app_old.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 = {}

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan handler for startup and shutdown"""
    # Startup
    try:
        global models_cache
        print("Loading models...")
        models_cache = load_models()
        print("All models loaded successfully!")
        yield
    except Exception as e:
        print(f"Failed to load models: {str(e)}")
        raise
    finally:
        # Shutdown - cleanup resources
        cleanup_memory()

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:
        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"""
    models_loaded = len(models_cache) == 5
    return {
        "status": "healthy" if models_loaded else "degraded",
        "models_loaded": models_loaded,
        "available_models": list(models_cache.keys()),
        "expected_models": ["jina", "robertalex", "jina-v3", "legal-bert", "roberta-ca"],
        "models_count": len(models_cache)
    }

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)