import json
import os
import sys
import hashlib
from datetime import datetime, timezone
from functools import lru_cache
from pathlib import Path
from typing import Literal

import numpy as np
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field


FieldMode = Literal["mean", "gated", "mu1", "mu2", "mu3", "mu4", "mu5", "mu6"]


class TrajectoryRequest(BaseModel):
    seed: list[float] = Field(default_factory=lambda: [0.5, 0.5, 0.5], min_length=3, max_length=3)
    fieldMode: FieldMode = "mean"
    steps: int = Field(default=700, ge=8, le=1600)
    dt: float = Field(default=1.0, gt=0, le=8.0)
    speedScale: float = Field(default=1.0, gt=0, le=8.0)


class ExplainRequest(BaseModel):
    trajectory: list[list[float]] = Field(min_length=2)
    fieldMode: FieldMode = "mean"
    callLlm: bool = True


CACHE_DIR = Path(os.environ.get("MINDVIS_CACHE_DIR", "/tmp/mindvis-api-cache"))
CACHE_DIR.mkdir(parents=True, exist_ok=True)


def mindvis_repo() -> Path:
    repo = Path(os.environ.get("MINDVIS_REPO", "/app/mindVisualizer")).resolve()
    if not repo.exists():
        raise RuntimeError(f"MINDVIS_REPO does not exist: {repo}")
    if str(repo) not in sys.path:
        sys.path.insert(0, str(repo))
    return repo


@lru_cache(maxsize=1)
def services():
    repo = mindvis_repo()
    data = repo / "data"

    from src.field_loader import TriLinearSampler, load_field
    from src.mesh_overlay import FlowMeshOverlay

    flow = load_field(data / "mdn" / "mdn_particles_rdcim_teacher_edge_hq_grid125_meta.json")
    mean = flow["mean"]
    mus = flow["mus"]
    pi = flow["pi"]
    ent = flow["ENT"]
    amin = flow["amin"]
    amax = flow["amax"]
    span = amax - amin
    diag = float(np.linalg.norm(span))
    target_step = 0.01 * max(diag, 1e-6)

    winner = np.argmax(pi, axis=-1)
    gated = np.zeros_like(mean, dtype=np.float32)
    for idx, mu in enumerate(mus):
        gated += mu * (winner[..., None] == idx).astype(np.float32)

    fields = {"mean": mean, "gated": gated}
    fields.update({f"mu{idx + 1}": mu for idx, mu in enumerate(mus)})
    samplers = {name: TriLinearSampler(field, amin, amax) for name, field in fields.items()}

    def vmax(field):
        mag = np.linalg.norm(field.reshape(-1, 3), axis=1)
        nz = mag[mag > 0]
        return float(np.percentile(nz, 100.0)) if nz.size else 1.0

    vmax_by_name = {name: vmax(field) for name, field in fields.items()}

    # The original Python app uses FlowMeshOverlay + its cached label grid for
    # probe interpretation. This API intentionally reuses that path.
    import vtk

    ren = vtk.vtkRenderer()
    win = vtk.vtkRenderWindow()
    win.OffScreenRenderingOn()
    win.AddRenderer(ren)
    mesh = FlowMeshOverlay(ren=ren, win=win, mesh_dir=data / "meshes", alignment_file=data / "brain_alignment.json")
    cache = data / "label_grid_cache.npz"
    if not mesh.load_label_grid(cache):
        raise RuntimeError("Python label_grid_cache is missing; run setup_brain_data.py in mindVisualizer first.")

