app.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 = {}

# Load jina-v3 at startup (most important model)
STARTUP_MODEL = "jina-v3"

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan handler for startup and shutdown"""
    # Startup - load jina-v3 model
    try:
        global models_cache
        print(f"Loading startup model: {STARTUP_MODEL}...")
        models_cache = load_models([STARTUP_MODEL])
        print(f"Startup model loaded successfully: {list(models_cache.keys())}")
        yield
    except Exception as e:
        print(f"Failed to load startup model: {str(e)}")
        # Continue anyway - jina-v3 can be loaded on demand if startup fails
        yield
    finally:
        # Shutdown - cleanup resources
        cleanup_memory()

def ensure_model_loaded(model_name: str, max_length_limit: int):
    """Load a specific model on demand if not already loaded"""
    global models_cache
    if model_name not in models_cache:
        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)}")

def validate_request_for_model(request: EmbeddingRequest, model_name: str, max_length_limit: int):
    """Validate request parameters for specific model"""
    if not request.texts:
        raise HTTPException(status_code=400, detail="No texts provided")
    
    if len(request.texts) > 50:
        raise HTTPException(status_code=400, detail="Maximum 50 texts per request")
    
    if request.max_length is not None and request.max_length > max_length_limit:
        raise HTTPException(status_code=400, detail=f"Max length for {model_name} is {max_length_limit}")

app = FastAPI(
    title="Multilingual & Legal Embedding API",
    description="Multi-model embedding API with dedicated endpoints per model",
    version="4.0.0",
    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 - Endpoint Per Model",
        "version": "4.0.0",
        "status": "running",
        "docs": "/docs",
        "startup_model": STARTUP_MODEL,
        "available_endpoints": {
            "jina-v3": "/embed/jina-v3",
            "roberta-ca": "/embed/roberta-ca", 
            "jina": "/embed/jina",
            "robertalex": "/embed/robertalex",
            "legal-bert": "/embed/legal-bert"
        }
    }

# Jina v3 - Multilingual (loads at startup)
@app.post("/embed/jina-v3", response_model=EmbeddingResponse)
async def embed_jina_v3(request: EmbeddingRequest):
    """Generate embeddings using Jina v3 model (multilingual)"""
    try:
        ensure_model_loaded("jina-v3", 8192)
        validate_request_for_model(request, "jina-v3", 8192)
        
        embeddings = get_embeddings(
            request.texts,
            "jina-v3",
            models_cache,
            request.normalize,
            request.max_length
        )
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used="jina-v3",
            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)}")

# Catalan RoBERTa
@app.post("/embed/roberta-ca", response_model=EmbeddingResponse)
async def embed_roberta_ca(request: EmbeddingRequest):
    """Generate embeddings using Catalan RoBERTa model"""
    try:
        ensure_model_loaded("roberta-ca", 512)
        validate_request_for_model(request, "roberta-ca", 512)
        
        embeddings = get_embeddings(
            request.texts,
            "roberta-ca",
            models_cache,
            request.normalize,
            request.max_length
        )
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used="roberta-ca",
            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)}")

# Jina v2 - Spanish/English
@app.post("/embed/jina", response_model=EmbeddingResponse)
async def embed_jina(request: EmbeddingRequest):
    """Generate embeddings using Jina v2 Spanish/English model"""
    try:
        ensure_model_loaded("jina", 8192)
        validate_request_for_model(request, "jina", 8192)
        
        embeddings = get_embeddings(
            request.texts,
            "jina",
            models_cache,
            request.normalize,
            request.max_length
        )
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used="jina",
            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)}")

# RoBERTalex - Spanish Legal
@app.post("/embed/robertalex", response_model=EmbeddingResponse)
async def embed_robertalex(request: EmbeddingRequest):
    """Generate embeddings using RoBERTalex Spanish legal model"""
    try:
        ensure_model_loaded("robertalex", 512)
        validate_request_for_model(request, "robertalex", 512)
        
        embeddings = get_embeddings(
            request.texts,
            "robertalex",
            models_cache,
            request.normalize,
            request.max_length
        )
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used="robertalex",
            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)}")

# Legal BERT - English Legal
@app.post("/embed/legal-bert", response_model=EmbeddingResponse)
async def embed_legal_bert(request: EmbeddingRequest):
    """Generate embeddings using Legal BERT English model"""
    try:
        ensure_model_loaded("legal-bert", 512)
        validate_request_for_model(request, "legal-bert", 512)
        
        embeddings = get_embeddings(
            request.texts,
            "legal-bert",
            models_cache,
            request.normalize,
            request.max_length
        )
        
        return EmbeddingResponse(
            embeddings=embeddings,
            model_used="legal-bert",
            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-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 - loaded at startup"
        ),
        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"
        ),
        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="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"
        )
    ]

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    startup_loaded = STARTUP_MODEL in models_cache
    
    return {
        "status": "healthy" if startup_loaded else "partial",
        "startup_model": STARTUP_MODEL,
        "startup_model_loaded": startup_loaded,
        "available_models": list(models_cache.keys()),
        "models_count": len(models_cache),
        "endpoints": {
            "jina-v3": f"/embed/jina-v3 {'(ready)' if 'jina-v3' in models_cache else '(loads on demand)'}",
            "roberta-ca": f"/embed/roberta-ca {'(ready)' if 'roberta-ca' in models_cache else '(loads on demand)'}",
            "jina": f"/embed/jina {'(ready)' if 'jina' in models_cache else '(loads on demand)'}",
            "robertalex": f"/embed/robertalex {'(ready)' if 'robertalex' in models_cache else '(loads on demand)'}",
            "legal-bert": f"/embed/legal-bert {'(ready)' if 'legal-bert' in models_cache else '(loads on demand)'}"
        }
    }

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)