main/api.py

import httpx
from typing import Optional, AsyncIterator, Dict, Any, Iterator
import logging
import asyncio
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}")

    def predict(self, x: str, **kwargs) -> Iterator[str]:
        """
        Non-async prediction method that yields results.
        """
        loop = asyncio.get_event_loop()
        async def async_gen():
            async for item in self._async_predict(x, **kwargs):
                yield item

        gen = async_gen()
        while True:
            try:
                yield loop.run_until_complete(gen.__anext__())
            except StopAsyncIteration:
                break

    async def _async_predict(self, x: str, **kwargs) -> AsyncIterator[str]:
        """
        Internal async prediction method.
        """
        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: Iterator[str], **kwargs) -> Dict[str, Any]:
        """Convert the model output to a response payload."""
        # For streaming responses
        if self.stream:
            return {"generated_text": output}
        # For non-streaming, take the first (and only) item from the iterator
        try:
            result = next(output)
            return {"generated_text": result}
        except StopIteration:
            return {"generated_text": ""}

    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()