/**
 * Auralynq ModelFit Index — API client
 */

import { consumeSSE } from "@/lib/sse";

const API_BASE = process.env.NEXT_PUBLIC_API_BASE ?? "http://localhost:8000";

export interface GPUInfo {
  vendor: string;
  name: string;
  vram_gb: number;
  backend: string;
  device_index: number;
  vram_free_gb?: number | null;
  vram_used_gb?: number | null;
  integrated?: boolean;
}

export interface HardwareProfile {
  os: { name: string; version: string };
  python_version: string;
  cpu: {
    model: string;
    cores_physical: number;
    cores_logical: number;
    arch?: string;
    avx2?: boolean;
    avx512?: boolean;
  };
  ram_gb: number;
  gpus: GPUInfo[];
  total_vram_gb: number;
  total_vram_free_gb?: number | null;
  disk_free_gb: number;
  best_backend: string;
  cuda_available: boolean;
  cuda_version: string | null;
  metal_available: boolean;
  rocm_available: boolean;
  avx2?: boolean;
  avx512?: boolean;
  ollama_available: boolean;
  ollama_version: string | null;
  hf_available: boolean;
  hf_cache_path: string | null;
  in_container: boolean;
  warnings: string[];
}

/** UI-facing verdict from the resource estimator (see resource_estimator.py). */
export type Verdict =
  | "runs_great"
  | "runs_ok"
  | "runs_offload"
  | "runs_cpu"
  | "runs_cpu_tight"
  | "too_big";

export interface ModelMeta {
  model_id: string;
  source: string;
  display_name: string;
  family: string;
  parameter_count_b: number | null;
  context_length: number | null;
  license: string;
  gated: boolean;
  tasks: string[];
  available_quantizations: string[];
  embedding: boolean;
  reranker: boolean;
  supports_adapters: boolean;
  vision: boolean;
  tool_calling: boolean;
  multilingual: boolean;
  ollama_tag: string | null;
  hf_repo: string | null;
  local_path: string | null;
  notes: string[];
}

export interface ResourceEstimate {
  model_id: string;
  quantization: string;
  context_tokens: number;
  estimated_vram_gb: number;
  estimated_ram_gb: number;
  estimated_disk_gb: number;
  fit_level: "comfortable" | "tight" | "not_recommended" | "impossible";
  fits: boolean;
  recommended_context: number;
  peak_vram_at_max_ctx_gb: number;
  verdict?: Verdict;
  fits_in_vram?: boolean;
  requires_cpu_offload?: boolean;
  headroom_gb?: number;
  vram_free_gb?: number | null;
  warnings: string[];
  is_estimate: true;
}

export interface ModelFitScore {
  model_id: string;
  overall_score: number;
  hardware_fit: number;
  speed_fit: number;
  rag_fit: number;
  task_fit: number;
  deployment_fit: number;
  label: string;
  best_quantization: string;
  reason: string;
  resource_estimate: ResourceEstimate | null;
  benchmark: BenchmarkResult | null;
  estimate_used: boolean;
  warnings: string[];
}

export interface BenchmarkPlan {
  model_id: string;
  quantization: string;
  task: string;
  num_examples: number;
  estimated_duration_min: number;
  sample_prompts: string[];
  requires_ollama: boolean;
  requires_model_download: false;
  warnings: string[];
  note: string;
}

export interface BenchmarkResult {
  run_id: string;
  model_id: string;
  quantization: string;
  task: string;
  status: "pending" | "running" | "completed" | "failed" | "cancelled";
  hardware: Partial<HardwareProfile>;
  avg_tok_per_sec: number | null;
  p50_latency_ms: number | null;
  p95_latency_ms: number | null;
  time_to_first_token_ms: number | null;
  peak_memory_gb: number | null;
  num_examples: number;
  completed_examples: number;
  rag_metrics: {
    citation_coverage: number | null;
    groundedness: number | null;
    abstention_accuracy: number | null;
  };
  error: string | null;
  started_at: string;
  completed_at: string | null;
  warnings: string[];
  is_measured: boolean;
}

async function apiFetch<T>(path: string, init?: RequestInit): Promise<T> {
  const res = await fetch(`${API_BASE}${path}`, {
    headers: { "Content-Type": "application/json" },
    ...init,
  });
  if (!res.ok) {
    const body = await res.text().catch(() => "");
    throw new Error(extractApiError(body) ?? `API ${path} → ${res.status}: ${body}`);
  }
  return res.json();
}

