// Input normalizer — accepts whatever shape a caller provides (clean
// structured spec, partial JSON, natural language, a mix) and produces
// a canonical shape the rest of the pipeline understands. This is the
// missing piece that lets KB + playbook_memory + overseer respond
// "seamlessly with whatever input they're given" as J framed it.
//
// The pipeline already works when input is `{role, city, state, count}`;
// what breaks today is "I need three welders in Nashville Monday" —
// the SQL-filter extractors return None, the boost pipeline sees no
// role/geo signal, and retrieval silently degrades to raw cosine.
//
// Strategy: fast path for structured input, LLM fallback only for
// natural language. We don't want to burn an LLM call on "role=Welder
// city=Nashville state=TN count=3" — that's wasteful.

import { generateContinuable } from "./agent.ts";

export interface NormalizedInput {
  role: string | null;
  city: string | null;
  state: string | null;         // two-letter
  count: number | null;
  client: string | null;
  deadline: string | null;       // ISO date or null
  intent: "fill" | "lookup" | "rescue" | "rebuild" | "unknown";
  raw_text: string;              // what the caller originally sent
  confidence: "high" | "medium" | "low";
  extraction_method: "structured" | "regex" | "llm" | "mixed";
  missing_fields: string[];      // what normalizer couldn't determine
}

// US state abbreviations for regex city,state extraction.
const STATE_ABBRS = new Set([
  "AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN","IA",
  "KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
  "NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT",
  "VA","WA","WV","WI","WY","DC",
]);

const STATE_NAMES_TO_ABBR: Record<string, string> = {
  alabama: "AL", alaska: "AK", arizona: "AZ", arkansas: "AR", california: "CA",
  colorado: "CO", connecticut: "CT", delaware: "DE", florida: "FL", georgia: "GA",
  hawaii: "HI", idaho: "ID", illinois: "IL", indiana: "IN", iowa: "IA",
  kansas: "KS", kentucky: "KY", louisiana: "LA", maine: "ME", maryland: "MD",
  massachusetts: "MA", michigan: "MI", minnesota: "MN", mississippi: "MS", missouri: "MO",
  montana: "MT", nebraska: "NE", nevada: "NV", "new hampshire": "NH", "new jersey": "NJ",
  "new mexico": "NM", "new york": "NY", "north carolina": "NC", "north dakota": "ND",
  ohio: "OH", oklahoma: "OK", oregon: "OR", pennsylvania: "PA", "rhode island": "RI",
  "south carolina": "SC", "south dakota": "SD", tennessee: "TN", texas: "TX", utah: "UT",
  vermont: "VT", virginia: "VA", washington: "WA", "west virginia": "WV", wisconsin: "WI",
  wyoming: "WY",
};

// Canonical staffing roles — the normalizer recognizes these regardless
// of casing / plurality. Extend as domains grow.
const KNOWN_ROLES = [
  "Warehouse Associate", "Machine Operator", "Forklift Operator", "Loader",
  "Material Handler", "Assembler", "Quality Tech", "Picker", "Packer",
  "Shipping Clerk", "Receiving Clerk", "Welder", "CNC Operator",
  "Maintenance Tech", "Electrician", "Tool & Die Maker", "Safety Coordinator",
  "Logistics Coordinator", "Packaging Operator", "Sanitation Worker",
  "Line Lead",
];

// Intent keyword hints. Check in order — "rescue" beats generic "fill"
// for "rescue the Nashville welder fill" kind of input.
const INTENT_HINTS: Array<{ re: RegExp; intent: NormalizedInput["intent"] }> = [
  { re: /\brescue|pivot|retry|remediat/i,       intent: "rescue" },
  { re: /\brebuild|reindex|re-?embed/i,         intent: "rebuild" },
  { re: /\blookup|find|show me|who is|what is/i, intent: "lookup" },
  { re: /\bfill|hire|staff|need|schedule|book/i, intent: "fill" },
];

