from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import Optional
import os
import asyncio
import threading
import json
import time
import httpx
from dotenv import load_dotenv

load_dotenv()

from neo4j_setup import neo4j_conn, setup_schema
from graphrag import retrieve_patient_trial_matches, rag_query, get_graph_stats
from data_ingestion import ingest_sample_data
from fhir_adapter import get_patient_profile, get_mock_fhir_patient, get_all_patient_ids, MOCK_FHIR_PATIENTS
from clinicaltrials_api import search_trials_sync, get_trial_details_sync, get_trial_details
from matching_engine import match_patient_to_trials, score_patient_for_trial, find_eligible_patients_for_trial
from a2a_workflow import start_pipeline, run_pipeline, get_workflow_status, list_workflows, _workflows
from analytics import get_kpi_summary, get_enrollment_funnel, get_site_performance, get_patient_demographics, get_recruitment_timeline, get_map_data
from recruitment_pipeline import get_kanban_board, get_all_records, create_record, update_status, generate_and_store_outreach, RecruitmentStatus
from llm_client import summarize_trial
from graph_seeder import run_seeder, seed_sync
from trial_enrichment import enrich_trials_from_search, get_eligible_patient_counts, get_graph_intelligence
from intake_matching import match_intake_to_trials, save_intake_as_patient, BIOMARKER_REGISTRY
from llm_client import get_provider_status
from fhir_server import (
    get_fhir_server_status, get_live_patient_profile,
    search_fhir_patients, build_sharp_context,
)
import consent_agent

app = FastAPI(
    title="Precision Clinical Trial Matching & Recruitment Agent",
    version="2.0.0",
    description="A2A-powered agent for precision clinical trial matching using FHIR R4 standards and GraphRAG",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ── Request Models ─────────────────────────────────────────────────────────────

class PatientIngestRequest(BaseModel):
    id: str
    age: int
    gender: str
    diagnosis_code: str

class WorkflowRequest(BaseModel):
    patient_id: str
    nct_id: Optional[str] = None
    condition: Optional[str] = None
    # SHARP / SMART on FHIR fields
    fhir_token: Optional[str] = None        # Bearer token for FHIR server access
    fhir_base_url: Optional[str] = None     # Override FHIR base for this session
    session_id: Optional[str] = None        # Caller-supplied session ID for tracing

class OutreachRequest(BaseModel):
    patient_id: str
    nct_id: str
    trial_title: str
    channel: str = "patient_email"

class StatusUpdateRequest(BaseModel):
    status: RecruitmentStatus

class RAGRequest(BaseModel):
    question: str

class IntakeLabs(BaseModel):
    hemoglobin: Optional[float] = None    # g/dL
    wbc: Optional[float] = None           # ×10⁹/L
    anc: Optional[float] = None           # ×10⁹/L
    platelets: Optional[float] = None     # ×10⁹/L
    creatinine: Optional[float] = None    # μmol/L
    egfr: Optional[float] = None          # mL/min/1.73m²
    bilirubin: Optional[float] = None     # μmol/L
    alt: Optional[float] = None           # U/L
    ast: Optional[float] = None           # U/L
    albumin: Optional[float] = None       # g/dL

class IntakeRequest(BaseModel):
    condition: str                          # free text: "breast cancer"
    age: Optional[int] = None              # years
    sex: Optional[str] = None              # MALE / FEMALE
    ecog: Optional[int] = None             # 0–4
    stage: Optional[str] = None            # I / II / III / IV
    biomarkers: list[str] = []             # list of BIOMARKER_REGISTRY keys
    labs: Optional[IntakeLabs] = None
    prior_chemo: bool = False
    prior_radiation: bool = False
    prior_surgery: bool = False
    medications: list[str] = []
    save_to_graph: bool = False            # persist as Patient node

class ConsentStatusRequest(BaseModel):
    status: str  # SIGNED | DECLINED | EXPIRED
    notes: Optional[str] = None

class A2ATaskRequest(BaseModel):
    task_id: Optional[str] = None
    type: str
    payload: dict

class RecruitmentRecordRequest(BaseModel):
    patient_id: str
    nct_id: str
    trial_title: str
    match_score: float = 0.75