    # Optional finer parcellation, exactly as the desktop flow mode auto-detects.
    try:
        from src.extra_parcellation import ExtraParcellation

        atlas = data / "extra_parcellation" / "combined_atlas.nii.gz"
        labels = data / "extra_parcellation" / "combined_atlas_labels.json"
        if atlas.exists():
            extra = ExtraParcellation(atlas, labels if labels.exists() else None)
            if extra.load():
                mesh.set_extra_parcellation(extra)
    except Exception as exc:
        print(f"[extra-parcellation] disabled: {exc}")

    ent_sampler = TriLinearSampler(mean, amin, amax) if ent is not None else None

    return {
        "amin": amin,
        "amax": amax,
        "span": span,
        "samplers": samplers,
        "vmax": vmax_by_name,
        "target_step": target_step,
        "mesh": mesh,
        "entropy": ent,
        "entropy_sampler": ent_sampler,
    }


app = FastAPI(title="MindVisualizer API", version="1.0.0")

origins = [origin.strip() for origin in os.environ.get("CORS_ORIGINS", "*").split(",") if origin.strip()]
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_methods=["GET", "POST", "OPTIONS"],
    allow_headers=["*"],
)


@app.get("/health")
def health():
    try:
        svc = services()
        return {"ok": True, "fieldMin": svc["amin"].tolist(), "fieldMax": svc["amax"].tolist()}
    except Exception as exc:
        raise HTTPException(status_code=503, detail=str(exc)) from exc


@app.post("/api/mindvis/trajectory")
def trajectory(req: TrajectoryRequest):
    svc = services()
    world, mags, stopped = integrate_probe(req, svc)
    transitions = analyze_regions(world, mags, req.callLlm if hasattr(req, "callLlm") else False)
    return {
        "fieldMode": req.fieldMode,
        "trajectory": normalized(world, svc["amin"], svc["span"]).tolist(),
        "worldTrajectory": world.tolist(),
        "fieldMagnitudes": mags.tolist(),
        "stopped": stopped,
        "transitions": transitions,
        "regionText": format_transition_text(transitions),
    }


@app.post("/api/mindvis/explain")
def explain(req: ExplainRequest, request: Request):
    return build_explanation(req, request=request)


@app.post("/api/mindvis/explain/stream")
def explain_stream(req: ExplainRequest, request: Request):
    def event_stream():
        def event(payload: dict) -> str:
            return json.dumps(payload) + "\n"

        try:
            cached = cached_explanation(req)
            if cached is not None:
                yield event({"type": "log", "message": "Returning a cached path explanation."})
                yield event({"type": "result", **cached})
                return

            yield event({"type": "log", "message": "Loading MindVisualizer field samplers and brain label grid."})
            svc = services()
            traj = np.asarray(req.trajectory, dtype=np.float32)
            if traj.ndim != 2 or traj.shape[1] != 3:
                raise HTTPException(status_code=400, detail="trajectory must be an array of [x,y,z] points")

            yield event({"type": "log", "message": f"Received {len(traj)} normalized probe samples in {req.fieldMode} mode."})
            world = denormalized(np.clip(traj, 0.0, 1.0), svc["amin"], svc["span"])
            sampler = svc["samplers"].get(req.fieldMode, svc["samplers"]["mean"])
            mags = np.linalg.norm(sampler.sample_vec(world), axis=1).astype(np.float32)

            yield event({"type": "log", "message": "Sampling flow strength and entropy along the traveled path."})
            transitions = analyze_regions(world, mags, req.callLlm)
            yield event({"type": "log", "message": f"Detected {len(transitions)} entered or nearby anatomical regions."})

            text = format_transition_text(transitions)
            if req.callLlm:
                ensure_free_budget(request)
                from src.region_analyzer import analyze_with_gpt

                yield event({"type": "log", "message": "Calling the OpenAI model for the neuroscience interpretation."})
                text = analyze_with_gpt(
                    transitions,
                    use_rag=os.environ.get("MINDVIS_USE_RAG", "0") == "1",
                    model=os.environ.get("OPENAI_MODEL", "gpt-5.4-mini"),
                    debug=os.environ.get("MINDVIS_DEBUG", "0") == "1",
                )
                ensure_not_budget_error(text)
                yield event({"type": "log", "message": "LLM interpretation finished."})

