"""
intelligence_pipeline.py

Updated to integrate:
- safety_classifier (request + output gating)
- slang detection
- transformer threat scoring
- optional OpenAI LLM fusion
- translation + explanation logic
"""

import json
import os
from typing import Dict, List, Optional

from prompt_engine import PromptEngine
from model_inference import ThreatModel
from safety_classifier import (
    safety_check,
    safety_check_output,
    detect_dangerous_intent,
)

try:
    from openai import OpenAI  # OpenAI Python SDK >= 1.0
except Exception:
    OpenAI = None


# ---------------------------------------------------------------------------
# Global initialization
# ---------------------------------------------------------------------------

engine = PromptEngine()

SLANG_LEXICON_PATH = os.path.join(os.path.dirname(__file__), "slang_lexicon.json")
if os.path.exists(SLANG_LEXICON_PATH):
    with open(SLANG_LEXICON_PATH, "r", encoding="utf-8") as f:
        SLANG_LEXICON: Dict[str, str] = json.load(f)
else:
    SLANG_LEXICON = {}

# Transformers model
MODEL_PATH = os.getenv("THREAT_MODEL_PATH", "bert-base-chinese")

try:
    threat_model = ThreatModel(MODEL_PATH)
except Exception:
    threat_model = None

# Optional LLM client
if OpenAI is not None and os.getenv("OPENAI_API_KEY"):
    try:
        llm_client = OpenAI()
    except Exception:
        llm_client = None
else:
    llm_client = None


# ---------------------------------------------------------------------------
# Utility: slang detection
# ---------------------------------------------------------------------------

def detect_slang(text: str) -> Dict[str, str]:
    hits = {}
    for term, meaning in SLANG_LEXICON.items():
        if term in text:
            hits[term] = meaning
    return hits


# ---------------------------------------------------------------------------
# Transformer risk scoring
# ---------------------------------------------------------------------------

def transformer_risk(text: str) -> Dict[str, float]:
    if threat_model is None:
        return {"risk_score": 0.0, "max_prob": 0.0}

    probs: List[float] = threat_model.predict_proba(text)
    if not probs:
        return {"risk_score": 0.0, "max_prob": 0.0}

    max_prob = max(probs)
    bucket = max(1, int(round(max_prob * 5))) if max_prob > 0 else 0

    return {"risk_score": float(bucket), "max_prob": float(max_prob)}


# ---------------------------------------------------------------------------
# LLM fusion logic
# ---------------------------------------------------------------------------

def llm_explain_threat(text: str, slang_hits: Dict[str, str], risk: Dict[str, float]) -> Optional[str]:
    if llm_client is None:
        return None

    slang_str = ", ".join(f"{k}: {v}" for k, v in slang_hits.items()) or "none"
    prompt = f"""
Analyze APJ underground market content with cultural nuance.

TEXT:
{text}

Signals:
- Risk bucket (1–5): {risk.get('risk_score', 0)}
- Classifier max probability: {risk.get('max_prob', 0.0):.3f}
- Detected slang: {slang_str}

Return:
- threat summary
- role of slang/idioms
- risk interpretation
"""

    try:
        response = llm_client.chat.completions.create(
            model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
            messages=[
                {"role": "system", "content": "You are a cyber threat intelligence analyst."},
                {"role": "user", "content": prompt},
            ],
            temperature=0.2,
        )
        return response.choices[0].message.content
    except Exception:
        return None


def llm_translate_explain(text: str) -> Optional[str]:
    if llm_client is None:
        return None

    prompt = engine.translate_explain(text)

    try:
        response = llm_client.chat.completions.create(
            model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
            messages=[
                {"role": "system", "content": "You are a bilingual Mandarin/Cantonese → English interpreter with cybercrime domain knowledge."},
                {"role": "user", "content": prompt},
            ],
            temperature=0.2,
        )
        return response.choices[0].message.content
    except Exception:
        return None


# ---------------------------------------------------------------------------
# PUBLIC: Threat analysis
# ---------------------------------------------------------------------------

def analyze_threat(text: str) -> str:
    text = (text or "").strip()

    # ---------------- Safety gate (incoming request) ----------------
    safety = safety_check(text)
    if safety["blocked"]:
        return safety["reason"]

    # ---------------- Slang detection ----------------
    slang = detect_slang(text)

    # ---------------- Transformer risk scoring ----------------
    risk = transformer_risk(text)

    # ---------------- LLM fusion ----------------
    llm_result = llm_explain_threat(text, slang, risk)

    # ---------------- Build output ----------------
    out = []

    if slang:
        out.append("### Slang / Idioms Detected")
        for k, v in slang.items():
            out.append(f"- {k}: {v}")
        out.append("")

    out.append(f"### Risk Score: {risk['risk_score']} (1–5)")
    out.append(f"Classifier max probability: {risk['max_prob']:.3f}\n")

    if llm_result:
        # Safety check on model output
        out_safety = safety_check_output(llm_result)
        if out_safety["blocked"]:
            return "⚠️ LLM output blocked: " + out_safety["reason"]

        out.append("### LLM Interpretation\n" + llm_result)
    else:
        out.append("### LLM Interpretation Unavailable")
        out.append("Only transformer-based scoring applied.")

    return "\n".join(out)


# ---------------------------------------------------------------------------
# PUBLIC: Translation mode
# ---------------------------------------------------------------------------

def translate_and_explain(text: str) -> str:
    text = (text or "").strip()

    safety = safety_check(text)
    if safety["blocked"]:
        return safety["reason"]

    llm_result = llm_translate_explain(text)
    if llm_result:
        out_safety = safety_check_output(llm_result)
        if out_safety["blocked"]:
            return "⚠️ LLM translation blocked: " + out_safety["reason"]
        return llm_result

    return f"LLM not configured.\nRaw text:\n{text}"