# ── Core / Health ──────────────────────────────────────────────────────────────

@app.get("/")
async def root():
    return {
        "name": "Precision Clinical Trial Matching Agent",
        "version": "2.0.0",
        "status": "operational",
        "standards": ["FHIR R4", "MCP", "A2A"],
    }

# ── Configuration & Provider Status ──────────────────────────────────────────

@app.get("/api/v1/config/llm")
async def llm_config():
    """Current LLM provider configuration and HIPAA BAA eligibility status."""
    return get_provider_status()

@app.get("/api/v1/config/fhir")
async def fhir_config():
    """Current FHIR server connection status and SMART token configuration."""
    return get_fhir_server_status()

@app.get("/api/v1/config")
async def full_config():
    """Full system configuration — LLM provider + FHIR server status."""
    return {
        "llm": get_provider_status(),
        "fhir": get_fhir_server_status(),
    }


# ── Live FHIR Patient Endpoints ───────────────────────────────────────────────

@app.get("/api/v1/fhir/patients")
async def list_live_fhir_patients(count: int = 10):
    """Fetch real Patient resources from the configured FHIR R4 server."""
    patients = search_fhir_patients(count=min(count, 50))
    return {"patients": patients, "total": len(patients), "source": "fhir_server"}

@app.get("/api/v1/fhir/patients/{fhir_id}")
async def get_live_fhir_patient(fhir_id: str, fhir_token: Optional[str] = None):
    """
    Fetch a patient from the live FHIR server, build a matching profile,
    and attach a SHARP context envelope.
    """
    sharp_ctx = build_sharp_context(
        patient_id=fhir_id,
        fhir_ref=f"Patient/{fhir_id}",
    )
    profile = get_live_patient_profile(fhir_id, sharp_context=sharp_ctx)
    if not profile:
        raise HTTPException(status_code=404, detail=f"FHIR Patient {fhir_id} not found on server")
    return profile

@app.post("/api/v1/fhir/patients/{fhir_id}/match-trials")
async def match_live_fhir_patient(fhir_id: str, fhir_token: Optional[str] = None, top_n: int = 5):
    """
    Full pipeline: fetch patient from live FHIR server → match against trials.
    SHARP context envelope included in response.
    """
    sharp_ctx = build_sharp_context(patient_id=fhir_id, fhir_ref=f"Patient/{fhir_id}")
    profile = get_live_patient_profile(fhir_id, sharp_context=sharp_ctx)
    if not profile:
        raise HTTPException(status_code=404, detail=f"FHIR Patient {fhir_id} not found")
    from matching_engine import match_patient_to_trials as _match
    condition = profile.get("diagnosis_names", ["cancer"])[0] if profile.get("diagnosis_names") else "cancer"
    matches = _match(fhir_id, condition, top_n)
    return {
        "fhir_id": fhir_id,
        "profile": profile,
        "matches": matches,
        "total": len(matches),
        "sharp_context": sharp_ctx,
    }


@app.get("/health")
async def health():
    stats = get_graph_stats()

    # Neo4j connectivity check
    neo4j_ok = False
    try:
        neo4j_conn.run_query("RETURN 1")
        neo4j_ok = True
    except Exception:
        pass

    # CT.gov reachability
    ctgov_ok = False
    try:
        async with httpx.AsyncClient(timeout=4) as client:
            r = await client.get(
                "https://clinicaltrials.gov/api/v2/studies",
                params={"query.term": "cancer", "pageSize": 1},
            )
            ctgov_ok = r.status_code == 200
    except Exception:
        pass

    patient_count = stats.get("patients", 0)
    trial_count = stats.get("trials", 0)
    edge_count = stats.get("eligible_for_relationships", 0)
    seeded = patient_count >= 100 and trial_count >= 50

    llm_status  = get_provider_status()
    fhir_status = get_fhir_server_status()

    overall = "healthy" if (neo4j_ok and ctgov_ok and seeded) else ("degraded" if neo4j_ok else "unhealthy")
    return {
        "status": overall,
        "neo4j": "connected" if neo4j_ok else "unavailable",
        "ctgov_api": "reachable" if ctgov_ok else "unreachable",
        "fhir_server": "reachable" if fhir_status.get("reachable") else "unreachable",
        "fhir_base_url": fhir_status.get("base_url"),
        "smart_auth": fhir_status.get("auth_method"),
        "graph_seeded": seeded,
        "graph_stats": stats,
        "patient_count": patient_count,
        "trial_count": trial_count,
        "eligible_edges": edge_count,
        "llm_provider": llm_status.get("provider"),
        "llm_model": llm_status.get("model"),
        "llm_hipaa_eligible": llm_status.get("hipaa_eligible"),
        "version": "2.0.0",
        "standards": ["FHIR R4", "MCP", "A2A", "SHARP"],
    }


