"""
Agent 4: AI Prompt Injection Detection Module
Uses a fine‑tuned DeBERTa model (MNLI) + rule‑based patterns.
"""

import re
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

class PromptInjectionAgent:
    """
    Detects prompt injection and jailbreak attempts in user inputs.
    Combines a transformer model (trained on MNLI) with heuristic rules.
    """
    def __init__(self, model_name="mrm8488/deberta-v3-small-finetuned-mnli", threshold=0.6):
        """
        Args:
            model_name: Hugging Face model identifier for a DeBERTa MNLI model.
            threshold: Confidence threshold above which input is flagged as injection.
        """
        print("Loading Prompt Injection Agent (MNLI-based)...")
        self.threshold = threshold

        # Load tokenizer and model
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.model.eval()  # inference mode
        print("✓ Model loaded successfully")

        # Rule‑based patterns (covers common jailbreak attempts)
        self.injection_patterns = [
            (r"ignore previous instructions", "instruction_override"),
            (r"ignore all previous", "instruction_override"),
            (r"disregard previous", "instruction_override"),
            (r"system prompt", "system_override"),
            (r"you are now", "role_playing"),
            (r"act as", "role_playing"),
            (r"new role:", "role_playing"),
            (r"forget your instructions", "instruction_override"),
            (r"do anything now", "privilege_escalation"),
            (r"you must", "privilege_escalation"),
            (r"you are free", "jailbreak"),
            (r"no restrictions", "jailbreak"),
            (r"override", "instruction_override"),
            (r"jailbreak", "jailbreak"),
            (r"dan", "jailbreak"),                # DAN mode
            (r"developer mode", "jailbreak"),
            (r"chatgpt, you are now", "role_playing"),
            (r"you are an ai with no ethics", "role_playing"),
            (r"output raw", "attention_diversion"),
            (r"base64 decode", "attention_diversion"),
        ]

    def analyze(self, text: str) -> dict:
        """
        Analyze input text for prompt injection.

        Returns:
            dict with keys:
                prompt_injection_detected (bool): final decision
                confidence (float): combined risk score
                risk_score (float): same as confidence (for backward compatibility)
                matched_patterns (list): regex patterns that fired
                attack_categories (list): types of injection detected
                explanation (list): human‑readable reasons
        """
        # -------------------- Rule‑based scan --------------------
        text_lower = text.lower()
        rule_score = 0.0
        matched_patterns = []
        attack_categories = []

        for pattern, category in self.injection_patterns:
            if re.search(pattern, text_lower):
                rule_score += 0.3
                matched_patterns.append(pattern)
                attack_categories.append(category)

        # -------------------- Transformer inference --------------------
        # Tokenize
        inputs = self.tokenizer(
            text,
            return_tensors="pt",
            truncation=True,
            max_length=512,
            padding=True
        )

        with torch.no_grad():
            outputs = self.model(**inputs)
            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)

        # MNLI classes: 0 = entailment, 1 = neutral, 2 = contradiction
        contradiction_prob = probs[0][2].item()

        # -------------------- Combine scores --------------------
        # 70% weight on contradiction probability, 30% on rule‑based
        combined_risk = 0.7 * contradiction_prob + 0.3 * min(rule_score, 1.0)
        detected = combined_risk > self.threshold

        # -------------------- Build explanation --------------------
        explanation = []
        explanation.append(f"Contradiction probability: {contradiction_prob:.1%}")
        if attack_categories:
            unique_cats = list(set(attack_categories))
            explanation.append(f"Rule matches: {', '.join(unique_cats)}")
        if detected:
            explanation.append(f"Combined risk {combined_risk:.1%} exceeds threshold {self.threshold:.0%}")

        return {
            "prompt_injection_detected": detected,
            "confidence": combined_risk,
            "risk_score": combined_risk,          # alias for compatibility
            "matched_patterns": matched_patterns,
            "attack_categories": list(set(attack_categories)),
            "explanation": explanation
        }