app/main_backup.py

import os
import time
import asyncio
import importlib
import subprocess
import sys
from fastapi import FastAPI, HTTPException, Depends, Body
from typing import Optional, List
from pydantic import ValidationError

# llama-cpp-python installed at runtime with CUDA support
try:
    import llama_cpp
except ImportError:
    print("[STARTUP] Installing llama-cpp-python with CUDA...")
    env = os.environ.copy()
    result = subprocess.run(
        [sys.executable, "-m", "pip", "install", "--quiet", "--prefer-binary", 
         "--index-url", "https://abetlen.github.io/llama-cpp-python/whl/cu121",
         "llama-cpp-python[server]>=0.3.16"],
        capture_output=True,
        text=True
    )
    if result.returncode != 0:
        print("[STARTUP] CUDA wheel failed, trying CPU fallback...")
        print(f"[STARTUP] Error details: {result.stderr[:500]}")
        subprocess.run([sys.executable, "-m", "pip", "install", "--quiet", "llama-cpp-python>=0.3.16"], check=False)
    else:
        print("[STARTUP] llama-cpp-python with CUDA installed")

from app.models.registry import registry, MODEL_CONFIG
from fastapi.middleware.cors import CORSMiddleware
from app.schemas.schemas import (
    EnhancedDescriptionResponse,
    CompareRequest,
    CompareResponse,
    ModelResult,
    ModelInfo,
    InfillRequest,
    InfillResponse,
    InfillResult,
    GapFill,
    CompareInfillRequest,
    CompareInfillResponse,
    ModelInfillResult,
)
from app.logic.infill_utils import (
    detect_gaps,
    parse_infill_response,
    apply_fills,
    build_fills_dict,
    normalize_gaps_to_tagged,
)
from app.auth.placeholder_auth import get_authenticated_user

app = FastAPI(
    title="Multi-Model Description Enhancer",
    description="AI-powered service for enhancing descriptions using multiple LLMs for A/B testing",
    version="3.0.0"
)

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:5173",
        "http://localhost:5174",
        os.getenv("FRONTEND_URL", "http://localhost:5173")
    ],
    allow_credentials=True,
    allow_methods=["POST", "GET"],
    allow_headers=["*"],
)

@app.on_event("startup")
async def startup_event():
    """
    Startup event - models are loaded lazily on first request.
    No models are pre-loaded to conserve memory.
    """
    print("Application started. Models will be loaded lazily on first request.")
    print(f"Available models: {registry.get_available_model_names()}")
    
    try:
        import torch
        gpu_available = torch.cuda.is_available()
        gpu_name = torch.cuda.get_device_name(0) if gpu_available else "N/A"
        print(f"GPU available: {gpu_available}, Device: {gpu_name}")
    except ImportError:
        print("PyTorch not available for GPU check")
    except Exception as e:
        print(f"GPU check failed: {e}")

# --- Helper function to load domain logic ---
def get_domain_config(domain: str):
    try:
        module = importlib.import_module(f"app.domains.{domain}.config")
        return module.domain_config
    except (ImportError, AttributeError):
        raise HTTPException(status_code=404, detail=f"Domain '{domain}' not found or not configured correctly.")

# --- API Endpoints ---

@app.get("/")
async def read_root():
    return {"message": "Welcome to the Multi-Model Description Enhancer API! Go to /docs for documentation."}

@app.get("/health")
async def health_check():
    """Check API health and model status."""
    models = registry.list_models()
    loaded_models = registry.get_loaded_models()
    active_model = registry.get_active_model()
    
    gpu_available = False
    gpu_name = "N/A"
    try:
        import torch
        gpu_available = torch.cuda.is_available()
        gpu_name = torch.cuda.get_device_name(0) if gpu_available else "N/A"
    except:
        pass
    
    return {
        "status": "ok",
        "available_models": len(models),
        "loaded_models": loaded_models,
        "active_local_model": active_model,
        "gpu_available": gpu_available,
        "gpu_device": gpu_name,
    }

