#!/usr/bin/env python3
"""MedGenesis – OpenAI async helpers (summary + QA).

Changes vs. legacy version
~~~~~~~~~~~~~~~~~~~~~~~~~~
* Centralised **`_client()`** getter with singleton cache (avoids TLS overhead).
* Exponential‑back‑off retry (2×, 4×) for transient 5xx.
* Supports model override (`model="gpt-4o-mini"`, etc.).
* Allows temperature & max_tokens tuning via kwargs.
* Returns *str* (content) directly; orchestrator wraps if needed.
"""
from __future__ import annotations

import os, asyncio, functools, time
from typing import Any, Dict

import openai

openai.api_key = os.getenv("OPENAI_API_KEY")
if not openai.api_key:
    raise RuntimeError("OPENAI_API_KEY not set in environment")

# ---------------------------------------------------------------------
# Internal client helper (cached)
# ---------------------------------------------------------------------
@functools.lru_cache(maxsize=1)
def _client() -> openai.AsyncOpenAI:
    return openai.AsyncOpenAI(api_key=openai.api_key)


async def _chat(messages: list[dict[str, str]], *, model: str, max_tokens: int, temperature: float = 0.2, retries: int = 3) -> str:
    delay = 2
    for _ in range(retries):
        try:
            resp = await _client().chat.completions.create(
                model=model,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature,
            )
            return resp.choices[0].message.content.strip()
        except openai.OpenAIError as e:
            if retries <= 1:
                raise
            await asyncio.sleep(delay)
            delay *= 2
    # Should not reach here
    return "[OpenAI request failed]"

# ---------------------------------------------------------------------
# Public helpers
# ---------------------------------------------------------------------
async def ai_summarize(text: str, *, prompt: str | None = None, model: str = "gpt-4o", max_tokens: int = 350) -> str:
    """LLM summariser tuned for biomedical search blobs."""
    if not prompt:
        prompt = (
            "Summarize the following biomedical search results. Highlight key findings, "
            "significant genes/drugs/trials, and suggest future research directions."
        )
    system = {"role": "system", "content": "You are an expert biomedical research assistant."}
    user   = {"role": "user", "content": f"{prompt}\n\n{text}"}
    return await _chat([system, user], model=model, max_tokens=max_tokens)


async def ai_qa(question: str, *, context: str = "", model: str = "gpt-4o", max_tokens: int = 350) -> str:
    """One‑shot QA against provided *context*."""
    system = {"role": "system", "content": "You are an advanced biomedical research agent."}
    user   = {"role": "user", "content": f"Answer the question using the given context.\n\nQuestion: {question}\nContext: {context}"}
    return await _chat([system, user], model=model, max_tokens=max_tokens)