            result = {"text": text, "transitions": transitions, "regionText": format_transition_text(transitions)}
            write_explanation_cache(req, result)
            yield event({"type": "result", **result})
        except HTTPException as exc:
            detail = exc.detail if isinstance(exc.detail, dict) else {"error": str(exc.detail)}
            yield event({"type": "error", "status": exc.status_code, **detail})
        except Exception as exc:
            yield event({"type": "error", "message": str(exc)})

    return StreamingResponse(event_stream(), media_type="application/x-ndjson")


def build_explanation(req: ExplainRequest, log=None, request: Request | None = None):
    cached = cached_explanation(req)
    if cached is not None:
        return cached

    log = log or (lambda _message: None)
    log("Loading MindVisualizer field samplers and brain label grid.")
    svc = services()
    traj = np.asarray(req.trajectory, dtype=np.float32)
    if traj.ndim != 2 or traj.shape[1] != 3:
        raise HTTPException(status_code=400, detail="trajectory must be an array of [x,y,z] points")

    log(f"Received {len(traj)} normalized probe samples in {req.fieldMode} mode.")
    world = denormalized(np.clip(traj, 0.0, 1.0), svc["amin"], svc["span"])
    sampler = svc["samplers"].get(req.fieldMode, svc["samplers"]["mean"])
    mags = np.linalg.norm(sampler.sample_vec(world), axis=1).astype(np.float32)
    log("Sampling flow strength and entropy along the traveled path.")
    transitions = analyze_regions(world, mags, req.callLlm)
    log(f"Detected {len(transitions)} entered or nearby anatomical regions.")
    text = format_transition_text(transitions)

    if req.callLlm:
        if request is not None:
            ensure_free_budget(request)
        from src.region_analyzer import analyze_with_gpt

        log("Calling the OpenAI model for the neuroscience interpretation.")
        text = analyze_with_gpt(
            transitions,
            use_rag=os.environ.get("MINDVIS_USE_RAG", "0") == "1",
            model=os.environ.get("OPENAI_MODEL", "gpt-5.4-mini"),
            debug=os.environ.get("MINDVIS_DEBUG", "0") == "1",
        )
        ensure_not_budget_error(text)
        log("LLM interpretation finished.")

    result = {"text": text, "transitions": transitions, "regionText": format_transition_text(transitions)}
    write_explanation_cache(req, result)
    return result


def cache_key(req: ExplainRequest) -> Path:
    payload = req.model_dump()
    digest = hashlib.sha256(json.dumps(payload, sort_keys=True).encode("utf-8")).hexdigest()
    return CACHE_DIR / f"mindvis-{digest}.json"


def cached_explanation(req: ExplainRequest) -> dict | None:
    path = cache_key(req)
    if not path.exists():
        return None
    try:
        return json.loads(path.read_text(encoding="utf-8"))
    except Exception:
        return None


def write_explanation_cache(req: ExplainRequest, result: dict) -> None:
    if not req.callLlm:
        return
    try:
        cache_key(req).write_text(json.dumps(result, ensure_ascii=False), encoding="utf-8")
    except Exception:
        pass


def ensure_free_budget(request: Request) -> None:
    if not client_budget_available(request):
        raise HTTPException(status_code=429, detail={"error": "client_free_limit_exhausted"})
    if not daily_budget_available():
        raise HTTPException(status_code=429, detail={"error": "budget_exhausted"})


def daily_budget_available() -> bool:
    limit = int(os.environ.get("MINDVIS_DAILY_REQUEST_LIMIT", "80"))
    if limit <= 0:
        return False
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    counter_path = CACHE_DIR / f"counter-{today}.txt"
    try:
        current = int(counter_path.read_text()) if counter_path.exists() else 0
    except Exception:
        current = 0
    if current >= limit:
        return False
    counter_path.write_text(str(current + 1))
    return True


