# api.py file in main directory of the Inference API module.

import httpx
from typing import Optional, AsyncIterator, Dict, Any
import logging
from litserve import LitAPI
from pydantic import BaseModel

class GenerationResponse(BaseModel):
    generated_text: str

class InferenceApi(LitAPI):
    def __init__(self):
        """Initialize the Inference API with configuration."""
        super().__init__()
        self.logger = logging.getLogger(__name__)
        self.logger.info("Initializing Inference API")
        self.client = None

    async def setup(self, device: Optional[str] = None):
        """Setup method required by LitAPI - initialize HTTP client"""
        self._device = device
        self.client = httpx.AsyncClient(
            base_url="http://localhost:8002",  # We'll need to make this configurable
            timeout=60.0
        )
        self.logger.info(f"Inference API setup completed on device: {device}")

    async def predict(self, x: str, **kwargs) -> AsyncIterator[str]:
        """
        Main prediction method required by LitAPI.
        Always yields, either chunks in streaming mode or complete response in non-streaming mode.
        """
        if self.stream:
            async for chunk in self.generate_stream(x, **kwargs):
                yield chunk
        else:
            response = await self.generate_response(x, **kwargs)
            yield response

    def decode_request(self, request: Any, **kwargs) -> str:
        """Convert the request payload to input format."""
        if isinstance(request, dict) and "prompt" in request:
            return request["prompt"]
        return request

    def encode_response(self, output: AsyncIterator[str], **kwargs) -> AsyncIterator[Dict[str, str]]:
        """Convert the model output to a response payload."""
        async def wrapper():
            async for chunk in output:
                yield {"generated_text": chunk}
        return wrapper()

    async def generate_response(
            self,
            prompt: str,
            system_message: Optional[str] = None,
            max_new_tokens: Optional[int] = None
    ) -> str:
        """Generate a complete response by forwarding the request to the LLM Server."""
        self.logger.debug(f"Forwarding generation request for prompt: {prompt[:50]}...")

        try:
            response = await self.client.post(
                "/api/v1/generate",
                json={
                    "prompt": prompt,
                    "system_message": system_message,
                    "max_new_tokens": max_new_tokens
                }
            )
            response.raise_for_status()
            data = response.json()
            return data["generated_text"]

        except Exception as e:
            self.logger.error(f"Error in generate_response: {str(e)}")
            raise

    async def generate_stream(
            self,
            prompt: str,
            system_message: Optional[str] = None,
            max_new_tokens: Optional[int] = None
    ) -> AsyncIterator[str]:
        """Generate a streaming response by forwarding the request to the LLM Server."""
        self.logger.debug(f"Forwarding streaming request for prompt: {prompt[:50]}...")

        try:
            async with self.client.stream(
                    "POST",
                    "/api/v1/generate/stream",
                    json={
                        "prompt": prompt,
                        "system_message": system_message,
                        "max_new_tokens": max_new_tokens
                    }
            ) as response:
                response.raise_for_status()
                async for chunk in response.aiter_text():
                    yield chunk

        except Exception as e:
            self.logger.error(f"Error in generate_stream: {str(e)}")
            raise

    async def cleanup(self):
        """Cleanup method - close HTTP client"""
        if self.client:
            await self.client.aclose()