import json
from typing import AsyncGenerator

from openai import AsyncOpenAI

from config import LLM_BASE_URL, LLM_MODEL, OPENAI_API_KEY
from tools import TOOLS, run_tool

_SYSTEM_INJECT = (
    "You have access to a live notepad. "
    "Use write_to_notepad whenever the user asks you to note, write down, remember, save, "
    "or record anything — always confirm what you wrote after calling the tool. "
    "Use read_notepad to recall saved notes. "
    "Use clear_notepad only when the user explicitly asks to erase all notes. "
    "Use get_datetime for the current date or time.\n\n"
    "You also act as a clinical assistant for blood test results. "
    "Whenever the user asks about their blood test, lab results, deficiencies, or what "
    "supplements they should take, first call get_blood_report_summary to see which "
    "markers are low, high, or normal. For every abnormal (low or high) marker, call "
    "search_supplements with a short query describing that marker (e.g. 'low vitamin D') "
    "to get supplement recommendations, then explain the deficiency and recommendation in "
    "plain spoken language — no markdown, bullet points, asterisks, or headers, since this "
    "is read aloud by a speaking avatar. If the search_supplements result includes "
    "interaction cautions (e.g. a medication, pregnancy, or condition to be careful with), "
    "briefly mention that caution too, in plain language — this matters most for markers "
    "like kidney function, cholesterol/lipids, and blood sugar where the wrong supplement "
    "can be risky. Always end any discussion of blood results or supplements with a "
    "reminder that this is not a medical diagnosis and the user should consult their "
    "doctor before starting any supplement or changing their dose."
)

_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 _build_history(messages: list) -> list:
    """Inject tool guidance into the system message."""
    msgs = list(messages)
    if msgs and msgs[0].get("role") == "system":
        msgs[0] = {**msgs[0], "content": _SYSTEM_INJECT + "\n\n" + msgs[0]["content"]}
    else:
        msgs.insert(0, {"role": "system", "content": _SYSTEM_INJECT})
    return msgs


async def run_agent(messages: list, model: str | None = None) -> str:
    """Run the full tool-calling loop and return the final text response."""
    history = _build_history(messages)
    m = model or LLM_MODEL
    print(f"[AGENT → {LLM_BASE_URL}] model={m}  msgs={len(history)}", flush=True)

    while True:
        resp = await _get_client().chat.completions.create(
            model=m,
            messages=history,
            tools=TOOLS,
            tool_choice="auto",
            store=True,
        )
        msg = resp.choices[0].message

        if not msg.tool_calls:
            print(f"[AGENT] ✓ final reply: {(msg.content or '')!r:.100}", flush=True)
            return msg.content or ""

        history.append(msg.model_dump(exclude_none=True))
        for tc in msg.tool_calls:
            args   = json.loads(tc.function.arguments or "{}")
            print(f"[AGENT] tool call: {tc.function.name}({args})", flush=True)
            result = await run_tool(tc.function.name, args)
            history.append({"role": "tool", "tool_call_id": tc.id, "content": result})


async def stream_agent(messages: list, model: str | None = None) -> AsyncGenerator[str, None]:
    """
    Stream tokens as soon as the model produces them. Every pass is a single
    streaming call — if the model asks for tool calls we resolve them and
    loop with another streaming call; if it answers directly, those tokens
    are yielded immediately with no extra non-streamed round trip first.
    """
    history = _build_history(messages)
    m = model or LLM_MODEL

    while True:
        stream = await _get_client().chat.completions.create(
            model=m,
            messages=history,
            tools=TOOLS,
            tool_choice="auto",
            stream=True,
            store=True,
        )

        tool_calls: dict[int, dict] = {}
        async for chunk in stream:
            delta = chunk.choices[0].delta
            if delta.content:
                yield delta.content
            for tc_delta in (delta.tool_calls or []):
                slot = tool_calls.setdefault(
                    tc_delta.index, {"id": "", "name": "", "arguments": ""}
                )
                if tc_delta.id:
                    slot["id"] = tc_delta.id
                if tc_delta.function:
                    if tc_delta.function.name:
                        slot["name"] += tc_delta.function.name
                    if tc_delta.function.arguments:
                        slot["arguments"] += tc_delta.function.arguments

        if not tool_calls:
            return  # final answer was already streamed above

        ordered = [tool_calls[i] for i in sorted(tool_calls)]
        history.append({
            "role": "assistant",
            "content": None,
            "tool_calls": [
                {
                    "id": tc["id"],
                    "type": "function",
                    "function": {"name": tc["name"], "arguments": tc["arguments"]},
                }
                for tc in ordered
            ],
        })
        for tc in ordered:
            args = json.loads(tc["arguments"] or "{}")
            print(f"[AGENT] tool call: {tc['name']}({args})", flush=True)
            result = await run_tool(tc["name"], args)
            history.append({"role": "tool", "tool_call_id": tc["id"], "content": result})