def client_budget_available(request: Request) -> bool:
    limit = int(os.environ.get("MINDVIS_FREE_EXPLAINS_PER_CLIENT", "2"))
    if limit <= 0:
        return False
    forwarded_for = request.headers.get("x-forwarded-for", "").split(",")[0].strip()
    host = forwarded_for or (request.client.host if request.client else "unknown")
    user_agent = request.headers.get("user-agent", "unknown")[:180]
    client_id = request.headers.get("x-mindvis-client-id", "unknown")[:120]
    client_hash = hashlib.sha256(f"{host}|{user_agent}|{client_id}".encode("utf-8")).hexdigest()[:16]
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    counter_path = CACHE_DIR / f"client-{today}-{client_hash}.txt"
    try:
        current = int(counter_path.read_text()) if counter_path.exists() else 0
    except Exception:
        current = 0
    if current >= limit:
        return False
    counter_path.write_text(str(current + 1))
    return True


def ensure_not_budget_error(text: str) -> None:
    msg = (text or "").lower()
    if any(token in msg for token in ["insufficient_quota", "quota", "billing", "rate limit", "[gpt error]"]):
        raise HTTPException(status_code=429, detail={"error": "openai_budget_exhausted"})


def integrate_probe(req: TrajectoryRequest, svc: dict) -> tuple[np.ndarray, np.ndarray, bool]:
    p = denormalized(np.asarray(req.seed[:3], dtype=np.float32)[None, :], svc["amin"], svc["span"])
    sampler = svc["samplers"].get(req.fieldMode, svc["samplers"]["mean"])
    step = req.dt * req.speedScale * (svc["target_step"] / max(svc["vmax"].get(req.fieldMode, 1.0), 1e-9))
    path = []
    mags = []
    stopped = False

    for _ in range(req.steps):
        path.append(p[0].copy())
        velocity = sampler.sample_vec(p)[0]
        mags.append(float(np.linalg.norm(velocity)))
        new_pos = np.clip(p[0] + velocity * step, svc["amin"], svc["amax"])

        # Original probe boundary behavior: if a step leaves labelled brain
        # space, try half-step; otherwise stop instead of wrapping.
        if not is_valid_probe_position(new_pos, svc["mesh"]):
            half = np.clip(p[0] + velocity * step * 0.5, svc["amin"], svc["amax"])
            if is_valid_probe_position(half, svc["mesh"]):
                new_pos = half
            else:
                stopped = True
                break

        if np.linalg.norm(new_pos - p[0]) < 1e-5:
            stopped = True
            break
        p[0] = new_pos.astype(np.float32)

    return np.asarray(path, dtype=np.float32), np.asarray(mags, dtype=np.float32), stopped


def is_valid_probe_position(point: np.ndarray, mesh) -> bool:
    key = mesh.get_region_at_point(point)
    if key is None:
        key = mesh.find_nearest_region(point, search_radius=2, max_distance_mm=5.0)
    return key is not None


def analyze_regions(world: np.ndarray, mags: np.ndarray, call_llm: bool) -> list[dict]:
    svc = services()
    from src.region_analyzer import analyze_probe_path

    transitions = analyze_probe_path(
        world,
        svc["mesh"],
        sample_every=5,
        field_mags=mags,
        entropy_sampler=svc["entropy_sampler"],
        entropy_field=svc["entropy"],
    )
    return make_json_safe(transitions)


def format_transition_text(transitions: list[dict]) -> str:
    from src.region_analyzer import format_transitions_text

    return format_transitions_text(transitions)


def denormalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.ndarray:
    return points.astype(np.float32) * span[None, :] + amin[None, :]


def normalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.ndarray:
    return (points.astype(np.float32) - amin[None, :]) / span[None, :]


def make_json_safe(value):
    if isinstance(value, np.ndarray):
        return value.tolist()
    if isinstance(value, np.generic):
        return value.item()
    if isinstance(value, dict):
        return {str(k): make_json_safe(v) for k, v in value.items() if not str(k).startswith("_")}
    if isinstance(value, list):
        return [make_json_safe(v) for v in value]
    if isinstance(value, tuple):
        return [make_json_safe(v) for v in value]
    return value