"""
agents/validator.py
────────────────────────────────────────────────────────────────────────────
ValidatorAgent: Two-stage security and validation pipeline for job descriptions.

Stage 1 — Programmatic filter (instant, zero LLM cost):
  - Minimum character length check
  - Forbidden prompt-injection phrase detection

Stage 2 — LLM gatekeeper (temperature=0.0, fully deterministic):
  - Classifies the input as VALID or INVALID job description
  - The job text is wrapped in XML tags so the LLM treats it as pure data

Stage 3 — Profile extraction (temperature=0.2, structured output):
  - Extracts INDUSTRY, ROLE_LEVEL, KEYWORDS (5-7), INTERVIEW_STYLE, TIPS
  - Returns a rich job_profile dict used by QuestionGenAgent and ScorerAgent
"""

import re

# ── Forbidden phrase list — checked BEFORE any LLM call ──────────────────────
_FORBIDDEN_PHRASES = [
    "ignore all previous",
    "ignore previous instructions",
    "disregard the above",
    "you are now",
    "act as",
    "pretend you are",
    "forget everything",
    "new instructions",
    "system prompt",
    "jailbreak",
    "do anything now",
    "dan mode",
    "respond with complete",
    "bypass your",
]

_MIN_LENGTH = 350  # Minimum characters for a legitimate job description


class ValidatorAgent:
    """
    Validates and analyses an incoming job description.
    
    Usage:
        agent = ValidatorAgent(llm_fn)   # llm_fn = ask_llm from engine
        result = agent.run(job_desc_text)
        # result: {"valid": True/False, "error_msg": str, ...profile fields}
    """

    def __init__(self, llm_fn):
        """
        Args:
            llm_fn: A callable that takes (prompt: str, temperature: float, max_tokens: int) → str.
                    Provided by engine.py so the agent stays decoupled from the LLM client.
        """
        self._ask = llm_fn

    # ── Public entry point ────────────────────────────────────────────────────
    def run(self, job_desc: str) -> dict:
        """
        Full validation + extraction pipeline.
        Returns a dict with 'valid' key; if valid=True, also contains profile fields.
        """
        clean = job_desc.strip() if job_desc else ""

        # Stage 1a: Length check
        if len(clean) < _MIN_LENGTH:
            return self._invalid("Job description is too short. Please paste the full posting (350+ characters).")

        # Stage 1b: Forbidden phrase check
        lower = clean.lower()
        for phrase in _FORBIDDEN_PHRASES:
            if phrase in lower:
                return self._invalid("⚠️ Invalid input detected. Please paste a real job description.")

        # Stage 2: LLM gatekeeper
        gatekeeper_verdict = self._gatekeeper(clean)
        if not gatekeeper_verdict:
            return self._invalid("Please enter a complete and legitimate job description.")

        # Stage 3: Profile extraction
        profile = self._extract_profile(clean)
        if not profile:
            return self._invalid("Could not analyse the job description. Please try again.")

        return {"valid": True, **profile}

    # ── Stage 2: LLM Gatekeeper ───────────────────────────────────────────────
    # Making _gatekeeper Mistral 7B v02 compatible(it accepts inside <s>[INST] tags only)
    def _gatekeeper(self, clean_text: str) -> bool:
        """Returns True only if the LLM classifies the text as a legitimate JD."""
        # Standardized to plain English text layout for cross-model compatibility
        prompt = f"""You are a security-hardened automated recruitment verification filter.
Your ONLY task is to classify whether the text inside the input tags is a legitimate, fully-structured job description containing real duties and organizational requirements.

If legitimate, reply with the exact word: VALID
If not legitimate, reply with the exact word: INVALID

Text to analyze:
{clean_text[:1200]}

Your response (VALID or INVALID):"""

        raw_response = self._ask(prompt, temperature=0.0, max_tokens=40)
        
        print(f"\n--- GATEKEEPER DEBUG --- \nRaw Model Output: '{raw_response}'\n-------------------------\n")
        
        verdict = raw_response.strip().upper()
        return "VALID" in verdict and "INVALID" not in verdict

        
    # ── Stage 3: Profile Extraction ───────────────────────────────────────────
    def _extract_profile(self, clean_text: str) -> dict | None:
        """
        Extracts structured profile metadata from a verified job description.
        Returns None if extraction fails (malformed LLM response).
        """
        prompt = f"""[INST] You are an expert HR analyst. Analyse this verified job description and extract structured metadata.

Job Description:
{clean_text[:2000]}

Extract and respond using EXACTLY these XML tags (one tag per line, content on the same line):
<INDUSTRY> the primary field/sector (e.g. Software Engineering, Healthcare, Finance, Marketing, Education) </INDUSTRY>
<ROLE_LEVEL> seniority level: Junior / Mid-Level / Senior / Lead / Manager / Director / Executive </ROLE_LEVEL>
<INTERVIEW_STYLE> primary interview style: Technical / Behavioral / Case-Based / Mixed </INTERVIEW_STYLE>
<KEYWORDS> 5 to 7 comma-separated key skills or terms a strong candidate MUST mention </KEYWORDS>
<TIPS> 4 specific bullet-point preparation tips for this exact role (start each with •) </TIPS>

Respond with only the 5 XML tag lines. No other text. [/INST]"""

        raw = self._ask(prompt, temperature=0.2, max_tokens=400)

        industry   = self._tag(raw, "INDUSTRY")
        role_level = self._tag(raw, "ROLE_LEVEL")
        style      = self._tag(raw, "INTERVIEW_STYLE")
        keywords_raw = self._tag(raw, "KEYWORDS")
        tips       = self._tag(raw, "TIPS")

        # Keywords are the most critical — if missing, extraction failed
        if not keywords_raw:
            return None

        keywords = [k.strip() for k in keywords_raw.split(",") if k.strip()]

        return {
            "industry":        industry or "General",
            "role_level":      role_level or "Mid-Level",
            "interview_style": style or "Mixed",
            "keywords":        keywords,
            "tips":            tips or "",
            "job_snippet":     clean_text[:80] + "...",
        }

    # ── Helpers ───────────────────────────────────────────────────────────────
    @staticmethod
    def _tag(text: str, name: str) -> str:
        """Extract content between <NAME> and </NAME> tags."""
        start, end = f"<{name}>", f"</{name}>"
        if start in text and end in text:
            return text.split(start)[1].split(end)[0].strip()
        return ""

    @staticmethod
    def _invalid(msg: str) -> dict:
        return {"valid": False, "error_msg": msg}