# services/llm/claude_service.py
"""
Claude (Anthropic) service implementation
"""
import anthropic
from typing import Dict, List
import json
from .base import LLMService
from config.api_keys import APIKeyManager
from utils.logger import setup_logger


class ClaudeService(LLMService):
    def __init__(
        self,
        model: str = "claude-3-7-sonnet-20250219", # Debemos usar el modelo claude-3-7-sonnet-20250219
        temperature: float = 0.3,
        max_tokens: int = 16000,
    ):
        api_key = APIKeyManager.get_claude_key()
        if not api_key:
            raise ValueError(
                "Claude API key not found. Please set the CLAUDE_API_KEY environment variable."
            )

        self.client = anthropic.Anthropic(api_key=api_key)
        self.model = model
        self.temperature = temperature
        self.max_tokens = max_tokens
        self.logger = setup_logger("claude")

    def generate_text(self, prompt: str) -> str:
        self.logger.info(f"--- PROMPT ---\n{prompt}")
        try:
            message = self.client.messages.create(
                model=self.model,
                max_tokens=self.max_tokens,
                temperature=self.temperature,
                messages=[
                    {
                        "role": "user",
                        "content": prompt,
                    }
                ],
            )
            response_content = message.content[0].text
            self.logger.info(f"--- RESPONSE ---\n{response_content}")
            return response_content
        except Exception as e:
            self.logger.error(f"Error in Claude API call: {e}")
            print(f"Error in Claude API call: {e}")
            return None

    def get_similarity_scores(self, texts_pairs: Dict[str, List[str]]) -> List[float]:
        # Claude's API doesn't have a dedicated similarity or JSON mode endpoint as straightforward as others.
        # We will instruct it to return JSON.
        system_prompt = (
            "You are an expert in semantic analysis. Evaluate the semantic similarity between the pairs of texts provided. "
            "Return your response ONLY as a JSON object containing a single key 'similarity_scores' with a list of floats from 0.0 to 1.0. "
            "Do not include any other text, explanation, or markdown formatting. The output must be a valid JSON."
        )

        request_payload = json.dumps(texts_pairs)

        try:
            message = self.client.messages.create(
                model=self.model,
                max_tokens=self.max_tokens,
                temperature=self.temperature,
                system=system_prompt,
                messages=[
                    {
                        "role": "user",
                        "content": request_payload,
                    }
                ],
            )

            response_content = message.content[0].text

            try:
                # Find the JSON part of the response
                json_start = response_content.find("{")
                json_end = response_content.rfind("}") + 1
                if json_start == -1 or json_end == 0:
                    raise ValueError("No JSON object found in the response.")

                json_str = response_content[json_start:json_end]
                scores_data = json.loads(json_str)

                if isinstance(scores_data, dict) and "similarity_scores" in scores_data:
                    return scores_data["similarity_scores"]
                else:
                    raise ValueError("Unexpected JSON format from Claude.")
            except (json.JSONDecodeError, ValueError) as e:
                print(f"Error decoding Claude JSON response: {e}")
                raise ValueError(
                    "Could not decode or parse similarity scores from Claude response."
                )

        except Exception as e:
            print(f"Error in Claude similarity calculation: {e}")
            return None