generator_function/multiodel_image_processor.py

import os, zipfile, tempfile, logging, base64
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Tuple, Optional
from PIL import Image
import io

from generator_function.image_function import generate_image
from prompt.prompt_services import get_prompts
from multimodel_services.replicate_generation_service import generate_image_with_model, convert_size_to_aspect_ratio
from multimodel_services.model_manager import is_gpt_model, get_all_parameters
from helpers_function.helper_meta_data import meta_data_helper_function
from helpers_function.helpers import upload_image_to_r2
from helpers_function.helpers import is_valid_image
from database.connections import get_results_collection as get_collection
from database.operations import start_job, finish_job
from util.session_state import current_uid

logger = logging.getLogger(__name__)
COL = get_collection()

def _resolve_user_id() -> str:
    return current_uid() or os.getenv("DEFAULT_USER_ID", "anonymous")

def process_zip_and_generate_images_multimodel(
    zip_path: str,
    category: str,
    size: str,
    quality: str,
    user_prompt: str,
    sentiment: str,
    platform: str,
    num_images: int,
    demo_mode: bool,
    existing_images: Optional[List[str]],
    blur: bool,
    uid: str,
    selected_model: str = "gpt_default",
    model_params: Optional[dict] = None,
) -> List[str]:
    """Enhanced image processor that supports both GPT and multimodel approaches"""
    num_images = 1 if demo_mode else num_images
    try:
        if zip_path.endswith(".zip"):
            temp_dir = extract_zip_file(zip_path)
            image_files = get_valid_image_files(temp_dir)
        else:
            image_files = [(os.path.basename(zip_path), zip_path)]

        results = process_image_files_multimodel(
            image_files, category, size, quality, user_prompt, sentiment, platform, 
            num_images, blur, uid, selected_model, model_params
        )
        all_urls = [url for entry in results for url in entry["urls"]]
        seen, deduped = set(), []
        for u in all_urls:
            if u not in seen:
                seen.add(u); deduped.append(u)
        return (existing_images or []) + deduped
    except Exception:
        logger.exception(f"Global error during processing file: {zip_path}")
        return existing_images or []

def extract_zip_file(zip_path: str) -> tempfile.TemporaryDirectory:
    temp_dir = tempfile.TemporaryDirectory()
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(temp_dir.name)
    logger.info(f"Extracted ZIP file: {zip_path}")
    return temp_dir

def get_valid_image_files(temp_dir: tempfile.TemporaryDirectory) -> List[Tuple[str, str]]:
    valid_files: List[Tuple[str, str]] = []
    for file in os.listdir(temp_dir.name):
        if "__MACOSX" in file: continue
        file_path = os.path.join(temp_dir.name, file)
        if is_valid_image(file):
            valid_files.append((file, file_path))
        else:
            logger.warning(f"Ignored non-image file: {file}")
    logger.info(f"Found {len(valid_files)} valid images.")
    return valid_files

def process_image_files_multimodel(image_files: List[Tuple[str, str]], category: str, size: str,
    quality: str, user_prompt: str, sentiment: str, platform: str, num_images: int, blur: bool, 
    uid: str, selected_model: str, model_params: Optional[dict]) -> List[dict]:
    """Process image files with multimodel support"""
    final_results: List[dict] = []
    with ThreadPoolExecutor(max_workers=5) as executor:
        futures = []
        for file_name, file_path in image_files:
            job_id: Optional[str] = None
            if COL is not None:
                try:
                    settings = {
                        "size": size, "quality": quality, "sentiment": sentiment, 
                        "platform": platform, "num_images": num_images, "blur": bool(blur),
                        "selected_model": selected_model, "model_params": model_params or {}
                    }
                    inputs = {"file_name": file_name, "mode": "img_or_zip_multimodel"}
                    job_id = start_job(
                        COL,
                        type="variation_multimodel",
                        created_by=uid,
                        category=category or "general",
                        inputs=inputs,
                        settings=settings,
                        user_prompt=user_prompt
                    )
                except Exception:
                    logger.exception("Failed to start DB job; continuing without DB logging.")
            futures.append(
                executor.submit(
                    process_single_image_multimodel,
                    file_name, file_path, category, size, quality, user_prompt, sentiment, 
                    platform, num_images, blur, job_id, selected_model, model_params,
                )
            )
        for future in as_completed(futures):
            try:
                result = future.result()
                if result: final_results.append(result)
            except Exception:
                logger.exception("Unhandled exception during image processing thread.")
    return final_results

