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

from rasa.nlu.classifiers.classifier import IntentClassifier
from rasa.shared.nlu.constants import TEXT, INTENT
from rasa.nlu.config import RasaNLUModelConfig
from rasa.shared.nlu.training_data.training_data import TrainingData
from rasa.shared.nlu.training_data.message import Message
from rasa.nlu.model import Metadata

logger = logging.getLogger(__name__)


class LlmIntentClassifier(IntentClassifier):
    """Delegates intent classification to an external HTTP micro-service."""

    name = "LlmIntentClassifier"
    defaults = {
        "classifier_url": "http://classifier:8000/classify",
        "timeout": 5.0,
        "model_name": None,
        "base_url": None,
        "class_set": [],
        "prompt_template": None,
    }

    def __init__(
        self,
        component_config: Optional[Dict[Text, Any]] = None,
    ) -> None:
        super().__init__(component_config or {})

        self.url: str = self.component_config.get("classifier_url")
        self.timeout: float = float(self.component_config.get("timeout"))
        self.model_name: Optional[Text] = self.component_config.get("model_name")
        self.base_url: Optional[Text] = self.component_config.get("base_url")
        self.class_set: List[Text] = self.component_config.get("class_set", [])
        self.prompt_template: Optional[Text] = self.component_config.get("prompt_template")

        # Validate required configuration
        missing: List[str] = []
        if not self.model_name:
            missing.append("model_name")
        if not self.base_url:
            missing.append("base_url")
        if not self.class_set:
            missing.append("class_set")
        if not self.prompt_template:
            missing.append("prompt_template")
        if missing:
            raise ValueError(
                f"Missing configuration for {', '.join(missing)} in LlmIntentClassifier"
            )

        # Push config to classifier backend
        self._configure_remote_classifier()

    def _configure_remote_classifier(self) -> None:
        """Send configuration to the classifier backend to initialize the model."""
        payload = {
            "model_name": self.model_name,
            "base_url": self.base_url,
            "class_set": self.class_set,
            "prompt_template": self.prompt_template,
        }
        try:
            config_url = self.url.replace("/classify", "/config")
            logger.debug(f"Sending classifier config to: {config_url}")
            response = requests.post(config_url, json=payload, timeout=self.timeout)
            response.raise_for_status()
            logger.info("Remote classifier initialized successfully.")
        except Exception as e:
            logger.warning(f"Failed to initialize remote classifier: {e}")

    def train(
        self,
        training_data: TrainingData,
        config: Optional[RasaNLUModelConfig] = None,
        **kwargs: Any,
    ) -> None:
        # No local training; this uses a remote service
        pass

    def process(self, message: Message, **kwargs: Any) -> None:
        text: Optional[Text] = message.get(TEXT)
        intent_name: Optional[Text] = None
        confidence: float = 0.0

        if text:
            payload: Dict[str, Any] = {"message": text}
            try:
                resp = requests.post(self.url, json=payload, timeout=self.timeout)
                resp.raise_for_status()
                result = resp.json().get("result")
                if isinstance(result, str):
                    intent_name = result
                    confidence = 1.0
            except Exception as e:
                logger.warning(f"LlmIntentClassifier HTTP error: {e}")

        message.set(INTENT, {"name": intent_name, "confidence": confidence}, add_to_output=True)

    def persist(
        self,
        file_name: Text,
        model_dir: Text,
    ) -> Optional[Dict[Text, Any]]:
        # Save configuration so it can be reloaded
        return {
            "classifier_url": self.url,
            "timeout": self.timeout,
            "model_name": self.model_name,
            "base_url": self.base_url,
            "class_set": self.class_set,
            "prompt_template": self.prompt_template,
        }

    @classmethod
    def load(
        cls,
        meta: Dict[Text, Any],
        model_dir: Text,
        model_metadata: Metadata = None,
        cached_component: Optional["LlmIntentClassifier"] = None,
        **kwargs: Any,
    ) -> "LlmIntentClassifier":
        # meta contains saved configuration
        return cls(meta)