import os
import json
import copy
import time
import random
import logging
import numpy as np
from typing import Any, Dict, List, Optional, Union

import torch
from PIL import Image
import gradio as gr
import requests
from PIL import Image
import io
import json
import spaces

from PIL import Image
import gradio as gr


from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
    AutoencoderKL,
    AutoPipelineForImage2Image,
    FluxPipeline,
    FlowMatchEulerDiscreteScheduler)

from huggingface_hub import (
    hf_hub_download,
    HfFileSystem,
    ModelCard,
    snapshot_download)

from diffusers.utils import load_image

# import spaces

import huggingface_hub
huggingface_hub.login(os.getenv('HF_TOKEN_FLUX3'))

def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.16,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu

def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    **kwargs,
):
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps

# FLUX pipeline
@torch.inference_mode()
def flux_pipe_call_that_returns_an_iterable_of_images(
    self,
    prompt: Union[str, List[str]] = None,
    prompt_2: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 28,
    timesteps: List[int] = None,
    guidance_scale: float = 3.5,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    latents: Optional[torch.FloatTensor] = None,
    prompt_embeds: Optional[torch.FloatTensor] = None,
    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    max_sequence_length: int = 512,
    good_vae: Optional[Any] = None,
):
    height = height or self.default_sample_size * self.vae_scale_factor
    width = width or self.default_sample_size * self.vae_scale_factor
    
    self.check_inputs(
        prompt,
        prompt_2,
        height,
        width,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    batch_size = 1 if isinstance(prompt, str) else len(prompt)
    device = self._execution_device

    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        device=device,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )
    
    num_channels_latents = self.transformer.config.in_channels // 4
    latents, latent_image_ids = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        device,
        generator,
        latents,
    )
    
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    image_seq_len = latents.shape[1]
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.base_image_seq_len,
        self.scheduler.config.max_image_seq_len,
        self.scheduler.config.base_shift,
        self.scheduler.config.max_shift,
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        device,
        timesteps,
        sigmas,
        mu=mu,
    )
    self._num_timesteps = len(timesteps)

    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None

    for i, t in enumerate(timesteps):
        if self.interrupt:
            continue

        timestep = t.expand(latents.shape[0]).to(latents.dtype)

        noise_pred = self.transformer(
            hidden_states=latents,
            timestep=timestep / 1000,
            guidance=guidance,
            pooled_projections=pooled_prompt_embeds,
            encoder_hidden_states=prompt_embeds,
            txt_ids=text_ids,
            img_ids=latent_image_ids,
            joint_attention_kwargs=self.joint_attention_kwargs,
            return_dict=False,
        )[0]

        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
        image = self.vae.decode(latents_for_image, return_dict=False)[0]
        yield self.image_processor.postprocess(image, output_type=output_type)[0]
        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
        torch.cuda.empty_cache()
        
    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
    image = good_vae.decode(latents, return_dict=False)[0]
    self.maybe_free_model_hooks()
    torch.cuda.empty_cache()
    yield self.image_processor.postprocess(image, output_type=output_type)[0]



dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)

