app_hybrid_backup.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from typing import List
import torch
import uvicorn

from models.schemas import EmbeddingRequest, EmbeddingResponse, ModelInfo
from utils.helpers import load_models, get_embeddings, cleanup_memory

# Global model cache
models_cache = {}

# Models to load at startup (most frequently used)
STARTUP_MODELS = ["jina-v3", "roberta-ca"]
# Models to load on demand
ON_DEMAND_MODELS = ["jina", "robertalex", "legal-bert"]

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan handler for startup and shutdown"""
    # Startup - load priority models
    try:
        global models_cache
        print(f"Loading startup models: {STARTUP_MODELS}...")
        models_cache = load_models(STARTUP_MODELS)
        print(f"Startup models loaded successfully: {list(models_cache.keys())}")
        yield
    except Exception as e:
        print(f"Failed to load startup models: {str(e)}")
        # Continue anyway - models can be loaded on demand
        yield
    finally:
        # Shutdown - cleanup resources
        cleanup_memory()

def ensure_model_loaded(model_name: str):
    """Load a specific model on demand if not already loaded"""
    global models_cache
    if model_name not in models_cache:
        if model_name in ON_DEMAND_MODELS:
            try:
                print(f"Loading model on demand: {model_name}...")
                new_models = load_models([model_name])
                models_cache.update(new_models)
                print(f"Model {model_name} loaded successfully!")
            except Exception as e:
                print(f"Failed to load model {model_name}: {str(e)}")
                raise HTTPException(status_code=500, detail=f"Model {model_name} loading failed: {str(e)}")
        else:
            raise HTTPException(status_code=400, detail=f"Unknown model: {model_name}")

app = FastAPI(
    title="Multilingual & Legal Embedding API",
    description="Multi-model embedding API for Spanish, Catalan, English and Legal texts",
    version="3.0.0",
    lifespan=lifespan
)

# Add CORS middleware to allow cross-origin requests
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify actual domains
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

@app.get("/")
async def root():
    return {
        "message": "Multilingual & Legal Embedding API",
        "models": ["jina", "robertalex", "jina-v3", "legal-bert", "roberta-ca"],
        "status": "running",
        "docs": "/docs",
        "total_models": 5
    }

@app.post("/embed", response_model=EmbeddingResponse)
async def create_embeddings(request: EmbeddingRequest):
    """Generate embeddings for input texts"""
    try:
        # Load specific model on demand if needed
        ensure_model_loaded(request.model)
        
        if not request.texts:
            raise HTTPException(status_code=400, detail="No texts provided")
        
        if len(request.texts) > 50:  # Rate limiting
            raise HTTPException(status_code=400, detail="Maximum 50 texts per request")
        
        embeddings = get_embeddings(
            request.texts,
            request.model,
            models_cache,
            request.normalize,
            request.max_length
        )
        
        # Cleanup memory after large batches
        if len(request.texts) > 20:
            cleanup_memory()
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used=request.model,
            dimensions=len(embeddings[0]) if embeddings else 0,
            num_texts=len(request.texts)
        )
        
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}")

@app.get("/models", response_model=List[ModelInfo])
async def list_models():
    """List available models and their specifications"""
    return [
        ModelInfo(
            model_id="jina",
            name="jinaai/jina-embeddings-v2-base-es",
            dimensions=768,
            max_sequence_length=8192,
            languages=["Spanish", "English"],
            model_type="bilingual",
            description="Bilingual Spanish-English embeddings with long context support"
        ),
        ModelInfo(
            model_id="robertalex",
            name="PlanTL-GOB-ES/RoBERTalex",
            dimensions=768,
            max_sequence_length=512,
            languages=["Spanish"],
            model_type="legal domain",
            description="Spanish legal domain specialized embeddings"
        ),
        ModelInfo(
            model_id="jina-v3",
            name="jinaai/jina-embeddings-v3",
            dimensions=1024,
            max_sequence_length=8192,
            languages=["Multilingual"],
            model_type="multilingual",
            description="Latest Jina v3 with superior multilingual performance"
        ),
        ModelInfo(
            model_id="legal-bert",
            name="nlpaueb/legal-bert-base-uncased",
            dimensions=768,
            max_sequence_length=512,
            languages=["English"],
            model_type="legal domain",
            description="English legal domain BERT model"
        ),
        ModelInfo(
            model_id="roberta-ca",
            name="projecte-aina/roberta-large-ca-v2",
            dimensions=1024,
            max_sequence_length=512,
            languages=["Catalan"],
            model_type="general",
            description="Catalan RoBERTa-large model trained on large corpus"
        )
    ]

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    startup_models_loaded = all(model in models_cache for model in STARTUP_MODELS)
    all_models_loaded = len(models_cache) == 5
    
    return {
        "status": "healthy" if startup_models_loaded else "partial",
        "startup_models_loaded": startup_models_loaded,
        "all_models_loaded": all_models_loaded,
        "available_models": list(models_cache.keys()),
        "startup_models": STARTUP_MODELS,
        "on_demand_models": ON_DEMAND_MODELS,
        "models_count": len(models_cache),
        "note": f"Startup models: {STARTUP_MODELS} | On-demand: {ON_DEMAND_MODELS}"
    }

if __name__ == "__main__":
    # Set multi-threading for CPU
    torch.set_num_threads(8)
    torch.set_num_interop_threads(1)
    
    uvicorn.run(app, host="0.0.0.0", port=7860)