main/routes.py

from fastapi import APIRouter, HTTPException
from fastapi.responses import StreamingResponse
from typing import Optional
import json
from time import time
import logging
from .api import InferenceApi
from .schemas import (
    GenerateRequest,
    EmbeddingRequest,
    EmbeddingResponse,
    SystemStatusResponse,
    ValidationResponse,
    ChatCompletionRequest,
    ChatCompletionResponse, QueryExpansionResponse, QueryExpansionRequest, ChunkRerankResponse, ChunkRerankRequest
)

router = APIRouter()
logger = logging.getLogger(__name__)
api = None
config = None

def init_router(inference_api: InferenceApi, conf):
    """Initialize router with an already setup API instance"""
    global api, config
    api = inference_api
    config = conf
    logger.info("Router initialized with Inference API instance")

@router.post("/generate")
async def generate_text(request: GenerateRequest):
    """Generate text response from prompt"""
    logger.info(f"Received generation request for prompt: {request.prompt[:50]}...")
    try:
        response = await api.generate_response(
            prompt=request.prompt,
            system_message=request.system_message,
            max_new_tokens=request.max_new_tokens
        )
        logger.info("Successfully generated response")
        return {"generated_text": response}
    except Exception as e:
        logger.error(f"Error in generate_text endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/generate/stream")
async def generate_stream(request: GenerateRequest):
    """Generate streaming text response from prompt"""
    logger.info(f"Received streaming generation request for prompt: {request.prompt[:50]}...")
    try:
        return StreamingResponse(
            api.generate_stream(
                prompt=request.prompt,
                system_message=request.system_message,
                max_new_tokens=request.max_new_tokens
            ),
            media_type="text/event-stream"
        )
    except Exception as e:
        logger.error(f"Error in generate_stream endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
    """OpenAI-compatible chat completion endpoint"""
    logger.info(f"Received chat completion request with {len(request.messages)} messages")

    try:
        # Extract the last user message, or combine messages if needed
        last_message = request.messages[-1].content

        if request.stream:
            # For streaming, we need to create a generator that yields OpenAI-compatible chunks
            async def generate_stream():
                async for chunk in api.generate_stream(
                        prompt=last_message,
                ):
                    # Create a streaming response chunk in OpenAI format
                    response_chunk = {
                        "id": "chatcmpl-123",
                        "object": "chat.completion.chunk",
                        "created": int(time()),
                        "model": request.model,
                        "choices": [{
                            "index": 0,
                            "delta": {
                                "content": chunk
                            },
                            "finish_reason": None
                        }]
                    }
                    yield f"data: {json.dumps(response_chunk)}\n\n"

                # Send the final chunk
                yield f"data: [DONE]\n\n"

            return StreamingResponse(
                generate_stream(),
                media_type="text/event-stream"
            )

        else:
            # For non-streaming, generate the full response
            response_text = await api.generate_response(
                prompt=last_message,
            )

            # Convert to OpenAI format
            return ChatCompletionResponse.from_response(
                content=response_text,
                model=request.model
            )

    except Exception as e:
        logger.error(f"Error in chat completion endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/expand_query", response_model=QueryExpansionResponse)
async def expand_query(request: QueryExpansionRequest):
    """Expand a query for RAG processing"""
    logger.info(f"Received query expansion request: {request.query[:50]}...")
    try:
        result = await api.expand_query(
            query=request.query,
            system_message=request.system_message
        )
        logger.info("Successfully expanded query")
        return result
    except FileNotFoundError as e:
        logger.error(f"Template file not found: {str(e)}")
        raise HTTPException(status_code=500, detail="Query expansion template not found")
    except json.JSONDecodeError as e:
        logger.error(f"Invalid JSON response from LLM: {str(e)}")
        raise HTTPException(status_code=500, detail="Invalid response format from LLM")
    except Exception as e:
        logger.error(f"Error in expand_query endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/rerank", response_model=ChunkRerankResponse)
async def rerank_chunks(request: ChunkRerankRequest):
    """Rerank chunks based on their relevance to the query"""
    logger.info(f"Received reranking request for query: {request.query[:50]}...")
    try:
        result = await api.rerank_chunks(
            query=request.query,
            chunks=request.chunks,
            system_message=request.system_message
        )
        logger.info(f"Successfully reranked {len(request.chunks)} chunks")
        return result
    except Exception as e:
        logger.error(f"Error in rerank_chunks endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/embedding", response_model=EmbeddingResponse)
async def generate_embedding(request: EmbeddingRequest):
    """Generate embedding vector from text"""
    logger.info(f"Received embedding request for text: {request.text[:50]}...")
    try:
        embedding = await api.generate_embedding(request.text)
        logger.info(f"Successfully generated embedding of dimension {len(embedding)}")
        return EmbeddingResponse(
            embedding=embedding,
            dimension=len(embedding)
        )
    except Exception as e:
        logger.error(f"Error in generate_embedding endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.get("/system/status",
            response_model=SystemStatusResponse,
            summary="Check System Status",
            description="Returns comprehensive system status including CPU, Memory, GPU, Storage, and Model information")
async def check_system():
    """Get system status from LLM Server"""
    try:
        return await api.check_system_status()
    except Exception as e:
        logger.error(f"Error checking system status: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.get("/system/validate",
            response_model=ValidationResponse,
            summary="Validate System Configuration",
            description="Validates system configuration, folders, and model setup")
async def validate_system():
    """Get system validation status from LLM Server"""
    try:
        return await api.validate_system()
    except Exception as e:
        logger.error(f"Error validating system: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/model/initialize",
             summary="Initialize default or specified model",
             description="Initialize model for use. Uses default model from config if none specified.")
async def initialize_model(model_name: Optional[str] = None):
    """Initialize a model for use"""
    try:
        return await api.initialize_model(model_name)
    except Exception as e:
        logger.error(f"Error initializing model: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/model/initialize/embedding",
             summary="Initialize embedding model",
             description="Initialize a separate model specifically for generating embeddings")
async def initialize_embedding_model(model_name: Optional[str] = None):
    """Initialize a model specifically for embeddings"""
    try:
        return await api.initialize_embedding_model(model_name)
    except Exception as e:
        logger.error(f"Error initializing embedding model: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/model/download",
             summary="Download default or specified model",
             description="Downloads model files. Uses default model from config if none specified.")
async def download_model(model_name: Optional[str] = None):
    """Download model files to local storage"""
    try:
        # Use model name from config if none provided
        model_to_download = model_name or config["model"]["defaults"]["model_name"]
        logger.info(f"Received request to download model: {model_to_download}")

        result = await api.download_model(model_to_download)
        logger.info(f"Successfully downloaded model: {model_to_download}")

        return result

    except Exception as e:
        logger.error(f"Error downloading model: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.on_event("shutdown")
async def shutdown_event():
    """Clean up resources on shutdown"""
    if api:
        await api.cleanup()