MAX_SEED = 2**32-1
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

all_logo_id = ['cdf4262a-d7b7-4bb1-a9b7-2d5c90dedcd8', '6fedff19-fd9b-41a0-a386-861fe1b88a8b', 'bd4b35f9-d35c-4869-881d-2570ba595b3f', 
                '7ddfbbfd-358b-4d7e-a064-0bcd1ea6d2c3', 'b57dddcb-2a2f-4e8d-89be-fa654f0fbd0f', '66f5710f-5c83-46a0-a99b-5920da80bb18', 
                '0b5768a1-b228-4ab8-9104-1300ee640cb7', '6b67c58e-b246-49b7-8f7e-4d2842e4d195', 'cc6fbe5e-c776-4539-a746-1f2582a37121', 
                '0f61e070-27a0-4587-b746-ada72ade63f4', '41906123-587a-4bd4-93b8-f5674559e7e2', 'e8f709b3-ec91-4678-8c4d-76f463a1e310', 
                'be5d487f-ef89-44e2-befe-3ef224b93238', 'a7c3971f-d08e-40b1-9e7e-08cb40e86b19', '1155ab32-72a1-4f35-bed7-711daef0fdd3', 
                '78e73618-342c-417e-8099-c15e8070c330', 'a9f9ca3a-e191-4c6d-b386-228a23d3e75b', '8c29a489-0d85-4440-8bdf-131562ec187b', 
                'clzlxdjks001j4mjifva5os2o_original', '6f76fd57-8e33-46bd-a42e-969d33a2d553', 'cm0db116i04dpfp3rl68tbw60_original', 
                'fc4bba42-ad4e-45f6-8f42-ac52bd9d4243', 'cm0jp5w3f00qfva73bnf84bg8_original', 'clzm0hjk9001yfofvw2l8f0sx_original', 
                '26512603-b547-4de5-9caf-fae533f9dda2', 'b2c74a87-19f6-4a1f-a15d-c0b87892adf4', '1718ad5a-5551-4b8b-8e3d-62d44ada7a07', 'ee813a7f-3e8c-410a-8d91-b630e0201c68', 
                'd1d5b57a-ef0e-4e25-a474-e9a99c6ffea4', 
                '7eec73e4-3ead-48b3-96c6-4b5b14cf0726', 'ebd4acde-829e-4958-afb3-ca165bdd31b4', 'clzlyer7n00d1118e599gguei_original', 'a53af9e3-6f12-4619-accf-e970592be2b6', 
                '1b2a4a9d-11a1-4d20-b09f-be46d04b4ae7', '224d99de-b15d-4c0a-aec9-2d685266be9b', '15d80b95-0fd9-4652-b799-de40593cfffb', 'a1cce8b5-cf5c-44db-8b9d-52604987cb45', 
                '78d58ad6-4250-40b4-a16b-4ef471315c5a', '420549d8-11f3-4398-918e-02ff875f8e19', 'clzm4lhdk01dtfszpy2p9zx3p_original', 'fc3c9c3b-d881-42ff-8876-6e44d70d2190', 
                'c8401df5-c9fa-4ade-b048-3f9ab8ff4092', '92397a93-7b74-40d8-b257-375b09aa4145', 'e882624f-b398-4195-a8fc-32243b170230', '2078b6fe-2b7f-4d32-8f27-6a4e9667ab37', '261e4a43-b2a0-4a74-a95e-5488a0873138', 'fdec9bef-53aa-4b0c-9397-051bbcbfae78', '441932ba-eac3-4cc0-ad83-3fe95b990714', 'f814bb55-0de1-4515-8b76-f5c07209dad2', '627db3f8-246b-43b6-8d03-ff1ea94c93a9', '4904e9d8-66dc-4572-a3db-29ba978a3bd4', '2445da99-00d0-448f-8373-bb855e6f576e', '811a7987-ec1b-4e4f-8fe1-4509d5e6ef74', '203774f0-86a2-4b9d-ba23-b9dac4770937', 'c41961fa-924b-4bfe-85ab-0ee82b455232', 