/**
 * Pull the human sentence out of the API's error envelope.
 *
 * Without this the UI printed the raw `{"error":{"code":"http_error",...}}` JSON
 * at the user, which is how a missing binary surfaced as an unreadable blob.
 */
export function extractApiError(body: string): string | null {
  try {
    const parsed = JSON.parse(body);
    const msg = parsed?.error?.message ?? parsed?.detail ?? parsed?.message;
    return typeof msg === "string" && msg.trim() ? msg : null;
  } catch {
    return null;
  }
}

// Hardware
export async function fetchHardware(): Promise<HardwareProfile> {
  return apiFetch("/api/modelfit/hardware");
}

// Models
export async function fetchModels(params?: {
  source?: string;
  family?: string;
  task?: string;
  embedding_only?: boolean;
  open_license?: boolean;
  q?: string;
  limit?: number;
}): Promise<{ models: ModelMeta[]; total: number }> {
  const qs = new URLSearchParams();
  if (params?.source) qs.set("source", params.source);
  if (params?.family) qs.set("family", params.family);
  if (params?.task) qs.set("task", params.task);
  if (params?.embedding_only) qs.set("embedding_only", "true");
  if (params?.open_license) qs.set("open_license", "true");
  if (params?.q) qs.set("q", params.q);
  if (params?.limit) qs.set("limit", String(params.limit));
  return apiFetch(`/api/modelfit/models?${qs}`);
}

export async function fetchInstalledModels(): Promise<{ models: ModelMeta[]; total: number; warnings: string[] }> {
  return apiFetch("/api/modelfit/models/installed");
}

// Estimate
export async function fetchEstimate(
  model_id: string,
  params_b: number,
  quantization: string,
  context_tokens = 4096,
): Promise<ResourceEstimate> {
  return apiFetch("/api/modelfit/estimate", {
    method: "POST",
    body: JSON.stringify({ model_id, params_b, quantization, context_tokens }),
  });
}

// Score
export async function fetchScore(
  model_id: string,
  quantization?: string,
  requested_tasks?: string[],
): Promise<ModelFitScore> {
  return apiFetch("/api/modelfit/score", {
    method: "POST",
    body: JSON.stringify({ model_id, quantization, requested_tasks: requested_tasks ?? [] }),
  });
}

// Recommendations
export async function fetchRecommendations(task?: string, limit = 5): Promise<{
  recommendations: ModelFitScore[];
  hardware_summary: { ram_gb: number; total_vram_gb: number; best_backend: string };
  task: string | null;
}> {
  const qs = new URLSearchParams();
  if (task) qs.set("task", task);
  qs.set("limit", String(limit));
  return apiFetch(`/api/modelfit/recommendations?${qs}`);
}

// Benchmark
export async function fetchBenchmarkPreview(
  model_id: string,
  quantization: string,
  task: string,
  num_examples: number,
): Promise<BenchmarkPlan> {
  return apiFetch("/api/modelfit/benchmark/preview", {
    method: "POST",
    body: JSON.stringify({ model_id, quantization, task, num_examples, confirmed: false }),
  });
}

export async function runBenchmark(
  model_id: string,
  quantization: string,
  task: string,
  num_examples: number,
): Promise<BenchmarkResult> {
  return apiFetch("/api/modelfit/benchmark/run", {
    method: "POST",
    body: JSON.stringify({ model_id, quantization, task, num_examples, confirmed: true }),
  });
}

export async function fetchBenchmarkRuns(): Promise<{ runs: BenchmarkResult[]; total: number }> {
  return apiFetch("/api/modelfit/benchmark/runs");
}

export async function fetchBenchmarkRun(run_id: string): Promise<BenchmarkResult> {
  return apiFetch(`/api/modelfit/benchmark/${run_id}`);
}

