import io
import json
import uuid
from pathlib import Path

from openai import AsyncOpenAI

from config import LLM_BASE_URL, LLM_MODEL, OPENAI_API_KEY

DATA_DIR   = Path(__file__).parent / "data"
STATE_FILE = DATA_DIR / "report_state.json"

EXTRACTION_PROMPT = (
    "You are a medical lab report parser. Extract every test result from the blood test "
    "report text below. Return ONLY a JSON object of the form "
    '{"results": [{"test_name": str, "value": str, "unit": str, "reference_range": str, '
    '"flag": "low"|"normal"|"high"|"unknown", "value_numeric": number|null, '
    '"ref_low": number|null, "ref_high": number|null}]}. '
    "If the report does not explicitly mark a result as low/high, infer the flag by "
    "comparing the value to the reference range. If a field isn't present in the report, "
    "use an empty string for string fields. "
    "value_numeric is the numeric form of `value` (e.g. \"14.2\" -> 14.2), or null if the "
    "value is not a plain number. ref_low/ref_high are the numeric lower/upper bounds "
    "parsed from `reference_range` when it describes a simple numeric range, e.g. "
    "\"30-100\" -> ref_low=30, ref_high=100; \"below 150\" -> ref_low=null, ref_high=150; "
    "\"above 40\" -> ref_low=40, ref_high=null. If `reference_range` is not a simple "
    "numeric range (qualitative, multiple sub-ranges, etc.), set both ref_low and ref_high "
    "to null. Do not include any text outside the JSON object."
)

_client: AsyncOpenAI | None = None


def _get_client() -> AsyncOpenAI:
    global _client
    if _client is None:
        _client = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=LLM_BASE_URL)
    return _client


def _load_state() -> dict:
    if STATE_FILE.exists():
        try:
            return json.loads(STATE_FILE.read_text())
        except Exception:
            pass
    return {"report_id": None, "filename": None, "results": [], "summary": None}


def _save_state() -> None:
    STATE_FILE.write_text(json.dumps(report_state, indent=2))


report_state: dict = _load_state()


def parse_pdf_text(raw: bytes) -> str:
    from pypdf import PdfReader
    reader = PdfReader(io.BytesIO(raw))
    pages  = [p.extract_text() or "" for p in reader.pages]
    return "\n\n".join(p for p in pages if p.strip())


async def extract_lab_values(report_text: str, model: str | None = None) -> list[dict]:
    resp = await _get_client().chat.completions.create(
        model=model or LLM_MODEL,
        messages=[
            {"role": "system", "content": EXTRACTION_PROMPT},
            {"role": "user", "content": report_text[:12000]},
        ],
        response_format={"type": "json_object"},
    )
    try:
        data = json.loads(resp.choices[0].message.content or "{}")
    except Exception:
        data = {}
    return data.get("results", [])


def format_report_summary(results: list[dict]) -> str:
    abnormal = [r for r in results if r.get("flag") in ("low", "high")]
    normal   = [r for r in results if r.get("flag") == "normal"]

    lines = []
    if abnormal:
        lines.append("Here are the markers that are outside the normal range:")
        for r in abnormal:
            name    = r.get("test_name") or "Unknown marker"
            val_str = f"{r.get('value', '')} {r.get('unit', '')}".strip()
            line    = f"{name} is {r.get('flag')} at {val_str}".strip()
            ref     = r.get("reference_range")
            if ref:
                line += f" (reference range: {ref})"
            lines.append(line + ".")
    else:
        lines.append("All markers in this report are within the normal range.")

    if normal:
        names = ", ".join(r.get("test_name") or "Unknown marker" for r in normal)
        lines.append(f"The following markers were normal: {names}.")

    lines.append(
        "This is not a medical diagnosis — please consult your doctor about these results "
        "before making any changes to your diet or supplements."
    )
    return "\n".join(lines)


async def process_report_upload(raw: bytes, filename: str, model: str | None = None) -> dict:
    if filename.lower().endswith(".pdf"):
        text = parse_pdf_text(raw)
    else:
        text = raw.decode("utf-8", errors="replace")

    if not text.strip():
        raise ValueError("Could not extract any text from the uploaded report.")

    results = await extract_lab_values(text, model=model)
    summary = format_report_summary(results)

    report_state["report_id"] = uuid.uuid4().hex[:8]
    report_state["filename"]  = filename
    report_state["results"]   = results
    report_state["summary"]   = summary
    _save_state()

    return dict(report_state)


def get_current_report() -> dict:
    if not report_state.get("results"):
        return {"has_report": False}
    return {"has_report": True, **report_state}


def clear_report() -> None:
    report_state["report_id"] = None
    report_state["filename"]  = None
    report_state["results"]   = []
    report_state["summary"]   = None
    _save_state()