                '9ef85a86-18ac-4997-85bd-77597e813365', '6e773cb1-3f86-47f0-be6e-f6d766363abe', 'fbb0230d-182b-4fdc-8706-fa4942b859a3', '28d14ef1-eefa-4bef-89d1-8587d38d85a9', '524f3dfa-7c7c-47b9-9215-089c2bc1c64a', 'a5c5f693-35dd-409c-acd8-47624e9aa1ee', 'c272e160-d0d1-42ca-aeea-28021c0af462', 'af79ae3a-ecd6-4d2a-9d91-be66f23f2b6f', '7bef70c9-9ca3-4f93-8b39-7402fa52de00', '7d12f9ea-22e5-43e6-8cd6-328686623d0a', 'bc2ce462-c16e-42f6-988d-b4f1c65c8ad2', '4f8e4b3a-5d35-4815-9c2b-275b3305ec0a', '2be53e73-0968-4b78-bba5-59ccb2049105', 'f5edae5e-6c10-497e-9afa-f63189829ff4', 'clzlx5nhl0116gffrhnh3o31q_original', 'dfac42a7-3849-413e-9e2b-b2a8b3c4b2ba', 'bc699a31-c65d-452d-9461-2665f9f35a5c', '7867dbeb-d3b0-40bc-ba1a-10cd72ed5c20', 'a27c528f-0c53-47e1-ae95-d01443c437af', '3a9bee83-89e8-4b21-acc5-6432c6c67f23', '473c73f4-f339-4ea5-be66-d6bdc7c8bc67', '217d072d-3228-4606-afc4-4747fca10ef2', '7ad65068-e390-447e-9dbc-7d6c1d8237e0', 'fb37c9fb-2b45-4813-8a67-652b5d7da5e9', '4475c787-8106-4e78-a4ec-c5d1d0203583', '1b8ad02b-3c06-41fe-a0d6-11fdc0ae7570', 'cm0oezpwp08otmc5oncwp0nfw_original', '321f7d70-74e2-4471-aacb-59dae9fc1c55', '3cd84324-f412-482a-a84e-a383568b1943', '3cc3ed6b-8d27-4520-a9d3-bb0aebfe95df', '52c7ab2f-f62d-4538-9687-42e1daf86e10', '5100ebb0-bc71-493e-acf0-025cedd07be8', '9352461e-c24c-4e5a-b5b6-e3f269fcac35', 'a1187d64-20b3-4f79-8146-8d6492216724', '66e3f5cc-785a-4fac-b61d-2f41e5763dcd', 'a8bc396f-e248-4535-98b4-075a9a501fe1', '3264fa0d-0474-45f3-a8b5-b5e0b1b46cf1', '2ae5d335-c956-4779-97ef-bc4c0ea36383', '2a476614-2311-4fcd-898e-61dc1ddecb78', 'd5cde960-7157-4ad4-89f3-f45226eb1f1c', '26999e76-57e7-4530-bffb-b5a116741f74', 'bf51ac9a-743b-49cb-ab80-d5b423172be3', 'eb3f820b-1643-4d77-b714-c7bca65989bf', 'clzltmwto0218sj8r90vs171o_original', 
                'a38fb2f2-6538-4d94-80a9-496821e6e590', '35e0976b-fab0-4a33-9684-b18292d8b57b', '87534b00-e40f-427e-b495-cf6fdefb7216', '165a95a4-30a1-4abe-8e6a-f2f700c8f79f', 'cd5e6334-3549-4bf4-87aa-0e10af3d2e5a', 'dfe0273c-734b-46b1-b27e-1467f1359526', 'aff4ce3d-223e-41f1-aff0-e09a3a176ecf', '830c6066-2496-4b69-8351-349c07f8e268', '77f06b36-f289-4886-ac44-d1abc26ae573', 'a109e302-3f03-47a7-998a-a1b7e5eac6fa', 'clzm592mo0213q6ceh56vv8ow_original', 'a2b66573-73e2-4f09-9aae-b472c96231f5', 'aa88edcc-df12-44fb-af68-b7595c8d90d4', 'e409516a-b4de-48a3-aacf-98deb42bc99b', '9e925589-5186-4190-a060-827dec89e608', 'ce3e300d-af12-4903-a3ce-52a44ea713e4', 'clzlv2fwe04lwnc1i7h3kgp8b_original', '437a1ca6-88fe-48d4-8595-3256f6c20683', 