# -*- coding: utf-8 -*-
"""Location: ./mcp-servers/python/mcp_eval_server/mcp_eval_server/judges/gemini_judge.py
Copyright 2025
SPDX-License-Identifier: Apache-2.0
Authors: Mihai Criveti

Google Gemini judge implementation for LLM-as-a-judge evaluation.
"""

# Standard
import asyncio
import logging
import os
from typing import Any, Dict, List, Optional

try:
    # Third-Party
    import google.generativeai as genai
    from google.generativeai.types import HarmBlockThreshold, HarmCategory
except ImportError:
    genai = None

# Third-Party
from tenacity import retry, stop_after_attempt, wait_exponential

# Local
from .base_judge import (
    BaseJudge,
    EvaluationCriteria,
    EvaluationResult,
    EvaluationRubric,
    PairwiseResult,
    RankingResult,
    ReferenceEvaluationResult,
)


class GeminiJudge(BaseJudge):
    """Judge implementation using Google Gemini API."""

    def __init__(self, config: Dict[str, Any]) -> None:
        """Initialize Gemini judge.

        Args:
            config: Configuration dictionary with Gemini settings

        Raises:
            ValueError: If API key not found or Gemini library not available
        """
        super().__init__(config)
        self.logger = logging.getLogger(__name__)

        if genai is None:
            raise ValueError("Google Generative AI library not installed. Please install with: pip install google-generativeai")

        api_key = os.getenv(config["api_key_env"])
        if not api_key:
            raise ValueError(f"API key not found in environment variable: {config['api_key_env']}")

        genai.configure(api_key=api_key)
        self.model_name = config["model_name"]

        # Initialize the model
        self.model = genai.GenerativeModel(
            model_name=self.model_name,
            safety_settings={
                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
            },
        )

        self.logger.debug(f"🔧 Initialized Gemini judge: {self.model_name}")

    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    async def _make_api_call(self, messages: List[Dict[str, str]], temperature: Optional[float] = None, max_tokens: Optional[int] = None) -> str:
        """Make API call with retry logic.

        Args:
            messages: Chat messages for the API call
            temperature: Optional temperature override
            max_tokens: Optional max tokens override

        Returns:
            Response content from the API

        Raises:
            Exception: If Gemini API call fails
        """
        self.logger.debug(f"🔗 Making Gemini API call to {self.model_name}")
        self.logger.debug(f"   Messages: {len(messages)}, Temperature: {temperature or self.temperature}, Max tokens: {max_tokens or self.max_tokens}")

        # Convert messages to Gemini format
        prompt_parts = []
        for msg in messages:
            if msg["role"] == "system":
                prompt_parts.append(f"System: {msg['content']}")
            elif msg["role"] == "user":
                prompt_parts.append(f"User: {msg['content']}")
            elif msg["role"] == "assistant":
                prompt_parts.append(f"Assistant: {msg['content']}")

        prompt = "\n\n".join(prompt_parts)

        # Generation config
        generation_config = genai.types.GenerationConfig(
            temperature=temperature or self.temperature,
            max_output_tokens=max_tokens or self.max_tokens,
        )

        try:
            # Run in thread pool since Google library is sync
            # Standard
            import concurrent.futures  # pylint: disable=import-outside-toplevel

            def make_request():
                response = self.model.generate_content(prompt, generation_config=generation_config)
                return response.text if response.text else ""

            loop = asyncio.get_event_loop()
            with concurrent.futures.ThreadPoolExecutor() as executor:
                result = await loop.run_in_executor(executor, make_request)

            self.logger.debug(f"✅ Gemini API response received - Length: {len(result)} chars")

            # Log the actual model response (truncated)
            truncated_response = result[:200] + "..." if len(result) > 200 else result
            self.logger.debug(f"   💬 Model response: {truncated_response}")

            return result

        except Exception as e:
            raise Exception(f"Gemini API call failed: {e}")

    async def evaluate_response(
        self,
        response: str,
        criteria: List[EvaluationCriteria],
        rubric: EvaluationRubric,
        context: Optional[str] = None,
        use_cot: bool = True,
    ) -> EvaluationResult:
        """Evaluate a single response using Gemini.

        Args:
            response: Text response to evaluate
            criteria: List of evaluation criteria
            rubric: Detailed scoring rubric
            context: Optional context
            use_cot: Whether to use chain-of-thought reasoning

        Returns:
            Evaluation result with scores and reasoning
        """

        criteria_text = self._format_criteria(criteria)
        rubric_text = self._format_rubric(rubric)

        prompt = self._render_template("evaluation", context=context, response=response, criteria_text=criteria_text, rubric_text=rubric_text, use_cot=use_cot)

        messages = [{"role": "system", "content": "You are a professional evaluation expert. Provide thorough, unbiased assessments."}, {"role": "user", "content": prompt}]

        response_text = await self._make_api_call(messages)

        return self._parse_evaluation_response(response_text, criteria, model=self.model_name, temperature=self.temperature, use_cot=use_cot)

    async def pairwise_comparison(
        self,
        response_a: str,
        response_b: str,
        criteria: List[EvaluationCriteria],
        context: Optional[str] = None,
        position_bias_mitigation: bool = True,
    ) -> PairwiseResult:
        """Compare two responses using Gemini.

        Args:
            response_a: First response
            response_b: Second response
            criteria: Comparison criteria
            context: Optional context
            position_bias_mitigation: Whether to mitigate position bias

        Returns:
            Pairwise comparison result
        """
        return await self._base_pairwise_comparison(response_a, response_b, criteria, context, position_bias_mitigation)

    async def rank_responses(
        self,
        responses: List[str],
        criteria: List[EvaluationCriteria],
        context: Optional[str] = None,
        ranking_method: str = "tournament",
    ) -> RankingResult:
        """Rank multiple responses using Gemini.

        Args:
            responses: List of response strings to rank
            criteria: List of evaluation criteria for ranking
            context: Optional context for evaluation
            ranking_method: Method to use for ranking

        Returns:
            RankingResult containing ranked responses and consistency score

        Raises:
            ValueError: If less than 2 responses provided or unknown ranking method
        """
        return await self._base_rank_responses(responses, criteria, context, ranking_method)

    async def evaluate_with_reference(
        self,
        response: str,
        reference: str,
        evaluation_type: str = "factuality",
        tolerance: str = "moderate",
    ) -> ReferenceEvaluationResult:
        """Evaluate response against reference using Gemini.

        Args:
            response: Response text to evaluate
            reference: Reference text to compare against
            evaluation_type: Type of evaluation
            tolerance: Tolerance level for evaluation

        Returns:
            ReferenceEvaluationResult containing score and analysis
        """
        return await self._base_reference_evaluation(response, reference, evaluation_type, tolerance)