# ── FHIR Patient Endpoints ─────────────────────────────────────────────────────

@app.get("/api/v1/patients")
async def list_patients():
    patients = []
    for pid in get_all_patient_ids():
        profile = get_patient_profile(pid)
        if profile:
            patients.append(profile)
    return {"patients": patients, "total": len(patients)}

@app.get("/api/v1/patients/{patient_id}")
async def get_patient(patient_id: str):
    profile = get_patient_profile(patient_id)
    if not profile:
        raise HTTPException(status_code=404, detail=f"Patient {patient_id} not found")
    fhir = get_mock_fhir_patient(patient_id)
    return {"profile": profile, "fhir_bundle": fhir.model_dump() if fhir else None}

@app.get("/api/v1/patients/{patient_id}/fhir")
async def get_patient_fhir(patient_id: str):
    fhir = get_mock_fhir_patient(patient_id)
    if not fhir:
        raise HTTPException(status_code=404, detail="Patient not found")
    return fhir.model_dump()

# Legacy endpoint
@app.post("/ingest_patient")
async def ingest_patient(patient: PatientIngestRequest):
    query = """
    MERGE (p:Patient {id: $id})
    SET p += {age: $age, gender: $gender}
    MERGE (d:Diagnosis {code: $code})
    MERGE (p)-[:HAS_DIAGNOSIS]->(d)
    """
    try:
        neo4j_conn.run_query(query, {"id": patient.id, "age": patient.age, "gender": patient.gender, "code": patient.diagnosis_code})
        return {"status": "Patient data ingested"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


# ── Trial Search & Details ─────────────────────────────────────────────────────

@app.get("/api/v1/trials/search")
async def search_trials_endpoint(
    condition: str,
    phase: Optional[str] = None,
    status: str = "RECRUITING",
    page_size: int = 20,
    background_tasks: BackgroundTasks = None,
):
    trials = search_trials_sync(condition, phase, status, page_size)
    # Passive graph enrichment — fire-and-forget in background
    if background_tasks and trials:
        background_tasks.add_task(enrich_trials_from_search, trials, condition)
    # Attach graph-derived eligible patient counts
    nct_ids = [t["nct_id"] for t in trials if t.get("nct_id")]
    counts = get_eligible_patient_counts(nct_ids)
    for t in trials:
        t["eligible_patients_in_graph"] = counts.get(t.get("nct_id", ""), 0)
    return {"trials": trials, "total": len(trials), "condition": condition, "sorted_by": "last_updated"}

@app.get("/api/v1/trials/{nct_id}")
async def get_trial(nct_id: str):
    trial = get_trial_details_sync(nct_id)
    if not trial:
        raise HTTPException(status_code=404, detail=f"Trial {nct_id} not found")
    summary = summarize_trial(trial)
    return {**trial, "ai_summary": summary}

@app.get("/api/v1/trials/{nct_id}/eligible-patients")
async def get_eligible_patients(nct_id: str):
    results = find_eligible_patients_for_trial(nct_id)
    return {"nct_id": nct_id, "eligible_patients": results, "total": len(results)}

@app.get("/api/v1/trials/{nct_id}/intelligence")
async def trial_graph_intelligence(nct_id: str):
    """Graph-derived intelligence: eligible count, similar trials, biomarker distribution, sites."""
    return get_graph_intelligence(nct_id)