'd8d1e2ae-e724-4e41-9ba2-9804fe9f0f6a', 'c322b3b4-d0da-4d61-ba6b-e93df2f06894', '4b4187d4-bc35-4904-bcbc-9e2f573129b5', 'afa51015-e46c-4cbe-af0f-8c2ca64dca99', '03131d68-d71d-4f8d-9846-fe96d1fe75b3', '89322c1b-9fc7-49e5-864e-2572ca9d88bd', 'd729d8bd-1e43-40dd-886f-43bdad31bd50', 'c3c3ff41-1ef2-4f29-8643-911c596243ce', '3beff94a-8b37-4180-98c5-f0064d10269f', 'c97ec271-d98a-479c-bbd2-84837d2a967a', '3fd07ae8-ed69-4dd6-af4a-328d6d63581d', 'be2b87c1-9dc8-472c-8699-a679eb55932b', 'd1e870e0-d435-420a-9ec4-f3454f8c3fe4', 'ef33ed05-a2e3-4892-95f7-a17c2af5689a', '1c60eb28-4f81-4cb9-b5c6-8fa35db50741', '78e2099b-8ec2-4aa2-86d1-5986bcd37264', '80ac10a0-dce8-43a5-9635-a5c9bd723864', 'c309836b-94e4-49a5-b4a5-82c6b855d4f8', 'd122f413-7284-44a7-a666-cc89501ade01', '330dd28d-e7d8-4411-ad79-3a29a958ee0e', '1ff22588-76be-4165-aae0-5eab89e89e6e', 'cm07esp9o035l1ll3tba7izme_original', '6e861806-f8f6-4060-a08f-60da56430a57', '03d96c11-1631-414f-81df-3a637f1171e0', 'b9ee7476-c819-4c3a-9152-c4cc6ab5ae5c', '36df50f7-9457-4544-b998-74ad83b84e3e', 'd083c9fb-4f2b-40d5-9faf-ba8b682404da', 'd5b5491d-f5c2-41ad-9264-149c6b636650', '22c7cc2e-1f86-47e5-bf3e-6dc3aff7a561', '2e1feb02-f7db-4715-8279-9c227f419f01', 'fa5b1b62-165c-4feb-8498-b5e0dbc718e4', '73d15efb-9e02-48f1-8d0a-85f5940ad2fc', '05ae284b-6628-4c1e-a4fe-4309c35a5ce7', '96e2aa82-9309-4db2-8445-ab23083a4ae8', '34469ca2-e6f5-4f35-b330-09363f0091e8', '0cc957c2-e852-4d44-a5fc-2323b1059438', '3118a292-ae9c-4a82-a114-42ade8c4194b', 'ed5923b1-304d-4d3f-b623-9f39e129a234', '83774134-3808-4326-b7eb-38b48c87cd5c', 'b8c6e642-227e-4ffe-823b-0a455da6aedf', '2047f294-56d5-4151-b130-d8a186670c79', 'ce748fc7-e676-4492-b669-08eb9ae2b36b', '8616a056-0502-41e8-af71-a80efc23f2e2', '54e4fe56-ccdf-4f9b-8aed-9d1797d53af7', 'be34cd45-b262-48b2-955d-96a190fa5555', '9b17d258-672e-4199-90a9-44bdd2590e23', '8389f554-4871-4f44-b402-783d36ed42eb', '9b9f4d8b-166d-423d-819e-d9ffe90452e3', '32257b5a-adb9-42df-9b45-a3fb25ceefac', 'f5873d0e-4eed-4c6f-ab17-3c22d4fecde2', '7e1b7897-a243-4317-b64d-70d6b442c6fc', '4d3a1ddc-4d04-4a90-8cc9-6181e675f8af', '425bb1ff-4fdb-46a9-a55d-dd76ef4d9505', 'clzm8droy013j1pfirahpzclw_original', 'clzm2obn802chg1mucntn92q9_original', '05852f13-15e2-4274-97cc-36e4379b72dd', 'ffb911ce-1a92-4211-8801-cc8c20184e65', 
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logo_dataset_id = []
for logo_id in all_logo_id:
    dict = {
        "image": f"https://huggingface.co./ameerazam08/Dataset/resolve/main/image_data/{logo_id}.jpeg",
        