def process_single_image_multimodel(file_name: str, file_path: str, category: str, size: str, 
    quality: str, user_prompt: str, sentiment: str, platform: str, num_images: int, blur: bool, 
    job_id: Optional[str], selected_model: str, model_params: Optional[dict]) -> Optional[dict]:
    """Process single image with multimodel support"""
    try:
        image_urls = generate_images_from_prompts_multimodel(
            file_path, size, quality, category, sentiment, user_prompt, platform, 
            num_images, blur, selected_model, model_params
        )
        if COL is not None and job_id:
            try:
                finish_job(COL, job_id, status=("completed" if image_urls else "failed"), outputs_urls=image_urls)
            except Exception:
                logger.exception("Failed to finish DB job.")
        if image_urls:
            return {"file_name": file_name, "urls": image_urls}
        return None
    except Exception as e:
        logger.error(f"Processing failed for {file_name}: {e}")
        if COL is not None and job_id:
            try:
                finish_job(COL, job_id, status="failed", outputs_urls=[])
            except Exception:
                logger.exception("Also failed to mark DB job as failed.")
        return None

def generate_images_from_prompts_multimodel(
    file_path: str, size: str, quality: str, category: str, sentiment: str, user_prompt: str,
    platform: str, num_images: int, blur: bool, selected_model: str, model_params: Optional[dict],
) -> List[str]:
    """Generate images using either GPT or multimodel approach"""
    image_urls: List[str] = []

    def worker(i: int) -> Optional[str]:
        try:
            if is_gpt_model(selected_model):
                # Use existing GPT approach
                image_bytes = generate_image(file_path, size, quality, category, sentiment, user_prompt, platform, blur, i)
            else:
                # Use multimodel approach
                image_bytes = generate_image_multimodel(
                    file_path, selected_model, category, sentiment, user_prompt, 
                    platform, size, model_params, i
                )
            
            if not image_bytes: return None
            image_with_metadata = meta_data_helper_function(image_bytes)
            s3_url = upload_image_to_r2(image_with_metadata)
            return s3_url
        except Exception as e:
            logger.error(f"Image generation failed: {e}")
            return None

    with ThreadPoolExecutor(max_workers=min(10, num_images)) as executor:
        futures = [executor.submit(worker, i) for i in range(num_images)]
        for future in as_completed(futures):
            result = future.result()
            if result: image_urls.append(result)
    return image_urls

def generate_image_multimodel(file_path: str, model_name: str, category: str, sentiment: str, 
                            user_prompt: str, platform: str, size: str, model_params: Optional[dict], 
                            variation_index: int) -> Optional[bytes]:
    """Generate image using multimodel approach"""
    try:
        # Convert image to base64
        with open(file_path, 'rb') as f:
            image_data = f.read()
        base64_image = base64.b64encode(image_data).decode()
        
        # Use existing prompt service to get prompt variations
        prompt_variations = get_prompts(
            base64_image, category, user_prompt, sentiment, None
        )
        
        # Select a prompt based on variation index
        if prompt_variations and len(prompt_variations) > 0:
            selected_prompt = prompt_variations[variation_index % len(prompt_variations)]
        else:
            # Fallback prompt
            selected_prompt = f"Generate a high-quality {category or 'advertising'} image. {user_prompt}"
        
        # Prepare model parameters
        all_params = get_all_parameters(model_name, model_params)
        
        # Only convert size to aspect ratio if no aspect ratio was provided by user
        if model_name == "google/nano-banana" and "aspect_ratio" in all_params:
            # If aspect_ratio is the default value, convert from size
            if all_params["aspect_ratio"] == "match_input_image":  # This is the default
                converted_ratio = convert_size_to_aspect_ratio(size, model_name)
                all_params["aspect_ratio"] = converted_ratio
                logger.info(f"Converted size '{size}' to aspect ratio '{converted_ratio}' for {model_name}")
            else:
                logger.info(f"Using user-selected aspect ratio '{all_params['aspect_ratio']}' for {model_name}")
        
        logger.info(f"Final parameters for {model_name}: {all_params}")
        
        # Generate image with selected model
        generated_image_data = generate_image_with_model(
            model_name, selected_prompt, all_params, base64_image
        )
        
        return generated_image_data
        
    except Exception as e:
        logger.error(f"Multimodel generation failed: {e}")
        return None