# ── Clinical Data Intake ───────────────────────────────────────────────────────

@app.post("/api/v1/intake/match")
async def intake_match(request: IntakeRequest):
    """
    Accept raw clinical data (SI units) and return ranked trial matches.
    No patient ID required — useful for individuals, clinicians, and researchers.
    """
    intake = {
        "condition": request.condition,
        "age": request.age,
        "sex": (request.sex or "").upper() or None,
        "ecog": request.ecog,
        "stage": request.stage,
        "biomarkers": request.biomarkers,
        "labs": request.labs.model_dump(exclude_none=True) if request.labs else {},
        "prior_chemo": request.prior_chemo,
        "prior_radiation": request.prior_radiation,
        "prior_surgery": request.prior_surgery,
        "medications": request.medications,
    }
    matches = match_intake_to_trials(intake, request.condition, limit=10)
    patient_id = None
    if request.save_to_graph:
        patient_id = save_intake_as_patient(intake)
    return {
        "condition": request.condition,
        "matches": matches,
        "total": len(matches),
        "patient_id": patient_id,
    }

@app.get("/api/v1/intake/biomarkers")
async def list_biomarkers():
    """Return the full biomarker registry for populating the intake form."""
    return {
        "biomarkers": [
            {"id": bid, "label": info[0]}
            for bid, info in BIOMARKER_REGISTRY.items()
        ]
    }

# Legacy endpoint
@app.get("/match_trials/{patient_id}")
async def match_trials_legacy(patient_id: str):
    matches = retrieve_patient_trial_matches(patient_id)
    return {"matches": matches}


# ── Matching Engine ────────────────────────────────────────────────────────────

@app.get("/api/v1/patients/{patient_id}/match-trials")
async def match_patient_trials(patient_id: str, condition: Optional[str] = None, top_n: int = 5):
    matches = match_patient_to_trials(patient_id, condition, top_n)
    return {"patient_id": patient_id, "matches": matches, "total": len(matches)}

@app.post("/api/v1/patients/{patient_id}/screen/{nct_id}")
async def screen_patient_for_trial(patient_id: str, nct_id: str):
    trial = await get_trial_details(nct_id)
    if not trial:
        raise HTTPException(status_code=404, detail=f"Trial {nct_id} not found")
    result = score_patient_for_trial(patient_id, trial)
    if "error" in result:
        raise HTTPException(status_code=404, detail=result["error"])
    return result


# ── A2A Workflow ───────────────────────────────────────────────────────────────

@app.post("/api/v1/workflow/run")
async def run_workflow(request: WorkflowRequest, background_tasks: BackgroundTasks):
    workflow_id = start_pipeline(request.patient_id, request.nct_id, request.condition)
    result = run_pipeline(workflow_id)
    return {
        "workflow_id": workflow_id,
        "status": result["current_state"],
        "result": result.get("result"),
        "events": result.get("events", []),
    }


@app.post("/api/v1/workflow/start")
async def start_workflow(request: WorkflowRequest, background_tasks: BackgroundTasks):
    """Start a pipeline and return workflow_id immediately; stream progress via /workflow/{id}/stream."""
    workflow_id = start_pipeline(
        request.patient_id, request.nct_id, request.condition,
        fhir_token=request.fhir_token,
        fhir_base_url=request.fhir_base_url,
        session_id=request.session_id,
    )
    background_tasks.add_task(_run_pipeline_background, workflow_id)
    sharp_ctx = _workflows[workflow_id].get("sharp_context", {})
    return {
        "workflow_id": workflow_id,
        "status": "PENDING",
        "stream_url": f"/api/v1/workflow/{workflow_id}/stream",
        "sharp_context": sharp_ctx,
    }


def _run_pipeline_background(workflow_id: str):
    run_pipeline(workflow_id)


