app.py

import time
from loguru import logger
from fastapi import FastAPI, HTTPException
from contextlib import asynccontextmanager

from models import RerankRequest, RerankResponse, RerankResult
from core import ModelManager


model_manager = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager with model preloading."""
    global model_manager
    
    # Startup
    logger.info("Starting reranking API...")
    try:
        model_manager = ModelManager("config.yaml")
        await model_manager.preload_all_models()
        logger.success("Reranking API startup complete!")
    except Exception as e:
        logger.error(f"Failed to initialize models: {e}")
        raise
    
    yield
    
    # Shutdown
    logger.info("Shutting down reranking API...")


app = FastAPI(
    title="Reranking API",
    description="""
High-performance API for document reranking using multiple state-of-the-art models.

✅ **Supported Models:**
- **Qwen/Qwen3-Reranker-0.6B**
- **BAAI/bge-reranker-v2-m3**
- **jinaai/jina-reranker-v2-base-multilingual**

🚀 **Features:**
- Multiple reranking models preloaded at startup
- List all available models 
- Optional instruction-based reranking (Qwen3)

⚠️ **Warning**: Not for production use!.
    """,
    version="1.0.0",
    lifespan=lifespan
)

@app.post("/rerank", response_model=RerankResponse, tags=["Reranking"])
async def rerank_documents(request: RerankRequest):
    """
    Rerank documents based on relevance to query.
    
    This endpoint takes a query and list of documents, then returns them
    ranked by relevance using the specified reranking model.
    
    Args:
        request: RerankRequest containing query, documents, and model info
        
    Returns:
        RerankResponse with ranked documents, scores, and metadata
        
    Example:
        ```json
        {
            "query": "machine learning algorithms",
            "documents": [
                "Deep learning uses neural networks",
                "Weather forecast for tomorrow",
                "Supervised learning with labeled data"
            ],
            "model_id": "jina-reranker-v2"
        }
        ```
    """
    if not request.query.strip():
        raise HTTPException(400, "Query cannot be empty")
    
    if not request.documents:
        raise HTTPException(400, "Documents list cannot be empty")
    
    valid_docs = [(i, doc.strip()) for i, doc in enumerate(request.documents) if doc.strip()]
    if not valid_docs:
        raise HTTPException(400, "No valid documents found after filtering empty strings")
    
    try:
        start_time = time.time()
        
        model = model_manager.get_model(request.model_id)
        original_indices, documents = zip(*valid_docs)
        
        scores = model.rerank(
            query=request.query.strip(),
            documents=list(documents),
            instruction=request.instruction
        )
        
        results = []
        for i, (orig_idx, doc, score) in enumerate(zip(original_indices, documents, scores)):
            results.append(RerankResult(
                text=doc,
                score=score,
                index=orig_idx
            ))
        
        results.sort(key=lambda x: x.score, reverse=True)
        
        if request.top_k:
            results = results[:request.top_k]
        
        processing_time = time.time() - start_time
        
        logger.info(
            f"Reranked {len(documents)} documents in {processing_time:.3f}s "
            f"using {request.model_id}"
        )
        
        return RerankResponse(
            results=results,
            query=request.query.strip(),
            model_id=request.model_id,
            processing_time=processing_time,
            total_documents=len(request.documents),
            returned_documents=len(results)
        )
        
    except ValueError as e:
        raise HTTPException(400, str(e))
    except Exception as e:
        logger.error(f"Reranking failed: {e}")
        raise HTTPException(500, f"Reranking failed: {str(e)}")


@app.get("/models", tags=["Models"])
async def list_models():
    """
    List all available reranking models.
    
    Returns information about all configured models including their
    loading status and capabilities.
    
    Returns:
        List of model information dictionaries
    """
    try:
        return model_manager.list_models()
    except Exception as e:
        logger.error(f"Failed to list models: {e}")
        raise HTTPException(500, str(e))


@app.get("/health", tags=["Monitoring"])
async def health_check():
    """
    Check API health and model status.
    
    Returns comprehensive health information including model loading
    status and system metrics.
    
    Returns:
        Health status dictionary
    """
    try:
        models = model_manager.list_models()
        loaded_models = [m for m in models if m['loaded']]
        
        return {
            "status": "ok",
            "total_models": len(models),
            "loaded_models": len(loaded_models),
            "available_models": [m['id'] for m in loaded_models],
            "models_info": models
        }
    except Exception as e:
        logger.error(f"Health check failed: {e}")
        return {
            "status": "error",
            "error": str(e)
        }
    
@app.get("/", tags=["Monitoring"])
async def root():
    return {
"message": "Welcome to Reranking API. Visit https://fahmiaziz-api-rerank-model.hf.space/docs for API documentation. And we also have Embedding API! Visit https://fahmiaziz-api-embedding.hf.space/docs", 
"version": "1.0.0"
}