        "json": f"https://huggingface.co./ameerazam08/Dataset/resolve/main/json_data/{logo_id}.json",
            }
    logo_dataset_id.append(dict)


sticker_dataset_id = []
for logo_id in all_sticker_id:
    dict = {
        # https://huggingface.co./ameerazam08/Dataset/resolve/main/sticker_data/00dc8ec6-fbd7-4811-bac9-58b4de22ab2f.jpeg
        "image": f"https://huggingface.co./ameerazam08/Dataset/resolve/main/sticker_data/{logo_id}.jpeg",
        "json": f"https://huggingface.co./ameerazam08/Dataset/resolve/main/sticker_json/{logo_id}.json",
            }
    sticker_dataset_id.append(dict)




def load_hf_image_and_json(image_url, json_url):
    """Loads an image from a Hugging Face URL and retrieves the associated JSON data."""
    
 
    image_response = requests.get(image_url)



    if image_response.status_code == 200:
        image = Image.open(io.BytesIO(image_response.content))
    else:
        
        raise ValueError(f"Failed to load image: {image_url}")
    
    # Load JSON Data
    json_response = requests.get(json_url)
    if json_response.status_code == 200:
        json_data = json_response.json()
        
        # Handle case where JSON is a string (i.e., double-encoded JSON)
        if isinstance(json_data, str):  
            try:
                json_data = json.loads(json_data)  # Decode string JSON again
            except json.JSONDecodeError:
                raise ValueError("Invalid JSON format")
    else:
        raise ValueError(f"Failed to load JSON: {json_url}")
    
    return image, json_data  # Return JSON as a dictionary


class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection_logo(evt: gr.SelectData, width, height):
    selected_data = logo_dataset_id[evt.index]
    print("selected_data ",selected_data.keys())

    image, json_data = load_hf_image_and_json(selected_data['image'],selected_data['json'])

    prompt = json_data['prompt']
    new_placeholder = f"{prompt}"
 
    updated_text = f"### Selected: Copy and Replace TRIGGER with your input Text ::\n {prompt}"
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

def update_selection_sticker(evt: gr.SelectData, width, height):
    selected_data = sticker_dataset_id[evt.index]
    print("selected_data ",selected_data.keys())

    image, json_data = load_hf_image_and_json(selected_data['image'],selected_data['json'])

    prompt = json_data['prompt']
    new_placeholder = f"{prompt}"
 
    updated_text = f"### Selected: Copy and Replace TRIGGER with your input Text ::\n {prompt}"
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

@spaces.GPU(duration=100)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": lora_scale},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img





@spaces.GPU(duration=100)
def run_lora_logo(prompt, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a Logo Image before proceeding.🧨")
    
    image, json_data = load_hf_image_and_json(logo_dataset_id[selected_index]['image'],logo_dataset_id[selected_index]['json'])
    prompt_mash = prompt#json_data['prompt']

    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)


    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
    final_image = None
    step_counter = 0
    for image in image_generator:
        step_counter+=1
        final_image = image
        progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
        yield image, seed, gr.update(value=progress_bar, visible=True)
        

@spaces.GPU(duration=100)
def run_lora_sticker(prompt, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a Sticker Image before proceeding.🧨")
    
    image, json_data = load_hf_image_and_json(sticker_dataset_id[selected_index]['image'],sticker_dataset_id[selected_index]['json'])
    prompt_mash = "Cartoon illustration of Sticker "+prompt 

    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)


    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
    final_image = None
    step_counter = 0
    for image in image_generator:
        step_counter+=1
        final_image = image
        progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
        yield image, seed, gr.update(value=progress_bar, visible=True)
        