@app.get("/models", response_model=List[ModelInfo])
async def list_models():
    """List all available models with their load status."""
    return registry.list_models()

@app.post("/models/{model_name}/load")
async def load_model(model_name: str):
    """
    Explicitly load a model into memory.
    For local models: unloads any previously loaded local model first.
    """
    if model_name not in registry.get_available_model_names():
        raise HTTPException(status_code=404, detail=f"Unknown model: {model_name}")
    
    try:
        info = await registry.load_model(model_name)
        return {"status": "loaded", "model": info}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to load model: {str(e)}")

@app.post("/models/{model_name}/unload")
async def unload_model(model_name: str):
    """
    Explicitly unload a model from memory to free resources.
    """
    if model_name not in registry.get_available_model_names():
        raise HTTPException(status_code=404, detail=f"Unknown model: {model_name}")
    
    try:
        result = await registry.unload_model(model_name)
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to unload model: {str(e)}")

@app.post("/enhance-description", response_model=EnhancedDescriptionResponse)
async def enhance_description(
    domain: str = Body(..., embed=True),
    data: dict = Body(..., embed=True),
    model: str = Body("bielik-1.5b", embed=True),
    user: Optional[dict] = Depends(get_authenticated_user)
):
    """
    Generate an enhanced description using a single model.
    - **domain**: The name of the domain (e.g., 'cars').
    - **data**: A dictionary with the data for the description.
    - **model**: Model to use (default: bielik-1.5b)
    """
    start_time = time.time()
    
    # Validate model
    if model not in registry.get_available_model_names():
        raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
    
    # Load Domain Configuration
    domain_config = get_domain_config(domain)
    DomainSchema = domain_config["schema"]
    create_prompt = domain_config["create_prompt"]

    # Validate Input Data
    try:
        validated_data = DomainSchema(**data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=f"Invalid data for domain '{domain}': {e}")

    # Prompt Construction
    chat_messages = create_prompt(validated_data)

    # Text Generation
    try:
        llm = await registry.get_model(model)
        generated_description = await llm.generate(
            chat_messages=chat_messages,
            max_new_tokens=150,
            temperature=0.75,
            top_p=0.9,
        )
    except Exception as e:
        print(f"Error during text generation with {model}: {e}")
        raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")

    generation_time = time.time() - start_time
    user_email = user['email'] if user else "anonymous"

    return EnhancedDescriptionResponse(
        description=generated_description,
        model_used=MODEL_CONFIG[model]["id"],
        generation_time=round(generation_time, 2),
        user_email=user_email
    )

@app.post("/compare", response_model=CompareResponse)
async def compare_models(
    request: CompareRequest,
    user: Optional[dict] = Depends(get_authenticated_user)
):
    """
    Compare outputs from multiple models for the same input.
    Returns results from all specified models (or all available if not specified).
    """
    total_start = time.time()
    
    # Get models to compare
    available_models = registry.get_available_model_names()
    models_to_use = request.models if request.models else available_models
    
    # Validate requested models
    for model in models_to_use:
        if model not in available_models:
            raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
    
    # Load Domain Configuration
    domain_config = get_domain_config(request.domain)
    DomainSchema = domain_config["schema"]
    create_prompt = domain_config["create_prompt"]

    # Validate Input Data
    try:
        validated_data = DomainSchema(**request.data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=f"Invalid data: {e}")

    # Prompt Construction
    chat_messages = create_prompt(validated_data)

    # Generate with each model
    results = []
    
    async def generate_with_model(model_name: str) -> ModelResult:
        start_time = time.time()
        try:
            llm = await registry.get_model(model_name)
            output = await llm.generate(
                chat_messages=chat_messages,
                max_new_tokens=150,
                temperature=0.75,
                top_p=0.9,
            )
            return ModelResult(
                model=model_name,
                output=output,
                time=round(time.time() - start_time, 2),
                type=MODEL_CONFIG[model_name]["type"],
                error=None
            )
        except Exception as e:
            return ModelResult(
                model=model_name,
                output="",
                time=round(time.time() - start_time, 2),
                type=MODEL_CONFIG[model_name]["type"],
                error=str(e)
            )
    
