import spaces
import os
import json
import time
import copy
import numpy as np
import torch
import random
from diffusers import AutoPipelineForText2Image, StableDiffusionPipeline,DiffusionPipeline, StableDiffusionXLPipeline, AutoencoderKL, AutoencoderTiny, UNet2DConditionModel
from huggingface_hub import hf_hub_download, snapshot_download
from pathlib import Path
from diffusers import EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, DPMSolverSDEScheduler
from diffusers.models.attention_processor import AttnProcessor2_0
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives import serialization, hashes
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.asymmetric import utils
import base64
import json
import ipown
import jwt
import glob
import traceback
from insightface.app import FaceAnalysis
import cv2
import re
import gradio as gr
import uuid
from PIL import Image
MAX_SEED = np.iinfo(np.int32).max
#from onediffx import compile_pipe, save_pipe, load_pipe

HF_TOKEN = os.getenv('HF_TOKEN')
VAR_PUBLIC_KEY = os.getenv('PUBLIC_KEY')
DATASET_ID = 'nsfwalex/checkpoint_n_lora'
scheduler_config = {
    "num_train_timesteps": 1000,
    "beta_start": 0.00085,
    "beta_end": 0.012,
    "beta_schedule": "scaled_linear",
    "set_alpha_to_one": False,
    "steps_offset": 1,
    "prediction_type": "epsilon",
}
samplers = {
    "Euler a": EulerAncestralDiscreteScheduler.from_config(scheduler_config),
    "DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(scheduler_config, use_karras_sigmas=True),
    "DPM2 a": DPMSolverMultistepScheduler.from_config(scheduler_config),
    "DPM++ SDE": DPMSolverSDEScheduler.from_config(scheduler_config),
    "DPM++ 2M SDE": DPMSolverSDEScheduler.from_config(scheduler_config, use_2m=True),
    "DPM++ 2S a": DPMSolverMultistepScheduler.from_config(scheduler_config, use_2s=True)
}

class AuthHelper:
    def load_public_key_from_file(self):
        public_key_bytes = VAR_PUBLIC_KEY.encode('utf-8')  # Convert to bytes if it's a string
        public_key = serialization.load_pem_public_key(
            public_key_bytes,
            backend=default_backend()
        )
        return public_key

    def __init__(self):
        self.public_key = self.load_public_key_from_file()

    # check authkey
    # 1. decode with public key
    # 2. check timestamp
    # 3. check current host, referer, ip it should be the same as values in jwt

    def decode_jwt(self, token, algorithms=["RS256"]):
        """
        Decode and verify a JWT using a public key.
        
        :param public_key: The public key used for verification.
        :param token: The JWT string to decode.
        :param algorithms: List of acceptable algorithms (default is ["RS256"]).
        :return: The decoded JWT payload if verification is successful.
        :raises: Exception if verification fails.
        """
        try:
            # Decode the JWT
            decoded_payload = jwt.decode(
                token,
                self.public_key,
                algorithms=algorithms,
                options={"verify_signature": True}  # Explicitly enable signature verification
            )
            return decoded_payload
        except Exception as e:
            print("Invalid token:", e)
            raise

    import hashlib

    def check_auth(self, request, token):
        # Extract parameters from the request
        if not request or request.query_params.get("_skip_token_passkey", "") == "nsfwaisio_125687":
            return True
        params = dict(request.query_params)
        # Gather request-specific information
        sip = request.client.host
        shost = request.headers.get("Host", "")
        sreferer = request.headers.get("Referer", "")
        suseragent = request.headers.get("User-Agent", "")

        print(sip, shost, sreferer, suseragent)

        # Decode the JWT token
        jwt_data = self.decode_jwt(token)
        jwt_auth = jwt_data.get("auth", "")

        if not jwt_auth:
            raise Exception("Missing auth field in token")

        # Create the MD5 hash of ip + host + referer + useragent
        auth_string = f"{sip}{shost}{sreferer}{suseragent}"
        calculated_md5 = hashlib.md5(auth_string.encode('utf-8')).hexdigest()

        print(f"Calculated MD5: {calculated_md5}, JWT Auth: {jwt_auth}")

