app.py

from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List
import asyncio
import os
from concurrent.futures import ThreadPoolExecutor
from src.Generate_caption import load_model_from_path, tokenizer_load
from src.Color_extraction import extract_colors
from src.Generate_productName_description import generate_product_name, generate_description, clean_response
from huggingface_hub import hf_hub_download
import tempfile

app = FastAPI()

# Load environment variables
API_KEY = os.environ.get("APIKey")

if not API_KEY:
    print(API_KEY)
    raise ValueError("API_KEY not set. Please configure your .env file or system environment.")

# Global variables for models and ThreadPool
vgg16_model = None
fifth_version_model = None
tokenizer = None
executor = ThreadPoolExecutor(max_workers=4)

# Ensure ONNX model path is set
HF_CACHE_DIR = "/app/hf_models_cache"
os.makedirs(HF_CACHE_DIR, exist_ok=True)

os.environ["XDG_CACHE_HOME"] = "/app/onnx_cache"
os.makedirs(os.environ["XDG_CACHE_HOME"], exist_ok=True)

async def download_model_from_hf(repo_id: str, filename: str) -> str:
    try:
        # Use the defined cache directory
        model_path = hf_hub_download(
            repo_id=repo_id,
            filename=filename,
            cache_dir=HF_CACHE_DIR, # Use defined cache dir
            local_dir=HF_CACHE_DIR, # Ensure download to this dir
            force_download=False # Avoid re-downloading if already cached
        )
        print(f"Using model {filename} from {model_path}")
        return model_path
    except Exception as e:
        print(f"Error downloading/finding {filename}: {str(e)}")
        raise


async def load_models():
    global vgg16_model, fifth_version_model, tokenizer
    if not all([vgg16_model, fifth_version_model, tokenizer]):
        print("Downloading and loading models from Hugging Face Hub...")

        try:
            # Download models in parallel
            vgg16_path, model_path, tokenizer_path = await asyncio.gather(
                download_model_from_hf("abdallah-03/AI_product_helper_models", "vgg16_feature_extractor.keras"),
                download_model_from_hf("abdallah-03/AI_product_helper_models", "fifth_version_model.keras"),
                download_model_from_hf("abdallah-03/AI_product_helper_models", "tokenizer.pkl")
            )

            # Load models using the downloaded paths
            vgg16_task = asyncio.to_thread(load_model_from_path, vgg16_path)
            fifth_version_task = asyncio.to_thread(load_model_from_path, model_path)
            tokenizer_task = asyncio.to_thread(tokenizer_load, tokenizer_path)

            vgg16_model, fifth_version_model, tokenizer = await asyncio.gather(
                vgg16_task, fifth_version_task, tokenizer_task
            )
            print("Models loaded successfully!")

        except Exception as e:
            print(f"Error loading models: {str(e)}")
            raise


@app.on_event("startup")
async def startup_event():
    asyncio.create_task(load_models())


# Pydantic Models
class ImagePathsRequest(BaseModel):
    image_paths: List[str]


class GenerateProductRequest(ImagePathsRequest):
    Brand_name: str


class GenerateDescriptionRequest(BaseModel):
    product_name: str


class AIproducthelper(ImagePathsRequest):
    Brand_name: str


# Exception Handlers
@app.exception_handler(Exception)
async def global_exception_handler(request: Request, exc: Exception):
    return JSONResponse(
        status_code=500,
        content={"success": False, "message": "Internal Server Error", "error": repr(exc)},
    )


@app.exception_handler(HTTPException)
async def http_exception_handler(request: Request, exc: HTTPException):
    return JSONResponse(
        status_code=exc.status_code,
        content={"success": False, "message": exc.detail},
    )


@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request: Request, exc: RequestValidationError):
    return JSONResponse(
        status_code=422,
        content={"success": False, "message": "Validation Error", "errors": exc.errors()},
    )


# Endpoints
@app.get("/")
def read_root():
    return {"message": "Hello from our API, models are loading in the background!"}


@app.get("/status/")
async def check_status():
    if all([vgg16_model, fifth_version_model, tokenizer]):
        return {
            "success": True,
            "message": "Models are ready!",
            "models_loaded": {
                "vgg16": vgg16_model is not None,
                "fifth_version": fifth_version_model is not None,
                "tokenizer": tokenizer is not None
            }
        }
    return {
        "success": False,
        "message": "Models are still loading...",
        "models_loaded": {
            "vgg16": vgg16_model is not None,
            "fifth_version": fifth_version_model is not None,
            "tokenizer": tokenizer is not None
        }
    }


@app.post("/extract-colors/")
async def extract_colors_endpoint(request: ImagePathsRequest):
    if not request.image_paths:
        raise HTTPException(status_code=400, detail="Image list cannot be empty.")

    try:
        colors = await asyncio.get_event_loop().run_in_executor(executor, extract_colors, request.image_paths)
        return {"success": True, "colors": colors}
    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"Error extracting colors: {repr(exc)}")


@app.post("/generate-product-name/")
async def generate_product_name_endpoint(request: GenerateProductRequest):
    if not request.image_paths:
        raise HTTPException(status_code=400, detail="Image list cannot be empty.")

    try:
        product_name = await asyncio.get_event_loop().run_in_executor(
            executor, generate_product_name, request.image_paths, request.Brand_name,
            vgg16_model, fifth_version_model, tokenizer, API_KEY
        )
        return {"success": True, "product_name": product_name}
    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"Error generating product name: {repr(exc)}")


@app.post("/generate-description/")
async def generate_description_endpoint(request: GenerateDescriptionRequest):
    try:
        description = await asyncio.get_event_loop().run_in_executor(
            executor, generate_description, API_KEY, request.product_name,
            vgg16_model, fifth_version_model, tokenizer
        )
        return {"success": True, "description": description}
    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"Error generating description: {repr(exc)}")


@app.post("/AI-product_help/")
async def ai_product_help_endpoint(request: AIproducthelper):
    if not request.image_paths:
        raise HTTPException(status_code=400, detail="Image list cannot be empty.")

    try:
        product_name = await asyncio.get_event_loop().run_in_executor(
            executor, generate_product_name, request.image_paths, request.Brand_name,
            vgg16_model, fifth_version_model, tokenizer, API_KEY
        )
        product_name = clean_response(product_name)

        description = await asyncio.get_event_loop().run_in_executor(
            executor, generate_description, API_KEY, product_name,
            vgg16_model, fifth_version_model, tokenizer
        )
        description = clean_response(description)

        return {"success": True, "product_name": product_name, "description": description}

    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"Error in AI product helper: {repr(exc)}")