"""Minimal LLM proxy for canvas.flow-master.ai.

Brokers POST /internal/canvas-llm/chat to one of:
  - OPENAI_API_KEY -> https://api.openai.com/v1/chat/completions
  - ANTHROPIC_API_KEY -> https://api.anthropic.com/v1/messages

Returns {content, provider}. Returns 503 when no key is configured so the
frontend's deterministic fallback fires.

Request shape (mirrors @earendil-works/pi-ai):
  POST /internal/canvas-llm/chat
  body: {"messages": [{"role": "system|user|assistant", "content": "..."}], "max_tokens": 320}

No streaming, no tool calls, no images — keep it tiny. The frontend already
calls deterministic tools on the EA2 backend; this proxy just synthesises
fluent natural-language responses when no tool matches.
"""

import json
import os
from typing import Any

import httpx
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "").strip()
OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4o-mini").strip()
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "").strip()
ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-haiku-latest").strip()

app = FastAPI(title="canvas-llm-proxy", version="0.1.0")


@app.get("/health")
async def health() -> dict[str, Any]:
    return {
        "ok": True,
        "providers_available": {
            "openai": bool(OPENAI_API_KEY),
            "anthropic": bool(ANTHROPIC_API_KEY),
        },
    }


def _split_system_messages(messages: list[dict]) -> tuple[str, list[dict]]:
    """Anthropic uses a separate `system` field; OpenAI accepts inline."""
    system_chunks: list[str] = []
    non_system: list[dict] = []
    for m in messages:
        if m.get("role") == "system":
            system_chunks.append(str(m.get("content", "")))
        else:
            non_system.append(m)
    return ("\n\n".join(system_chunks), non_system)


@app.post("/internal/canvas-llm/chat")
async def chat(request: Request) -> JSONResponse:
    try:
        body = await request.json()
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"invalid json: {e}")

    messages = body.get("messages", [])
    if not isinstance(messages, list) or not messages:
        raise HTTPException(status_code=400, detail="messages[] required")
    max_tokens = int(body.get("max_tokens", 320))

    if ANTHROPIC_API_KEY:
        system, rest = _split_system_messages(messages)
        anth_messages = [
            {"role": m.get("role", "user"), "content": [{"type": "text", "text": str(m.get("content", ""))}]}
            for m in rest
            if m.get("role") in ("user", "assistant")
        ]
        if not anth_messages:
            anth_messages = [{"role": "user", "content": [{"type": "text", "text": "Hello."}]}]
        payload = {
            "model": ANTHROPIC_MODEL,
            "max_tokens": max_tokens,
            "messages": anth_messages,
        }
        if system:
            payload["system"] = system
        async with httpx.AsyncClient(timeout=30.0) as client:
            r = await client.post(
                "https://api.anthropic.com/v1/messages",
                headers={
                    "x-api-key": ANTHROPIC_API_KEY,
                    "anthropic-version": "2023-06-01",
                    "content-type": "application/json",
                },
                json=payload,
            )
        if r.status_code >= 400:
            return JSONResponse(
                status_code=502,
                content={"error": "anthropic upstream", "status": r.status_code, "body": r.text[:500]},
            )
        data = r.json()
        chunks = data.get("content") or []
        content = "".join(c.get("text", "") for c in chunks if c.get("type") == "text").strip()
        return JSONResponse(content={"content": content, "provider": "anthropic"})

    if OPENAI_API_KEY:
        payload = {
            "model": OPENAI_MODEL,
            "max_tokens": max_tokens,
            "messages": [
                {"role": m.get("role", "user"), "content": str(m.get("content", ""))}
                for m in messages
            ],
        }
        async with httpx.AsyncClient(timeout=30.0) as client:
            r = await client.post(
                "https://api.openai.com/v1/chat/completions",
                headers={
                    "Authorization": f"Bearer {OPENAI_API_KEY}",
                    "content-type": "application/json",
                },
                json=payload,
            )
        if r.status_code >= 400:
            return JSONResponse(
                status_code=502,
                content={"error": "openai upstream", "status": r.status_code, "body": r.text[:500]},
            )
        data = r.json()
        choices = data.get("choices") or []
        content = ""
        if choices:
            msg = choices[0].get("message") or {}
            content = str(msg.get("content", "")).strip()
        return JSONResponse(content={"content": content, "provider": "openai"})

    return JSONResponse(
        status_code=503,
        content={"error": "natural language replies are not enabled in this environment"},
    )