        # Compare the calculated hash with the `auth` field from the JWT
        if calculated_md5 == jwt_auth:
            return True

        raise Exception("Invalid authentication")

class InferenceManager:
    def __init__(self, config_path="config.json", ext_model_pathes={}):
        cfg = {}
        with open(config_path, "r", encoding="utf-8") as f:
            cfg = json.load(f)
        self.cfg = cfg
        self.ext_model_pathes = ext_model_pathes
        
        lora_options_path = cfg.get("loras", "")
        self.model_version = cfg["model_version"]
        self.lora_load_options = self.load_json(lora_options_path)  # Load LoRA load options
        self.lora_models = self.load_index_file("index.json")  # Load index.json
        self.preloaded_loras = []  # Array to store preloaded LoRAs with name and weights
        self.ip_adapter_faceid_pipeline = None
        self.base_model_pipeline = self.load_base_model()  # Load the base model
        
        self.preload_loras()  # Preload LoRAs based on options

    def load_json(self, filepath):
        """Load JSON file into a dictionary."""
        if os.path.exists(filepath):
            with open(filepath, "r", encoding="utf-8") as f:
                return json.load(f)
        return {}

    def load_index_file(self, index_file):
        """Download index.json from Hugging Face and return the file path."""
        index_path = download_from_hf(index_file)
        if index_path:
            with open(index_path, "r", encoding="utf-8") as f:
                return json.load(f)
        return {}

    @spaces.GPU(duration=40)
    def compile_onediff(self):
        self.base_model_pipeline.to("cuda")
        pipe = self.base_model_pipeline
        # load the compiled pipe
        load_pipe(pipe, dir="cached_pipe")
        print("Start oneflow compiling...")
        start_compile = time.time()
        pipe = compile_pipe(pipe)
        # run once to trigger compilation
        image = pipe(
            prompt="street style, detailed, raw photo, woman, face, shot on CineStill 800T",
            height=512,
            width=512,
            num_inference_steps=10,
            output_type="pil",
        ).images
        image[0].save(f"test_image.png")
        compile_time = time.time() - start_compile
        #self.base_model_pipeline.to("cpu")
        # save the compiled pipe
        save_pipe(pipe, dir="cached_pipe")
        self.base_model_pipeline = pipe
        print(f"OneDiff compile in {compile_time}s")
    
    def load_base_model(self):
        """Load base model and return the pipeline."""
        start = time.time()
        cfg = self.cfg

        model_version = self.model_version
        ckpt_dir = snapshot_download(repo_id=cfg["model_id"], local_files_only=False)

        if model_version == "1.5":
            vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), torch_dtype=torch.bfloat16)
            pipe = StableDiffusionPipeline.from_pretrained(ckpt_dir, vae=vae, torch_dtype=torch.bfloat16, use_safetensors=True)
        else:
            use_vae = cfg.get("vae", "")
            if not use_vae:
                vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), torch_dtype=torch.bfloat16)
            elif use_vae == "tae":
                vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.bfloat16)
            else:
                vae = AutoencoderTiny.from_pretrained(use_vae, torch_dtype=torch.bfloat16)
            print(ckpt_dir)
            pipe = StableDiffusionXLPipeline.from_pretrained(
                cfg["model_id"],#ckpt_dir,!TEST! directly use hf path
                #vae=vae,!TEST!remove vae
                #unet=unet,
                torch_dtype=torch.bfloat16,
                use_safetensors=True,
                add_watermarker=False,
                #variant="fp16",
                #custom_pipeline   = "lpw_stable_diffusion_xl",
            )
            #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
            clip_skip = cfg.get("clip_skip", 1)
            # Adjust clip skip for XL (assumed not relevant for SD 1.5)
            #pipe.text_encoder.config.num_hidden_layers -= (clip_skip - 1)#!TEST! remove skip clip
        
        load_time = round(time.time() - start, 2)
        print(f"Base model loaded in {load_time}s")

        if cfg.get("load_ip_adapter_faceid", False): #!!the pipeline can not be used independently anymore
            if model_version in ("pony", "xl"):
                ip_ckpt = self.ext_model_pathes.get("ip-adapter-faceid-sdxl", "")
                if ip_ckpt:
                    print(f"loading ip adapter model...")
                    self.ip_adapter_faceid_pipeline = ipown.IPAdapterFaceIDXL(pipe, ip_ckpt, 'cuda', torch_dtype=torch.bfloat16)
                else:
                    print("ip-adapter-faceid-sdxl not found, skip")            

        return pipe


    def preload_loras(self):
        """Preload all LoRAs marked as 'preload=True' and store for later use."""
        for lora_name, lora_info in self.lora_load_options.items():
            try:
                start = time.time()

                # Find the corresponding LoRA in index.json
                lora_index_info = next((l for l in self.lora_models['lora'] if l['name'] == lora_name), None)
                if not lora_index_info:
                    raise ValueError(f"LoRA {lora_name} not found in index.json.")