// Fast structured path — the input is already a FillEvent-shaped JSON
// or a close cousin. Returns null to fall through to regex/LLM.
function fromStructured(raw: any): NormalizedInput | null {
  if (!raw || typeof raw !== "object") return null;
  const role = typeof raw.role === "string" ? raw.role : null;
  const city = typeof raw.city === "string" ? raw.city : null;
  const stateRaw = typeof raw.state === "string" ? raw.state : null;
  const count = typeof raw.count === "number" ? raw.count : null;
  // Only treat as structured if AT LEAST role OR city is present; a
  // bare {kind: "baseline_fill"} shouldn't go through this path.
  if (!role && !city) return null;
  return {
    role,
    city,
    state: normalizeState(stateRaw),
    count,
    client: typeof raw.client === "string" ? raw.client : null,
    deadline: typeof raw.deadline === "string" ? raw.deadline : null,
    intent: (raw.kind === "misplacement" || /rescue/i.test(raw.kind ?? "")) ? "rescue" : "fill",
    raw_text: JSON.stringify(raw),
    confidence: "high",
    extraction_method: "structured",
    missing_fields: missingFields(role, city, stateRaw, count),
  };
}

function normalizeState(s: string | null | undefined): string | null {
  if (!s) return null;
  const trimmed = s.trim();
  if (STATE_ABBRS.has(trimmed.toUpperCase())) return trimmed.toUpperCase();
  const full = trimmed.toLowerCase();
  if (STATE_NAMES_TO_ABBR[full]) return STATE_NAMES_TO_ABBR[full];
  return null;
}

function missingFields(role: any, city: any, state: any, count: any): string[] {
  const out: string[] = [];
  if (!role) out.push("role");
  if (!city) out.push("city");
  if (!state) out.push("state");
  if (count === null || count === undefined) out.push("count");
  return out;
}

// Regex path — handles structured-ish text like "3 welders in Nashville TN".
// Returns partial extraction; LLM path fills the rest if this leaves
// too many missing fields.
function fromRegex(text: string): NormalizedInput {
  const lower = text.toLowerCase();

  // Count: "3 welders" or "need 5" or "fill 8 safety coordinators"
  let count: number | null = null;
  const countMatch = text.match(/\b(?:need|fill|book|hire|schedule|want)\s+(\d+)\b/i)
                  ?? text.match(/\b(\d+)\s+(?:x\s+)?[A-Za-z]/);
  if (countMatch) count = parseInt(countMatch[1], 10);

  // Role: longest prefix match from KNOWN_ROLES (case-insensitive).
  // Plural forms ("welders") match "Welder" via endsWith("s") strip.
  let role: string | null = null;
  for (const known of KNOWN_ROLES.sort((a, b) => b.length - a.length)) {
    const needle = known.toLowerCase();
    if (lower.includes(needle)) { role = known; break; }
    if (needle.endsWith("r") && lower.includes(needle + "s")) { role = known; break; }
  }

  // City, state: "Nashville TN" or "Nashville, Tennessee" or "Nashville, TN".
  // City capture is 1-3 capitalized words — anything longer is usually
  // the surrounding phrase ("Forklift Operators in Chicago"). Prefer
  // "in {city}" anchor when present.
  let city: string | null = null;
  let state: string | null = null;
  const cityPat = `([A-Z][a-zA-Z.'-]+(?:\\s+[A-Z][a-zA-Z.'-]+){0,2})`;
  // First preference: "in {City}, XX" or "in {City} XX"
  const inAbbrev = text.match(new RegExp(`\\bin\\s+${cityPat},?\\s+([A-Z]{2})\\b`));
  if (inAbbrev && STATE_ABBRS.has(inAbbrev[2])) {
    city = inAbbrev[1].trim();
    state = inAbbrev[2];
  } else {
    // Bare "{City}, XX" (no "in" anchor)
    const bareAbbrev = text.match(new RegExp(`${cityPat},\\s+([A-Z]{2})\\b`));
    if (bareAbbrev && STATE_ABBRS.has(bareAbbrev[2])) {
      city = bareAbbrev[1].trim();
      state = bareAbbrev[2];
    } else {
      // "{City}, FullName" variant. Can't use case-insensitive flag
      // because it would let cityPat's [A-Z] match lowercase letters.
      // Instead try both exact-case and Title-case variants.
      for (const [full, abbr] of Object.entries(STATE_NAMES_TO_ABBR)) {
        const fullTitle = full.split(" ").map(w => w[0].toUpperCase() + w.slice(1)).join(" ");
        const variants = [full, fullTitle];
        for (const variant of variants) {
          const re = new RegExp(`\\bin\\s+${cityPat},?\\s+${variant}\\b`);
          const m = text.match(re);
          if (m) { city = m[1].trim(); state = abbr; break; }
        }
        if (city) break;
        // Bare "{City}, FullName" (no "in")
        for (const variant of variants) {
          const re = new RegExp(`${cityPat},\\s+${variant}\\b`);
          const m = text.match(re);
          if (m) { city = m[1].trim(); state = abbr; break; }
        }
        if (city) break;
      }
    }
  }

