"""
Medium Grader — Scores multi-step issue resolution responses.
"""

from typing import Tuple, Optional


class MediumGrader:
    """
    Scoring logic for Medium (Multi-Step) Task.

    Formula:
        score = step_detection_score * completeness_multiplier * tone_multiplier

    Step Detection:
        Each required step has associated keywords.
        If the agent's reply matches those keywords → step is identified.

    Completeness:
        Partial answers (missing some required info for the step) get partial credit.

    Tone:
        Professional, empathetic tone adds a small bonus.
    """

    # Keywords that signal each step type
    STEP_KEYWORDS = {
        # Medium scenario step types → what words trigger them
        "acknowledge_frustration":  ["understand", "frustrat", "sorry to hear", "apologize", "that must", "i can see"],
        "acknowledge_issue":        ["understand", "sorry", "i can see", "must be", "apologize", "frustrat"],
        "collect_account_info":     ["email", "account", "order number", "name", "id", "could you provide", "can i get", "please share"],
        "collect_order_info":       ["order number", "order id", "reference", "can i get", "could you share"],
        "investigate":              ["look into", "check", "investigate", "pull up", "look at", "review your account"],
        "check_basic_steps":        ["browser", "cache", "clear", "try", "incognito", "different browser", "cookies"],
        "offer_password_reset":     ["reset", "password", "link", "send you", "email you", "forgot password"],
        "confirm_resolution":       ["resolved", "able to log", "working now", "fixed", "sorted", "everything ok", "is that working"],
        "resolve_or_escalate":      ["refund", "credit", "resolve", "fix", "escalate", "team will", "processed"],
        "apologize_sincerely":      ["sorry", "apologize", "sincerely apologize", "deeply sorry", "truly sorry"],
        "confirm_item_details":     ["ordered", "confirm", "blue jacket", "correct item", "you ordered", "size"],
        "arrange_replacement":      ["replacement", "send the correct", "return", "new", "reship", "express", "free return"],
    }

    POLITE_PHRASES = [
        "happy to help", "certainly", "of course", "absolutely",
        "please", "thank you", "glad", "i understand", "i appreciate",
        "let me help", "my pleasure",
    ]

    BAD_PHRASES = [
        "not my problem", "policy says", "can't do anything", "nothing i can do",
        "you should have", "your fault", "read the faq",
    ]

    def grade(
        self,
        action: str,
        scenario: dict,
        history: list,
        steps_completed: list,
    ) -> Tuple[float, dict, Optional[str]]:
        """
        Grade the agent's response for a medium-task step.

        Returns:
            (score: float, grader_info: dict, step_identified: str or None)
        """
        action_lower = action.lower()
        required_steps = scenario.get("required_steps", [])

        # 1. Detect which step the agent is performing
        step_identified = None
        step_match_score = 0.0

        for step in required_steps:
            if step in steps_completed:
                continue  # Already done, skip
            step_keywords = self.STEP_KEYWORDS.get(step, [])
            if not step_keywords:
                continue
            matches = sum(1 for kw in step_keywords if kw in action_lower)
            match_ratio = matches / len(step_keywords)
            if match_ratio > step_match_score and matches >= 1:
                step_match_score = match_ratio
                step_identified = step

        # 2. Completeness score (how thoroughly the step is addressed)
        completeness = min(1.0, step_match_score * 1.5) if step_identified else 0.3

        # 3. Tone multiplier
        has_polite = any(p in action_lower for p in self.POLITE_PHRASES)
        has_bad = any(p in action_lower for p in self.BAD_PHRASES)

        if has_bad:
            tone_mult = 0.6
            tone = "bad"
        elif has_polite:
            tone_mult = 1.1
            tone = "polite"
        else:
            tone_mult = 1.0
            tone = "neutral"

        # Final score
        if step_identified:
            score = min(1.0, completeness * tone_mult)
        else:
            # No useful step found — partial score for being polite
            score = 0.2 * tone_mult if has_polite else 0.1

        grader_info = {
            "step_identified": step_identified,
            "step_match_score": round(step_match_score, 3),
            "completeness": round(completeness, 3),
            "tone": tone,
            "already_done": steps_completed,
        }

        return round(score, 3), grader_info, step_identified