                # Check if the LoRA base model matches the current model version
                if self.model_version not in lora_info['base_model'] or not lora_info.get('preload', False):
                    print(f"Skipping {lora_name} as it's not compatible with the current model version.")
                    continue

                # Load LoRA weights from the specified path
                weight_path = download_from_hf(lora_index_info['path'], local_dir=None)
                if not weight_path:
                    raise ValueError(f"Failed to download LoRA weights for {lora_name}")
                load_time = round(time.time() - start, 2)
                print(f"Downloaded {lora_name} in {load_time}s")
                self.base_model_pipeline.load_lora_weights(
                    weight_path,
                    weight_name=lora_index_info["path"],
                    adapter_name=lora_name
                )

                # Store the preloaded LoRA name and weight for merging later
                if lora_info.get("preload", False):
                    self.preloaded_loras.append({
                        "name": lora_name,
                        "weight": lora_info.get("weight", 1.0)
                    })
                    load_time = round(time.time() - start, 2)
                    print(f"Preloaded LoRA {lora_name} with weight {lora_info.get('weight', 1.0)} in {load_time}s.")
            except Exception as e:
                print(f"Lora {lora_name} not loaded, skipping... {e}")

    def build_pipeline_with_lora(self, lora_list, sampler=None, new_pipeline=False):
        """Build the pipeline with specific LoRAs, loading any that are not preloaded."""
        # Deep copy the base pipeline
        start = time.time()
        if new_pipeline:
            temp_pipeline = copy.deepcopy(self.base_model_pipeline)
        else:
            temp_pipeline = self.base_model_pipeline
        copy_time = round(time.time() - start, 2)
        print(f"pipeline copied in {copy_time}s")
        # Track LoRAs to be loaded dynamically
        dynamic_loras = []

        # Check if any LoRAs in lora_list need to be loaded dynamically
        for lora_name in lora_list:
            if not any(l['name'] == lora_name for l in self.preloaded_loras):
                lora_info = next((l for l in self.lora_models['lora'] if l['name'] == lora_name), None)
                if lora_info and self.model_version in lora_info["attr"].get("base_model", []):
                    dynamic_loras.append({
                        "name": lora_name,
                        "filename": lora_info["path"],
                        "scale": 1.0  # Assuming default weight as 1.0 for dynamic LoRAs
                    })

        # Fuse preloaded and dynamic LoRAs
        all_loras = [{"name": x["name"], "scale": x["weight"], "preloaded": True} for x in self.preloaded_loras] + dynamic_loras
        set_lora_weights(temp_pipeline, all_loras,False)

        build_time = round(time.time() - start, 2)
        print(f"Pipeline built with LoRAs in {build_time}s.")
        if not sampler:
            sampler = self.cfg.get("sampler", "Euler a")
        # Define samplers
        
        # Set the scheduler based on the selected sampler
        temp_pipeline.scheduler = samplers[sampler]

        # Move the final pipeline to the GPU
        temp_pipeline
        return temp_pipeline

    def release(self, temp_pipeline):
        """Release the deepcopied pipeline to recycle memory."""
        del temp_pipeline
        torch.cuda.empty_cache()
        print("Memory released and cache cleared.")