    # Run all models (sequentially to avoid memory issues)
    for model_name in models_to_use:
        result = await generate_with_model(model_name)
        results.append(result)
    
    return CompareResponse(
        domain=request.domain,
        results=results,
        total_time=round(time.time() - total_start, 2)
    )

@app.get("/user/me")
async def get_user_info(user: dict = Depends(get_authenticated_user)):
    """Get current authenticated user information"""
    if not user:
        raise HTTPException(status_code=401, detail="Not authenticated")
    return {
        "user_id": user['user_id'],
        "email": user['email'],
        "name": user.get('name', 'Unknown')
    }


# --- Batch Infill Endpoints ---

@app.post("/infill", response_model=InfillResponse)
async def batch_infill(
    request: InfillRequest,
    user: Optional[dict] = Depends(get_authenticated_user)
):
    """
    Batch gap-filling for ads using a single model.
    
    Accepts items with [GAP:n] markers or ___ and returns filled text
    with per-gap choices and alternatives.
    
    NOTE: For texts > 6000 chars, consider chunking (not yet implemented).
    """
    print(f"DEBUG: Hit batch_infill endpoint with model={request.model}", flush=True)
    total_start = time.time()
    
    # Validate model
    if request.model not in registry.get_available_model_names():
        raise HTTPException(status_code=400, detail=f"Unknown model: {request.model}")
    
    # Load domain config for infill prompt
    domain_config = get_domain_config(request.domain)
    if "create_infill_prompt" not in domain_config:
        raise HTTPException(
            status_code=400,
            detail=f"Domain '{request.domain}' does not support infill operations"
        )
    create_infill_prompt = domain_config["create_infill_prompt"]
    
    # Process each item
    results = []
    error_count = 0
    
    for item in request.items:
        result = await process_infill_item(
            item=item,
            model_name=request.model,
            options=request.options,
            create_infill_prompt=create_infill_prompt
        )
        results.append(result)
        if result.status == "error":
            error_count += 1
    
    return InfillResponse(
        model=request.model,
        results=results,
        total_time=round(time.time() - total_start, 2),
        processed_count=len(results),
        error_count=error_count
    )


@app.post("/compare-infill", response_model=CompareInfillResponse)
async def compare_infill(
    request: CompareInfillRequest,
    user: Optional[dict] = Depends(get_authenticated_user)
):
    """
    Multi-model batch gap-filling comparison for A/B testing.
    
    Runs the same batch of items through multiple models and returns
    per-model results for comparison.
    """
    total_start = time.time()
    
    # Get models to compare
    available_models = registry.get_available_model_names()
    models_to_use = request.models if request.models else available_models
    
    # Validate requested models
    for model in models_to_use:
        if model not in available_models:
            raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
    
    # Load domain config
    domain_config = get_domain_config(request.domain)
    if "create_infill_prompt" not in domain_config:
        raise HTTPException(
            status_code=400,
            detail=f"Domain '{request.domain}' does not support infill operations"
        )
    create_infill_prompt = domain_config["create_infill_prompt"]
    
    # Process with each model (sequentially for memory safety)
    model_results = []
    
    for model_name in models_to_use:
        model_start = time.time()
        results = []
        error_count = 0
        
        for item in request.items:
            result = await process_infill_item(
                item=item,
                model_name=model_name,
                options=request.options,
                create_infill_prompt=create_infill_prompt
            )
            results.append(result)
            if result.status == "error":
                error_count += 1
        
        model_results.append(ModelInfillResult(
            model=model_name,
            type=MODEL_CONFIG[model_name]["type"],
            results=results,
            time=round(time.time() - model_start, 2),
            error_count=error_count
        ))
    
    return CompareInfillResponse(
        domain=request.domain,
        models=model_results,
        total_time=round(time.time() - total_start, 2)
    )


async def process_infill_item(
    item,
    model_name: str,
    options,
    create_infill_prompt
) -> InfillResult:
    """
    Process a single infill item.
    
