) -> str:
    """
    Transcribe audio.

    Tries Tier 0 (local vLLM) → Tier 1 (HF MedASR) → Tier 2 (GenAI) → raises.
    """
    # --- Tier 0: Local vLLM (if it supports ASR) ---
    local_url = _get_local_vllm_url()
    if local_url:
        try:
            return _local_transcribe_audio(audio_bytes, local_url)
        except Exception as exc:
            log.warning(f"[LocalVLLM] ASR failed: {exc}")

    # --- Tier 1: HF Inference API ---
    hf_token = _get_hf_token()
    if hf_token:
        try:
            from huggingface_hub import InferenceClient
            client = InferenceClient(token=hf_token)
            result = client.automatic_speech_recognition(audio=audio_bytes, model=model_id)
            transcript = result.text if hasattr(result, "text") else str(result)
            log.info(f"[HF] MedASR transcribed {len(transcript)} chars")
            return transcript
        except Exception as exc:
            log.warning(f"[HF] ASR failed: {exc}")

    # --- Tier 2: GenAI SDK ---
    genai_key = _get_genai_key()
    if genai_key:
        try:
            return _genai_transcribe_audio(audio_bytes, genai_key)
        except Exception as exc:
            log.warning(f"[GenAI] audio transcription failed: {exc}")

    raise RuntimeError(
        "No inference backend available for audio transcription. "
        "Set LOCAL_VLLM_URL, HF_TOKEN, or GOOGLE_API_KEY — see module docstring."
    )


def _local_transcribe_audio(audio_bytes: bytes, base_url: str) -> str:
    """Transcribe audio using local Whisper-compatible endpoint (Tier 0)."""
    import json as _json
    import urllib.request

    base_url = base_url.rstrip("/")
    # Use OpenAI-compatible transcriptions endpoint
    boundary = "----MedScribeAudioBoundary"
    body = (
        f"--{boundary}\r\n"
        f'Content-Disposition: form-data; name="file"; filename="audio.wav"\r\n'
        f"Content-Type: audio/wav\r\n\r\n"
    ).encode("utf-8") + audio_bytes + f"\r\n--{boundary}--\r\n".encode("utf-8")

    req = urllib.request.Request(
        f"{base_url}/v1/audio/transcriptions",
        data=body,
        headers={"Content-Type": f"multipart/form-data; boundary={boundary}"},
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=60) as resp:
        data = _json.loads(resp.read().decode("utf-8"))

    result = data.get("text", "")
    log.info(f"[LocalVLLM] transcribed {len(result)} chars (offline)")
    return result


def _genai_transcribe_audio(audio_bytes: bytes, api_key: str) -> str:
    """Transcribe audio using GenAI SDK."""
    from google import genai
    from google.genai import types as gtypes

    client = genai.Client(api_key=api_key)

    audio_part = gtypes.Part.from_bytes(data=audio_bytes, mime_type="audio/wav")
    text_part = gtypes.Part.from_text(
        text="Transcribe this medical audio recording accurately. "
             "Include all medical terminology, drug names, and clinical findings. "
             "Output ONLY the transcript text, no commentary."
    )

    config = gtypes.GenerateContentConfig(
        system_instruction=(
            "You are a medical transcription specialist. "
            "Produce accurate verbatim transcripts."
        ),
        max_output_tokens=4096,
        temperature=0.0,
    )

    response = client.models.generate_content(
        model="gemma-3-4b-it",
        contents=[audio_part, text_part],
        config=config,
    )
    result = response.text
    log.info(f"[GenAI] transcribed {len(result)} chars")
    return result


# ---------------------------------------------------------------------------
# PIL Image -> bytes helper
# ---------------------------------------------------------------------------

def pil_to_bytes(image: Any, format: str = "JPEG") -> bytes:
    """Convert a PIL Image to raw bytes."""
    from PIL import Image as PILImage
    if not isinstance(image, PILImage.Image):
        raise TypeError(f"Expected PIL Image, got {type(image)}")
    if image.mode != "RGB":
        image = image.convert("RGB")
    buf = io.BytesIO()
    image.save(buf, format=format)
    return buf.getvalue()