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


child_related_regex = re.compile(
    r'(child|children|kid|kids|baby|babies|toddler|infant|juvenile|minor|underage|preteen|adolescent|youngster|youth|son|daughter|young|kindergarten|preschool|'
    r'([1-9]|1[0-7])[\s_\-|\.\,]*year(s)?[\s_\-|\.\,]*old|'  # Matches 1 to 17 years old with various separators
    r'little|small|tiny|short|young|new[\s_\-|\.\,]*born[\s_\-|\.\,]*(boy|girl|male|man|bro|brother|sis|sister))',
    re.IGNORECASE
)

# Function to remove child-related content from a prompt
def remove_child_related_content(prompt):
    cleaned_prompt = re.sub(child_related_regex, '', prompt)
    return cleaned_prompt.strip()

# Function to check if a prompt contains child-related content
def contains_child_related_content(prompt):
    if child_related_regex.search(prompt):
        return True
    return False

def save_image(img):
    path = "./tmp/"
    # Ensure the Hugging Face path exists locally
    if not os.path.exists(path):
        os.makedirs(path)
    # Generate a unique filename
    unique_name = str(uuid.uuid4()) + ".webp"
    unique_name = os.path.join(path, unique_name)
    
    # Convert the image to WebP format
    webp_img = img.convert("RGB")  # Ensure the image is in RGB mode
    
    # Save the image in WebP format with high quality
    webp_img.save(unique_name, "WEBP", quality=90)
    
    # Open the saved WebP file and return it as a PIL Image object
    with Image.open(unique_name) as webp_file:
        webp_image = webp_file.copy()
    
    return webp_image, unique_name

class ModelManager:
    def __init__(self, model_directory):
        """
        Initialize the ModelManager by scanning all `.model.json` files in the given directory.

        :param model_directory: The directory to scan for model config files (e.g., "/path/to/models").
        """
        print("downloading models")
        print("loading face analysis...")
        self.app = None
        #download_from_hf()
        self.ext_model_pathes = {
            "ip-adapter-faceid-sdxl": hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sdxl.bin", repo_type="model")
        }

        self.models = {}
        self.ext_models = {}
        self.model_directory = model_directory
        self.load_models()
        
    #not enabled at the moment
    def load_instant_x(self):
        #load all models
        hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
        hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
        hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
        os.makedirs("./models",exist_ok=True)
        download_from_hf("models/antelopev2/1k3d68.onnx",local_dir="./models")
        download_from_hf("models/antelopev2/2d106det.onnx",local_dir="./models")
        download_from_hf("models/antelopev2/genderage.onnx",local_dir="./models")
        download_from_hf("models/antelopev2/glintr100.onnx",local_dir="./models")
        download_from_hf("models/antelopev2/scrfd_10g_bnkps.onnx",local_dir="./models")
        
        # prepare 'antelopev2' under ./models
        app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
        app.prepare(ctx_id=0, det_size=(640, 640))

        # prepare models under ./checkpoints
        face_adapter = f'./checkpoints/ip-adapter.bin'
        controlnet_path = f'./checkpoints/ControlNetModel'

    def load_models(self):
        """
        Scan the model directory for `.model.json` files and initialize InferenceManager instances for each one.

        :param model_directory: Directory to scan for `.model.json` files.
        """
        model_files = glob.glob(os.path.join(self.model_directory, "*.model.json"))
        if not model_files:
            print(f"No model configuration files found in {self.model_directory}")
            return

        for file_path in model_files:
            model_name = self.get_model_name_from_url(file_path).split(".")[0]
            print(f"Initializing model: {model_name} from {file_path}")
            try:
                # Initialize InferenceManager for each model
                self.models[model_name] = InferenceManager(config_path=file_path, ext_model_pathes=self.ext_model_pathes)
            except Exception as e:
                print(traceback.format_exc())
                print(f"Failed to initialize model {model_name} from {file_path}: {e}")

    def get_model_name_from_url(self, url):
        """
        Extract the model name from the config file path (filename without extension).

        :param url: The file path of the configuration file.
        :return: The model name (file name without extension).
        """
        filename = os.path.basename(url)
        model_name, _ = os.path.splitext(filename)
        return model_name

    def get_model_pipeline(self, model_id, lora_list, sampler=None, new_pipeline=False):
        """
        Build the pipeline with specific LoRAs for a model.

