"""
MedGenesis – dual-LLM orchestrator
----------------------------------
• Accepts `llm` arg ("openai" | "gemini")
• Defaults to "openai" if arg omitted
"""

import asyncio, httpx
from typing import Dict, Any, List

from mcp.arxiv          import fetch_arxiv
from mcp.pubmed         import fetch_pubmed
from mcp.nlp            import extract_keywords
from mcp.umls           import lookup_umls
from mcp.openfda        import fetch_drug_safety
from mcp.ncbi           import search_gene, get_mesh_definition
from mcp.disgenet       import disease_to_genes
from mcp.clinicaltrials import search_trials
from mcp.openai_utils   import ai_summarize, ai_qa
from mcp.gemini         import gemini_summarize, gemini_qa   # make sure gemini.py exists

# ---------------- LLM router ----------------
def _get_llm(llm: str):
    if llm.lower() == "gemini":
        return gemini_summarize, gemini_qa
    return ai_summarize, ai_qa                         # default → OpenAI


async def _enrich_genes_mesh_disg(keys: List[str]) -> Dict[str, Any]:
    jobs = []
    for k in keys:
        jobs += [search_gene(k), get_mesh_definition(k), disease_to_genes(k)]
    res = await asyncio.gather(*jobs, return_exceptions=True)

    genes, meshes, disg = [], [], []
    for i, r in enumerate(res):
        if isinstance(r, Exception):  # skip failures quietly
            continue
        if   i % 3 == 0: genes.extend(r)
        elif i % 3 == 1: meshes.append(r)
        else:            disg.extend(r)
    return {"genes": genes, "meshes": meshes, "disgenet": disg}


# ------------------------------------------------------------------
async def orchestrate_search(query: str, llm: str = "openai") -> Dict[str, Any]:
    """
    Main orchestrator – returns dict for UI.
    """
    # 1) Literature ---------------------------------------------------
    arxiv_f  = asyncio.create_task(fetch_arxiv(query))
    pubmed_f = asyncio.create_task(fetch_pubmed(query))
    papers   = sum(await asyncio.gather(arxiv_f, pubmed_f), [])

    # 2) Keywords -----------------------------------------------------
    blob = " ".join(p["summary"] for p in papers)
    keys = extract_keywords(blob)[:8]

    # 3) Enrichment ---------------------------------------------------
    umls_f   = [lookup_umls(k)       for k in keys]
    fda_f    = [fetch_drug_safety(k) for k in keys]
    genes_f  = asyncio.create_task(_enrich_genes_mesh_disg(keys))
    trials_f = asyncio.create_task(search_trials(query, max_studies=10))

    umls, fda, genes, trials = await asyncio.gather(
        asyncio.gather(*umls_f,  return_exceptions=True),
        asyncio.gather(*fda_f,   return_exceptions=True),
        genes_f,
        trials_f,
    )

    # 4) AI summary ---------------------------------------------------
    summarize, _ = _get_llm(llm)
    summary = await summarize(blob)

    return {
        "papers"          : papers,
        "umls"            : umls,
        "drug_safety"     : fda,
        "ai_summary"      : summary,
        "llm_used"        : llm.lower(),
        "genes"           : genes["genes"],
        "mesh_defs"       : genes["meshes"],
        "gene_disease"    : genes["disgenet"],
        "clinical_trials" : trials,
    }


async def answer_ai_question(question: str, context: str, llm: str = "openai") -> Dict[str, str]:
    """One-shot follow-up Q-A via chosen engine."""
    _, qa = _get_llm(llm)
    return {"answer": await qa(question, context)}