import logging
from typing import Dict, Any, Optional, List

logger = logging.getLogger(__name__)

class InjectionDetector:
    """
    Detects prompt injection attacks and other adversarial inputs.
    Placeholder implementation.
    """
    def __init__(self) -> None:
        logger.info("InjectionDetector initialized.")
        self.rules = {
            "instruction_manipulation": [
                "ignore your previous instructions",
                "ignore the above",
                "disregard the above",
                "forget the above",
            ],
            "role_play_attack": [
                "you are now",
                "act as",
                "roleplay as",
                "new set of rules",
            ],
        }

    def analyze_text(self, text_input: str, conversation_history: Optional[List[str]] = None) -> Dict[str, Any]:
        """
        Analyzes the text input for signs of prompt injection.

        Returns:
            A dictionary containing the analysis results.
        """
        score = 0
        detected_rules = []
        highlighted_keywords = []
        lower_text = text_input.lower()

        for rule_name, keywords in self.rules.items():
            for kw in keywords:
                if kw in lower_text:
                    score += 1
                    detected_rules.append(f"{rule_name}: {kw}")
                    highlighted_keywords.append(kw)

        if score > 0:
            classification = "potential_injection"
            explanation = f"Potential prompt injection detected based on keywords: {', '.join(highlighted_keywords)}"
            spotlight = {
                "highlighted_text": list(set(highlighted_keywords)),
                "triggered_rules": detected_rules,
                "explanation": explanation,
            }
        else:
            classification = "benign"
            explanation = "No signs of prompt injection detected."
            spotlight = None

        return {
            "detector_name": "InjectionDetector",
            "classification": classification,
            "score": float(score),
            "explanation": explanation,
            "spotlight": spotlight,
            "error": None
        }
