import time
from langchain.callbacks.base import BaseCallbackHandler
from typing import Dict, Any, List
from langchain.schema import LLMResult
from common.utils.business_event_context import current_event


class PersistentLLMMetricsHandler(BaseCallbackHandler):
    """Metrics handler that allows metrics to be retrieved from within any call. In case metrics are required for other
    purposes than business event logging."""

    def __init__(self):
        self.total_tokens: int = 0
        self.prompt_tokens: int = 0
        self.completion_tokens: int = 0
        self.start_time: float = 0
        self.end_time: float = 0
        self.total_time: float = 0

    def reset(self):
        self.total_tokens = 0
        self.prompt_tokens = 0
        self.completion_tokens = 0
        self.start_time = 0
        self.end_time = 0
        self.total_time = 0

    def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:
        self.start_time = time.time()

    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        self.end_time = time.time()
        self.total_time = self.end_time - self.start_time

        usage = response.llm_output.get('token_usage', {})
        self.prompt_tokens += usage.get('prompt_tokens', 0)
        self.completion_tokens += usage.get('completion_tokens', 0)
        self.total_tokens = self.prompt_tokens + self.completion_tokens

    def get_metrics(self) -> Dict[str, int | float]:
        return {
            'total_tokens': self.total_tokens,
            'prompt_tokens': self.prompt_tokens,
            'completion_tokens': self.completion_tokens,
            'time_elapsed': self.total_time,
            'interaction_type': 'LLM',
        }