@app.get("/api/v1/workflow/{workflow_id}/stream")
async def stream_workflow(workflow_id: str, request: Request):
    """SSE endpoint — streams A2A state transitions as they happen."""
    async def event_generator():
        seen = 0
        timeout = 120  # max seconds to stream
        deadline = time.time() + timeout
        while time.time() < deadline:
            if await request.is_disconnected():
                break
            wf = _workflows.get(workflow_id)
            if not wf:
                yield f"data: {json.dumps({'error': 'workflow_not_found'})}\n\n"
                break
            events = wf.get("events", [])
            # Emit any new events since last check
            for evt in events[seen:]:
                payload = {
                    "state": evt["state"],
                    "message": evt["message"],
                    "timestamp": evt["timestamp"],
                }
                if evt.get("data") and not evt["data"].__class__.__name__ == "dict" or evt.get("data"):
                    try:
                        # Only include lightweight summary data, not full result blobs
                        d = evt.get("data") or {}
                        if isinstance(d, dict):
                            safe = {k: v for k, v in d.items() if k not in ("matched_trials", "recruitment_records", "patient_profile")}
                            if safe:
                                payload["data"] = safe
                    except Exception:
                        pass
                yield f"data: {json.dumps(payload)}\n\n"
                seen += 1
            current = wf.get("current_state", "")
            if current in ("COMPLETED", "FAILED"):
                # Send final event with result summary
                result = wf.get("result") or {}
                final = {
                    "state": current,
                    "eligible_trials": result.get("eligible_trials", 0),
                    "total_evaluated": result.get("total_trials_evaluated", 0),
                    "recruitment_records": len(result.get("recruitment_records", [])),
                    "error": wf.get("error"),
                }
                yield f"data: {json.dumps(final)}\n\n"
                yield "data: [DONE]\n\n"
                break
            await asyncio.sleep(0.5)

    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )


@app.get("/api/v1/workflow/{workflow_id}/status")
async def workflow_status(workflow_id: str):
    status = get_workflow_status(workflow_id)
    if "error" in status:
        raise HTTPException(status_code=404, detail=status["error"])
    return status

@app.get("/api/v1/workflows")
async def list_all_workflows():
    return {"workflows": list_workflows()}


# ── Consent & Scheduling Agent ────────────────────────────────────────────────

@app.post("/api/v1/a2a/task")
async def a2a_task(request: A2ATaskRequest):
    """A2A inter-agent task endpoint — routes CONSENT_REQUEST and SCHEDULE_REQUEST tasks."""
    result = consent_agent.receive_a2a_task(request.model_dump())
    return result

@app.get("/api/v1/consent")
async def list_consents(patient_id: Optional[str] = None):
    return {"consents": consent_agent.list_consent_records(patient_id)}

@app.get("/api/v1/consent/stats")
async def consent_stats():
    return consent_agent.get_consent_stats()

@app.get("/api/v1/consent/{consent_id}")
async def get_consent(consent_id: str):
    record = consent_agent.get_consent_record(consent_id)
    if not record:
        raise HTTPException(status_code=404, detail="Consent record not found")
    return record

@app.patch("/api/v1/consent/{consent_id}/status")
async def update_consent(consent_id: str, request: ConsentStatusRequest):
    valid = {"SIGNED", "DECLINED", "EXPIRED"}
    if request.status not in valid:
        raise HTTPException(status_code=400, detail=f"status must be one of {valid}")
    result = consent_agent.update_consent_status(consent_id, request.status, request.notes or "")
    if "error" in result:
        raise HTTPException(status_code=404, detail=result["error"])
    return result

@app.get("/api/v1/appointments")
async def list_appointments(patient_id: Optional[str] = None):
    return {"appointments": consent_agent.list_appointments(patient_id)}

@app.patch("/api/v1/appointments/{appt_id}/confirm")
async def confirm_appointment(appt_id: str):
    result = consent_agent.confirm_appointment(appt_id)
    if "error" in result:
        raise HTTPException(status_code=404, detail=result["error"])
    return result


# ── Recruitment Pipeline ───────────────────────────────────────────────────────

@app.get("/api/v1/recruitment/board")
async def kanban_board():
    return get_kanban_board()

@app.get("/api/v1/recruitment/records")
async def all_recruitment_records():
    return {"records": get_all_records()}

@app.post("/api/v1/recruitment/records")
async def create_recruitment_record(request: RecruitmentRecordRequest):
    record = create_record(request.patient_id, request.nct_id, request.trial_title, request.match_score)
    return record

@app.patch("/api/v1/recruitment/records/{record_id}/status")
async def update_record_status(record_id: str, request: StatusUpdateRequest):
    try:
        return update_status(record_id, request.status)
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))

@app.post("/api/v1/recruitment/outreach")
async def generate_outreach(request: OutreachRequest):
    trial = get_trial_details_sync(request.nct_id) or {
        "nct_id": request.nct_id,
        "title": request.trial_title,
        "brief_summary": "",
        "phase": "N/A",
        "sponsor": "N/A",
        "locations": [],
    }
    try:
        result = generate_and_store_outreach(
            request.patient_id, request.nct_id, request.trial_title, trial, request.channel
        )
        return result
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))