// ── Discover & Setup ──────────────────────────────────────────────────────────

export interface DiscoverHardware {
  os: string;
  cpu: string;
  cpu_cores_physical?: number;
  cpu_cores_logical?: number;
  arch?: string;
  avx2?: boolean;
  avx512?: boolean;
  ram_gb: number;
  total_vram_gb: number;
  total_vram_free_gb?: number | null;
  gpus: GPUInfo[];
  best_backend: string;
  ollama_available: boolean;
  in_container?: boolean;
}

export interface DiscoverEntry {
  model_id: string;
  overall_score: number;
  hardware_fit: number;
  speed_fit: number;
  rag_fit: number;
  task_fit: number;
  deployment_fit: number;
  label: string;
  best_quantization: string;
  reason: string;
  resource_estimate: ResourceEstimate | null;
  benchmark: BenchmarkResult | null;
  estimate_used: boolean;
  warnings: string[];
  model_meta: ModelMeta;
  already_installed: boolean;
  pull_command: string | null;
  source: string;
}

export interface DiscoverResult {
  hardware: DiscoverHardware;
  task: string | null;
  total_candidates: number;
  recommendations: DiscoverEntry[];
  note: string;
}

export interface PullResult {
  status: string;
  model_id: string;
  message: string;
  local_path?: string;
  /** Ollama pulls only — the background job to stream progress from. */
  job_id?: string;
  stream_url?: string;
}

export type PullPhase = "queued" | "manifest" | "downloading" | "verifying" | "success" | "error";

export interface PullProgress {
  job_id: string;
  model_id: string;
  tag: string;
  phase: PullPhase;
  status_text: string;
  completed_bytes: number;
  total_bytes: number;
  percent: number;
  speed_bps: number;
  eta_s: number | null;
  layers_done: number;
  layers_total: number;
  message: string;
  error: string;
  error_status: number | null;
  elapsed_s: number;
}

export async function discoverModels(
  task: string | null,
  includeHf: boolean,
  refresh: boolean,
  limit = 30,
): Promise<DiscoverResult> {
  return apiFetch("/api/modelfit/discover", {
    method: "POST",
    body: JSON.stringify({ task, include_hf: includeHf, refresh, limit }),
  });
}

export async function pullModel(modelId: string): Promise<PullResult> {
  return apiFetch("/api/modelfit/pull", {
    method: "POST",
    body: JSON.stringify({ model_id: modelId, confirmed: true }),
  });
}

export async function fetchPullJob(jobId: string): Promise<PullProgress> {
  return apiFetch(`/api/modelfit/pull/${jobId}`);
}

/**
 * Stream a pull job's progress until it terminates.
 *
 * The download belongs to the job, not to this request — closing the stream (or
 * the modal) does not cancel it, and re-subscribing resumes from current state.
 */
export async function streamPullProgress(
  jobId: string,
  onProgress: (p: PullProgress) => void,
  signal?: AbortSignal,
): Promise<PullProgress> {
  const res = await fetch(`${API_BASE}/api/modelfit/pull/${jobId}/stream`, {
    headers: { Accept: "text/event-stream" },
    signal,
  });
  if (!res.ok || !res.body) {
    const body = await res.text().catch(() => "");
    throw new Error(extractApiError(body) ?? `Could not follow pull ${jobId}.`);
  }

  const reader = res.body.getReader();
  const decoder = new TextDecoder();
  let buffer = "";
  let last: PullProgress | null = null;

  for (;;) {
    const { done, value } = await reader.read();
    if (done) break;
    buffer += decoder.decode(value, { stream: true });
    const { events, rest } = consumeSSE<PullProgress>(buffer);
    buffer = rest;
    for (const ev of events) {
      last = ev;
      onProgress(ev);
      if (ev.phase === "success" || ev.phase === "error") {
        void reader.cancel().catch(() => {});
        return ev;
      }
    }
  }

  // Stream closed without a terminal frame — ask the job directly.
  return last?.phase === "success" || last?.phase === "error" ? last : fetchPullJob(jobId);
}

export function formatBytes(bytes: number): string {
  if (!bytes) return "0 B";
  const units = ["B", "KB", "MB", "GB", "TB"];
  const i = Math.min(units.length - 1, Math.floor(Math.log(bytes) / Math.log(1024)));
  return `${(bytes / 1024 ** i).toFixed(i >= 2 ? 1 : 0)} ${units[i]}`;
}

export function formatEta(seconds: number | null): string {
  if (seconds == null || !Number.isFinite(seconds) || seconds <= 0) return "—";
  if (seconds < 60) return `${Math.round(seconds)}s`;
  const m = Math.floor(seconds / 60);
  const s = Math.round(seconds % 60);
  return m < 60 ? `${m}m ${s}s` : `${Math.floor(m / 60)}h ${m % 60}m`;
}