    Returns InfillResult with status, filled_text, and gaps.
    """
    try:
        # Normalize gaps to [GAP:n] format
        normalized_text, gaps = normalize_gaps_to_tagged(item.text_with_gaps)
        
        if not gaps:
            # No gaps found, return original text
            return InfillResult(
                id=item.id,
                status="ok",
                filled_text=item.text_with_gaps,
                gaps=[],
                error=None
            )
        
        # Build prompt
        if item.custom_messages:
            chat_messages = item.custom_messages
            use_grammar = False  # Custom messages = plain text output expected
        else:
            chat_messages = create_infill_prompt(normalized_text, options, attributes=item.attributes)
            use_grammar = True  # Standard prompt = use grammar for structured JSON
        
        # Generate with optional GBNF grammar constraint
        llm = await registry.get_model(model_name)
        
        grammar_str = None
        if use_grammar and hasattr(llm, 'llm') and llm.llm is not None:
            # Use model's default grammar (loaded from answers.gbnf) if available
            if hasattr(llm, 'default_grammar') and llm.default_grammar:
                grammar_str = llm.default_grammar
                print(f"DEBUG: Using model's default GBNF grammar", flush=True)
            else:
                # Fallback to dynamic grammar generation
                try:
                    from app.logic.grammar_utils import get_infill_grammar
                    grammar_str = get_infill_grammar(len(gaps))
                    print(f"DEBUG: Using dynamic GBNF grammar for {len(gaps)} gaps", flush=True)
                except ImportError:
                    pass
        
        raw_output = await llm.generate(
            chat_messages=chat_messages,
            max_new_tokens=options.max_new_tokens,
            temperature=0.3 if use_grammar else options.temperature,  # Lower temp with grammar
            top_p=0.9,
            grammar=grammar_str,
        )
        
        # If custom_messages were provided, the output is plain text (not JSON)
        # Just return it directly as a single gap fill
        if item.custom_messages:
            # Clean up the raw output - strip whitespace, quotes, etc.
            choice = raw_output.strip().strip('"\'.,').strip()
            return InfillResult(
                id=item.id,
                status="ok",
                filled_text=choice,  # The filled text is just the choice itself
                gaps=[GapFill(index=1, marker="[GAP:1]", choice=choice, alternatives=[])],
                error=None
            )
        
        # Parse JSON from output (standard prompt format)
        parsed = parse_infill_response(raw_output)
        
        if not parsed:
            # JSON parsing failed
            return InfillResult(
                id=item.id,
                status="error",
                filled_text=None,
                gaps=[],
                error=f"Failed to parse JSON from model output: {raw_output[:200]}..."
            )
        
        # Extract gaps and build result
        gap_fills = []
        fills_dict = {}
        
        for gap_data in parsed.get("gaps", []):
            gap_fill = GapFill(
                index=gap_data.get("index", 0),
                marker=gap_data.get("marker", ""),
                choice=gap_data.get("choice", ""),
                alternatives=gap_data.get("alternatives", [])
            )
            gap_fills.append(gap_fill)
            fills_dict[gap_fill.index] = gap_fill.choice
        
        # Get filled text - prefer model's version, fallback to reconstruction
        filled_text = parsed.get("filled_text")
        if not filled_text and fills_dict:
            filled_text = apply_fills(normalized_text, gaps, fills_dict)
        
        return InfillResult(
            id=item.id,
            status="ok",
            filled_text=filled_text,
            gaps=gap_fills,
            error=None
        )
        
    except Exception as e:
        return InfillResult(
            id=item.id,
            status="error",
            filled_text=None,
            gaps=[],
            error=str(e)
        )