import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

// ─── Intent types ─────────────────────────────────────────────────────────────

export type InternalChatIntent =
  | "check_eligibility"     // by patient name → look up in DB
  | "eligibility_by_id"     // by explicit memberId + dob (no name)
  | "check_and_claim"       // eligibility + claim procedures
  | "find_patient"          // look up patient record only
  | "navigate_claims"
  | "navigate_schedule"
  | "general";

export interface ChatClassification {
  intent: InternalChatIntent;
  // --- patient resolution (one of name OR id+dob) ---
  patientName?: string;       // for check_eligibility / find_patient
  memberId?: string;          // for eligibility_by_id / check_and_claim
  dob?: string;               // for eligibility_by_id / check_and_claim (MM/DD/YYYY)
  // --- insurance hint (only if explicitly stated in the message) ---
  insuranceHint?: string;     // raw text, e.g. "masshealth", "BCBS", "CCA"
  // --- procedures (raw text, NOT CDT codes — CDT lookup is done in workflow) ---
  procedureNames?: string[];  // for check_and_claim, e.g. ["perio exam", "adult cleaning"]
  fallbackReply: string;
}

// ─── System prompt ────────────────────────────────────────────────────────────

const BASE_SYSTEM_PROMPT = `You are an internal assistant for a dental office management app.
Staff type natural language commands. Your ONLY job is to classify the intent and extract
structured parameters. Do NOT map procedure names to CDT codes — return them as plain text.

Respond ONLY with valid JSON (no markdown fences):
{
  "intent": "<intent>",
  "patientName": "<full name if mentioned by name>",
  "memberId": "<member/insurance ID if given explicitly>",
  "dob": "<date of birth in MM/DD/YYYY if given>",
  "insuranceHint": "<insurance name only if explicitly stated in the message, e.g. 'masshealth', 'BCBS MA', 'CCA'>",
  "procedureNames": ["<raw procedure name>", ...],
  "fallbackReply": "<1-2 sentence reply to show the user>"
}

Omit any field that is not present in the message.

Intents:
- check_eligibility  : user wants to check insurance for a patient identified by NAME only
                       e.g. "check Maria Jesus", "verify insurance for John Smith"
- eligibility_by_id  : user provides a member ID and date of birth (no patient name)
                       e.g. "check masshealth for 100xxxx, 10/10/1988"
- check_and_claim    : user wants to check eligibility AND submit procedures as claims
                       e.g. "check masshealth for 100xxxx, 10/10/1988 and claim perio exam and adult cleaning"
                       e.g. "check Maria Jesus and claim D0120 D1110"
- find_patient       : look up a patient record only, no eligibility
                       e.g. "find patient John", "look up Smith"
- navigate_claims    : open the claims page
- navigate_schedule  : open the appointments/schedule page
- general            : anything else

Rules:
- For check_and_claim, procedureNames should be the RAW user text
  (e.g. "perio exam", "adult cleaning", "D0120") — do NOT translate to codes
- insuranceHint is only set when the user explicitly names an insurance in the message
- Keep fallbackReply to 1-2 sentences
- For navigate intents, fallbackReply = "Opening the [page] page..."`;

// ─── Classifier ───────────────────────────────────────────────────────────────

export async function classifyInternalChat(
  message: string,
  apiKey: string,
  extraSystemPrompt?: string
): Promise<ChatClassification> {
  const fallback: ChatClassification = {
    intent: "general",
    fallbackReply:
      "I can search for a patient, check eligibility, run check & claim, or navigate to claims or appointments.",
  };

  if (!apiKey) return fallback;

  const systemPrompt = extraSystemPrompt
    ? `${BASE_SYSTEM_PROMPT}\n\nAdditional office context:\n${extraSystemPrompt}`
    : BASE_SYSTEM_PROMPT;

  try {
    const llm = new ChatGoogleGenerativeAI({ model: "gemini-1.5-flash", apiKey });
    const response = await llm.invoke([
      { role: "system", content: systemPrompt },
      { role: "user", content: message },
    ]);

    const raw = String(response.content).trim();
    const jsonStr = raw.replace(/^```json\s*/i, "").replace(/```\s*$/, "").trim();
    const parsed = JSON.parse(jsonStr) as ChatClassification;
    if (!parsed.intent || !parsed.fallbackReply) return fallback;
    return parsed;
  } catch {
    return fallback;
  }
}