        :param model_id: The model ID (the model name extracted from the config URL).
        :param lora_list: List of LoRAs to be applied to the model pipeline.
        :param sampler: The sampler to be used for the pipeline.
        :param new_pipeline: Flag to indicate whether to create a new pipeline or reuse the existing one.
        :return: The built pipeline with LoRAs applied.
        """
        model = self.models.get(model_id)
        if not model:
            print(f"Model {model_id} not found.")
            return None
        try:
            print(f"Building pipeline with LoRAs for model {model_id}...")
            return model.build_pipeline_with_lora(lora_list, sampler, new_pipeline)
        except Exception as e:
            print(traceback.format_exc())
            print(f"Failed to build pipeline for model {model_id}: {e}")
            return None

    def release_model(self, model_id):
        """
        Release resources and clear memory for a specific model.

        :param model_id: The model ID (the model name extracted from the config URL).
        """
        model = self.models.get(model_id)
        if not model:
            print(f"Model {model_id} not found.")
            return
        try:
            print(f"Releasing model {model_id}...")
            model.release(model.base_model_pipeline)
        except Exception as e:
            print(f"Failed to release model {model_id}: {e}")

    @spaces.GPU(duration=40)
    def generate_with_faceid(self, model_id, inference_params, progress=gr.Progress(track_tqdm=True)):
        # Clear GPU memory
        torch.cuda.empty_cache()
        model = self.models.get(model_id)
        if not model:
            raise Exception(f"invalid model_id {model_id}")
        if not model.ip_adapter_faceid_pipeline:
            raise Exception(f"model does not support ip adapter")
        ip_model = model.ip_adapter_faceid_pipeline
        cfg = model.cfg
        p = inference_params.get("prompt")
        negative_prompt = inference_params.get("negative_prompt", cfg.get("negative_prompt", ""))
        steps = inference_params.get("steps", cfg.get("inference_steps", 30))
        guidance_scale = inference_params.get("guidance_scale", cfg.get("guidance_scale", 7))
        width = inference_params.get("width", cfg.get("width", 512))
        height = inference_params.get("height", cfg.get("height", 512))
        images = inference_params.get("images", [])
        likeness_strength = inference_params.get("likeness_strength", 0.4)
        face_strength = inference_params.get("face_strength", 0.1)
        sampler = inference_params.get("sampler", cfg.get("sampler", ""))
        lora_list = inference_params.get("loras", [])
        seed = inference_params.get("seed", 0)

        if not images:
            raise Exception(f"face images not provided")
        start = time.time()
        ip_model.pipe.scheduler = samplers["Euler a"]
        ip_model.pipe.to("cuda")
        ip_model.image_proj_model.to("cuda")
        if not self.app:
            self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider'])#, 'CPUExecutionProvider'
            self.app.prepare(ctx_id=0, det_size=(512, 512))
        print("extracting face...")
        faceid_all_embeds = []
        for image in images:
            face = cv2.imread(image) ##here accepts image path
            faces = self.app.get(face)
            faceid_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
            faceid_all_embeds.append(faceid_embed)

        average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0)
        
        print("start inference...")
        style_selection = ""
        use_negative_prompt = True
        randomize_seed = True
        seed = seed or int(randomize_seed_fn(seed, randomize_seed))
        p = remove_child_related_content(p)
        prompt_str = cfg.get("prompt", "{prompt}").replace("{prompt}", p)
        #generator = torch.Generator(model.base_model_pipeline.device).manual_seed(seed)
        print(f"generate: p={p}, np={negative_prompt}, steps={steps}, guidance_scale={guidance_scale}, size={width},{height}, seed={seed}")
        print(f"device: embedding={average_embedding.device}, ip_model={ip_model.pipe.device}, pipe={model.base_model_pipeline.device}")
        images = ip_model.generate(
            prompt=prompt_str,
            negative_prompt=negative_prompt,
            faceid_embeds=average_embedding,
            scale=likeness_strength,
            width=width,
            height=height,
            guidance_scale=face_strength,
            num_inference_steps=steps,
            #generator=generator,
            num_images_per_prompt=1,
            #output_type="pil",
            #callback_on_step_end=callback_dynamic_cfg,
            #callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
        ).images
        cost = round(time.time() - start, 2)
        print(f"inference done in {cost}s")
        images = [save_image(img) for img in images]
        image_paths = [i[1] for i in images]
        print(prompt_str, image_paths)
        return [i[0] for i in images]