  // Intent
  let intent: NormalizedInput["intent"] = "unknown";
  for (const h of INTENT_HINTS) {
    if (h.re.test(text)) { intent = h.intent; break; }
  }

  const missing = missingFields(role, city, state, count);
  return {
    role, city, state, count,
    client: null,
    deadline: null,
    intent,
    raw_text: text,
    confidence: missing.length === 0 ? "high" : missing.length <= 2 ? "medium" : "low",
    extraction_method: "regex",
    missing_fields: missing,
  };
}

// LLM path — when regex leaves too many fields missing, ask a small
// local model to extract what's present. Uses `think:false` and a
// strict JSON prompt so the call stays cheap.
async function fromLLM(text: string, fallback: NormalizedInput): Promise<NormalizedInput> {
  const prompt = `Extract the staffing request shape from this text. Respond with ONLY a JSON object, no prose.

Text: ${text}

Required shape:
{
  "role": "string | null (one of: Welder, Forklift Operator, Warehouse Associate, Machine Operator, Loader, Material Handler, Assembler, Quality Tech, Picker, Packer, Shipping Clerk, Receiving Clerk, CNC Operator, Maintenance Tech, Electrician, Tool & Die Maker, Safety Coordinator, Logistics Coordinator, Packaging Operator, Sanitation Worker, Line Lead, or null if unclear)",
  "city": "string | null (US city name, properly capitalized)",
  "state": "string | null (two-letter abbrev like TN, IL)",
  "count": "number | null (integer number of workers needed)",
  "client": "string | null (company/client name if mentioned)",
  "deadline": "string | null (ISO date YYYY-MM-DD if a specific date is mentioned, else null)",
  "intent": "string (one of: fill, lookup, rescue, rebuild, unknown)"
}

Return the JSON object now:`;

  try {
    const raw = await generateContinuable("qwen3:latest", prompt, {
      max_tokens: 400,
      shape: "json",
      think: false,
      max_continuations: 2,
    });
    const match = raw.match(/\{[\s\S]*\}/);
    if (!match) return fallback;
    const parsed = JSON.parse(match[0]);
    const role = typeof parsed.role === "string" && parsed.role !== "null" ? parsed.role : null;
    const city = typeof parsed.city === "string" && parsed.city !== "null" ? parsed.city : null;
    const state = normalizeState(parsed.state);
    const count = typeof parsed.count === "number" ? parsed.count
                : typeof parsed.count === "string" ? parseInt(parsed.count, 10) || null
                : null;
    const client = typeof parsed.client === "string" && parsed.client !== "null" ? parsed.client : null;
    const deadline = typeof parsed.deadline === "string" && parsed.deadline !== "null" ? parsed.deadline : null;
    const intent = ["fill", "lookup", "rescue", "rebuild", "unknown"].includes(parsed.intent)
      ? parsed.intent as NormalizedInput["intent"]
      : "unknown";
    const missing = missingFields(role, city, state, count);
    return {
      role: role ?? fallback.role,
      city: city ?? fallback.city,
      state: state ?? fallback.state,
      count: count ?? fallback.count,
      client: client ?? fallback.client,
      deadline: deadline ?? fallback.deadline,
      intent: intent !== "unknown" ? intent : fallback.intent,
      raw_text: text,
      confidence: missing.length === 0 ? "high" : missing.length <= 2 ? "medium" : "low",
      extraction_method: "llm",
      missing_fields: missing,
    };
  } catch {
    return fallback;
  }
}

// Top-level normalizer. Auto-detects input shape:
//   - Object with role/city → fast structured path
//   - String with enough regex signal → regex path
//   - Low-signal string → LLM path with regex seed
export async function normalizeInput(raw: unknown): Promise<NormalizedInput> {
  // Structured path
  if (raw && typeof raw === "object") {
    const s = fromStructured(raw);
    if (s) return s;
  }
  // String path
  const text = typeof raw === "string" ? raw
             : raw && typeof raw === "object" ? JSON.stringify(raw)
             : String(raw);
  const reg = fromRegex(text);
  // If regex got at least role + city, trust it.
  if (reg.role && reg.city) {
    return reg;
  }
  // Otherwise try LLM fallback, using regex result as seed.
  return await fromLLM(text, reg);
}

// Synchronous-only variant — returns whatever regex/structured can get
// without an LLM call. Useful when caller can't await (e.g. a prompt
// template).
export function normalizeInputSync(raw: unknown): NormalizedInput {
  if (raw && typeof raw === "object") {
    const s = fromStructured(raw);
    if (s) return s;
  }
  const text = typeof raw === "string" ? raw
             : raw && typeof raw === "object" ? JSON.stringify(raw)
             : String(raw);
  return fromRegex(text);
}