# ── Analytics & Dashboard ──────────────────────────────────────────────────────

@app.get("/api/v1/analytics/kpi")
async def kpi_summary():
    return get_kpi_summary()

@app.get("/api/v1/analytics/funnel")
async def enrollment_funnel(trial_id: Optional[str] = None):
    return {"funnel": get_enrollment_funnel(trial_id)}

@app.get("/api/v1/analytics/sites")
async def site_performance():
    return {"sites": get_site_performance()}

@app.get("/api/v1/analytics/demographics")
async def patient_demographics(trial_id: Optional[str] = None):
    return get_patient_demographics(trial_id)

@app.get("/api/v1/analytics/timeline")
async def recruitment_timeline(days: int = 30):
    return {"timeline": get_recruitment_timeline(days)}

@app.get("/api/v1/map/data")
async def map_data():
    return get_map_data()


# ── GraphRAG ───────────────────────────────────────────────────────────────────

@app.get("/api/v1/graph/query")
async def graph_query(question: str):
    response = rag_query(question)
    return {"response": response}

@app.post("/api/v1/graph/query")
async def graph_query_post(request: RAGRequest):
    response = rag_query(request.question)
    return {"response": response}

@app.get("/api/v1/graph/stats")
async def graph_stats():
    return get_graph_stats()

@app.get("/api/v1/graph/patients")
async def list_graph_patients(condition: Optional[str] = None, limit: int = 200):
    """Query Neo4j for seeded patient records."""
    if condition:
        rows = neo4j_conn.run_query(
            "MATCH (p:Patient) WHERE toLower(p.condition) CONTAINS toLower($cond) "
            "RETURN p.id AS id, p.name AS name, p.age AS age, p.condition AS condition, "
            "p.city AS city, p.state AS state ORDER BY p.id LIMIT $limit",
            {"cond": condition, "limit": limit},
        )
    else:
        rows = neo4j_conn.run_query(
            "MATCH (p:Patient) RETURN p.id AS id, p.name AS name, p.age AS age, "
            "p.condition AS condition, p.city AS city, p.state AS state "
            "ORDER BY p.id LIMIT $limit",
            {"limit": limit},
        )
    return {"patients": rows, "total": len(rows)}

# Legacy
@app.get("/rag_query")
async def rag_query_legacy(question: str):
    return {"response": rag_query(question)}

@app.post("/enrich_graph")
async def enrich_legacy():
    return {"reward": 0.75, "message": "Graph enrichment via RL (see rl_enrichment.py)"}


# ── Setup ──────────────────────────────────────────────────────────────────────

@app.post("/setup")
async def full_setup(background_tasks: BackgroundTasks):
    setup_schema()
    ingest_sample_data()
    # Seed real data from live APIs in the background
    background_tasks.add_task(_run_seeder_thread)
    return {"status": "Setup started — schema initialized, sample data ingested, real-data seeding running in background"}

@app.post("/setup_sample_data")
async def setup_sample():
    ingest_sample_data()
    return {"status": "Sample data ingested"}

@app.post("/seed")
async def seed_graph(background_tasks: BackgroundTasks, conditions: list[str] | None = None):
    """Trigger real-data seeding from ClinicalTrials.gov, RxNorm, ICD-10, PubMed."""
    background_tasks.add_task(_run_seeder_thread, conditions)
    return {
        "status": "Seeding started in background",
        "sources": ["clinicaltrials.gov", "rxnorm.nlm.nih.gov", "icd10cm nlm", "pubmed ncbi"],
        "conditions": conditions or "all default oncology conditions",
    }

@app.get("/seed/status")
async def seed_status():
    stats = get_graph_stats()
    return {"graph_stats": stats, "note": "Check /api/v1/graph/stats for node counts"}

def _run_seeder_thread(conditions: list[str] | None = None):
    """Run the async seeder in a new thread (avoids event loop conflict with FastAPI)."""
    try:
        asyncio.run(run_seeder(conditions))
    except Exception as e:
        print(f"[seeder] error: {e}")


if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)