import os, json, httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

AGENT_URL = os.getenv("AGENT_URL", "http://ai-agent:8080")
OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")

def _read_api_key():
    # Prefer file from Docker secret if present
    path = os.getenv("OPENAI_API_KEY_FILE", "/run/secrets/openai_api_key")
    if os.path.exists(path):
        return open(path, "r").read().strip()
    return os.getenv("OPENAI_API_KEY", "")

SYSTEM_PROMPT = (
    "You are an ops command planner. Convert the user's intent into a STRICT JSON object "
    "with fields: action (scale|restart_service), params (dict). No prose. Examples: "
    '{"action":"scale","params":{"service":"weblabs_php","replicas":3}} '
    'or {"action":"restart_service","params":{"service":"weblabs_php"}}. '
    "Only produce valid JSON. If unclear, choose the safest no-op."
)

class ChatIn(BaseModel):
    prompt: str

app = FastAPI(title="AI Relay (LLM -> Agent)")

@app.get("/health")
def health():
    return {"ok": True}

@app.post("/chat")
async def chat(inp: ChatIn):
    api_key = _read_api_key()
    if not api_key:
        raise HTTPException(500, "Missing OPENAI_API_KEY (env or secret).")

    # Call OpenAI Responses API
    url = "https://api.openai.com/v1/responses"
    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    body = {
        "model": OPENAI_MODEL,
        "input": f"{SYSTEM_PROMPT}\nUSER: {inp.prompt}",
        "max_output_tokens": 300,
        "temperature": 0.1
    }

    async with httpx.AsyncClient(timeout=30) as client:
        r = await client.post(url, headers=headers, json=body)
        if r.status_code >= 400:
            raise HTTPException(502, f"OpenAI error: {r.text}")
        data = r.json()
        # Responses API returns output in 'output_text' (or tool messages). Try common fields.
        content = data.get("output_text") or data.get("content") or ""
        if isinstance(content, list):
            # Some responses return a list of content parts; take text from first text part
            for part in content:
                if part.get("type") in ("output_text", "text") and part.get("text"):
                    content = part["text"]
                    break
        if not isinstance(content, str):
            content = str(content)

    # Parse JSON from the model output
    try:
        cmd = json.loads(content)
    except Exception as e:
        raise HTTPException(500, f"Failed to parse model JSON: {e}; content={content[:200]}")

    # Forward to the agent
    async with httpx.AsyncClient(timeout=15) as client:
        r = await client.post(f"{AGENT_URL}/command", json=cmd)
        if r.status_code >= 400:
            raise HTTPException(r.status_code, f"Agent error: {r.text}")
        return r.json()