    @spaces.GPU(duration=40)
    def generate(self, model_id, inference_params, progress=gr.Progress(track_tqdm=True)):
        def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):
            cfg_disabling_at = cfg.get('cfg_disabling_rate', 0.75)
            if step_index == int(pipe.num_timesteps * cfg_disabling_at):
                callback_kwargs['prompt_embeds'] = callback_kwargs['prompt_embeds'].chunk(2)[-1]
                callback_kwargs['add_text_embeds'] = callback_kwargs['add_text_embeds'].chunk(2)[-1]
                callback_kwargs['add_time_ids'] = callback_kwargs['add_time_ids'].chunk(2)[-1]
                pipe._guidance_scale = 0.0

            return callback_kwargs
        model = self.models.get(model_id)
        if not model:
            raise Exception(f"invalid model_id {model_id}")
        
        cfg = model.cfg
        p = inference_params.get("prompt")
        negative_prompt = inference_params.get("negative_prompt", cfg.get("negative_prompt", ""))
        steps = inference_params.get("steps", cfg.get("inference_steps", 30))
        guidance_scale = inference_params.get("guidance_scale", cfg.get("guidance_scale", 7))
        width = inference_params.get("width", cfg.get("width", 512))
        height = inference_params.get("height", cfg.get("height", 512))
        sampler = inference_params.get("sampler", cfg.get("sampler", ""))
        lora_list = inference_params.get("loras", [])
        seed = inference_params.get("seed", 0)

        pipe = model.build_pipeline_with_lora(lora_list, sampler)

        start = time.time()
        pipe.to("cuda")
        print("start inference...")
        style_selection = ""
        use_negative_prompt = True
        randomize_seed = True
        seed = seed or int(randomize_seed_fn(seed, randomize_seed))
        guidance_scale = guidance_scale or cfg.get("guidance_scale", 7.5)
        p = remove_child_related_content(p)
        prompt_str = cfg.get("prompt", "{prompt}").replace("{prompt}", p)
        generator = torch.Generator(pipe.device).manual_seed(seed)
        print(f"generate: p={p}, np={negative_prompt}, steps={steps}, guidance_scale={guidance_scale}, size={width},{height}, seed={seed}")
        images = pipe(
            prompt=prompt_str,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            generator=generator,
            num_images_per_prompt=1,
            output_type="pil",
            #callback_on_step_end=callback_dynamic_cfg,
            #callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
        ).images
        cost = round(time.time() - start, 2)
        print(f"inference done in {cost}s")
        images = [save_image(img) for img in images]
        image_paths = [i[1] for i in images]
        print(prompt_str, image_paths)
        return [i[0] for i in images]
            
# Hugging Face file download function - returns only file path
def download_from_hf(filename, local_dir=None, repo_id=DATASET_ID, repo_type="dataset"):
    try:
        file_path = hf_hub_download(
            filename=filename,
            repo_id=DATASET_ID,
            repo_type="dataset",
            revision="main",
            local_dir=local_dir,
            local_files_only=False,  # Attempt to load from cache if available
        )
        return file_path  # Return file path only
    except Exception as e:
        print(f"Failed to load {filename} from Hugging Face: {str(e)}")
        return None


# Function to load and fuse LoRAs
def set_lora_weights(pipe, lorajson: list[dict], fuse=False):
    try:
        if not lorajson or not isinstance(lorajson, list):
            return

        a_list = []
        w_list = []
        for d in lorajson:
            if not d or not isinstance(d, dict) or not d["name"] or d["name"] == "None":
                continue

            k = d["name"]
            if not d.get("preloaded", False):
                start = time.time()
                weight_path = download_from_hf(d['filename'], local_dir=None)
                if weight_path:
                    pipe.load_lora_weights(weight_path, weight_name=d['filename'], adapter_name=k)

                load_time = round(time.time() - start, 2)
                print(f"LoRA {k} loaded in {load_time}s.")

            a_list.append(k)
            w_list.append(d["scale"])

        if not a_list:
            return

        start = time.time()
        pipe.set_adapters(a_list, adapter_weights=w_list)
        if fuse:
            pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
        fuse_time = round(time.time() - start, 2)
        print(f"LoRAs fused in {fuse_time}s.")
    except Exception as e:
        print(f"External LoRA Error: {e}")
        raise Exception(f"External LoRA Error: {e}") from e