run_lora_logo.zerogpu = True
run_lora_sticker.zerogpu = True



with gr.Blocks(theme="gstaff/sketch") as app:
    title = gr.HTML(
        """<h1>FLUX LOGO/Sticker Generator </h1>""",
        elem_id="title",
    )
    with gr.Tabs():
        with gr.TabItem("LOGO"):
            # State and Input for LOGO tab
            selected_index_logo = gr.State(None)
            with gr.Row():
                with gr.Column(scale=4):
                    prompt_logo = gr.Textbox(
                        label="Prompt", 
                        lines=5, 
                        placeholder=":/ choose the Logo and type the prompt "
                    )
                with gr.Column(scale=1, elem_id="gen_column"):
                    generate_button_logo = gr.Button(
                        "Generate", 
                        variant="primary", 
                        elem_id="gen_btn"
                    )
            with gr.Row():
                with gr.Column():
                    selected_info_logo = gr.Markdown("")
                    gallery_logo = gr.Gallery(
                        [(item["image"], str(idx)) for idx, item in enumerate(logo_dataset_id)],
                        label="LOGO Gallery",
                        allow_preview=False,
                        columns=4,
                        rows=10,
                        elem_id="gallery_logo",
                        show_share_button=False,
                    )
                with gr.Column():
                    progress_bar_logo = gr.Markdown(elem_id="progress_logo", visible=False)
                    result_logo = gr.Image(label="Generated Image")
            with gr.Row():
                with gr.Accordion("Advanced Settings", open=False):
                    with gr.Row():
                        image_strength_logo = gr.Slider(
                            label="Denoise Strength", 
                            info="Lower means more image influence", 
                            minimum=0.1, 
                            maximum=1.0, 
                            step=0.01, 
                            value=0.75
                        )
                    with gr.Column():
                        with gr.Row():
                            cfg_scale_logo = gr.Slider(
                                label="CFG Scale", 
                                minimum=1, 
                                maximum=20, 
                                step=0.5, 
                                value=3.5
                            )
                            steps_logo = gr.Slider(
                                label="Steps", 
                                minimum=1, 
                                maximum=50, 
                                step=1, 
                                value=28
                            )
                        with gr.Row():
                            width_logo = gr.Slider(
                                label="Width", 
                                minimum=256, 
                                maximum=1536, 
                                step=64, 
                                value=1024
                            )
                            height_logo = gr.Slider(
                                label="Height", 
                                minimum=256, 
                                maximum=1536, 
                                step=64, 
                                value=1024
                            )
                        with gr.Row():
                            randomize_seed_logo = gr.Checkbox(True, label="Randomize seed")
                            seed_logo = gr.Slider(
                                label="Seed", 
                                minimum=0, 
                                maximum=MAX_SEED, 
                                step=1, 
                                value=0, 
                                randomize=True
                            )
            # Set up interactions for LOGO tab
            gallery_logo.select(
                update_selection_logo,
                inputs=[width_logo, height_logo],
                outputs=[prompt_logo, selected_info_logo, selected_index_logo, width_logo, height_logo]
            )
            gr.on(
                triggers=[generate_button_logo.click, prompt_logo.submit],
                fn=run_lora_logo,
                inputs=[
                    prompt_logo, image_strength_logo, cfg_scale_logo, steps_logo,
                    selected_index_logo, randomize_seed_logo, seed_logo,
                    width_logo, height_logo
                ],
                outputs=[result_logo, seed_logo, progress_bar_logo]
            )
        
        with gr.TabItem("Sticker"):
            # State and Input for Sticker tab
            selected_index_sticker = gr.State(None)
            with gr.Row():
                with gr.Column(scale=4):
                    prompt_sticker = gr.Textbox(
                        label="Prompt", 
                        lines=5, 
                        placeholder=":/ choose the Sticker and type the prompt "
                    )
                with gr.Column(scale=1, elem_id="gen_column"):
                    generate_button_sticker = gr.Button(
                        "Generate", 
                        variant="primary", 
                        elem_id="gen_btn"
                    )
            with gr.Row():
                with gr.Column():
                    selected_info_sticker = gr.Markdown("")
                    gallery_sticker = gr.Gallery(
                        [(item["image"], str(idx)) for idx, item in enumerate(sticker_dataset_id)],
                        label="Sticker Gallery",
                        allow_preview=False,
                        columns=4,
                        rows=10,
                        elem_id="gallery_sticker",
                        show_share_button=False,
                    )
                with gr.Column():
                    progress_bar_sticker = gr.Markdown(elem_id="progress_sticker", visible=False)
                    result_sticker = gr.Image(label="Generated Image")
            with gr.Row():
                with gr.Accordion("Advanced Settings", open=False):
                    with gr.Row():
                        image_strength_sticker = gr.Slider(
                            label="Denoise Strength", 
                            info="Lower means more image influence", 
                            minimum=0.1, 
                            maximum=1.0, 
                            step=0.01, 
                            value=0.75
                        )
                    with gr.Column():
                        with gr.Row():
                            cfg_scale_sticker = gr.Slider(
                                label="CFG Scale", 
                                minimum=1, 
                                maximum=20, 
                                step=0.5, 
                                value=3.5
                            )
                            steps_sticker = gr.Slider(
                                label="Steps", 
                                minimum=1, 
                                maximum=50, 
                                step=1, 
                                value=28
                            )
                        with gr.Row():
                            width_sticker = gr.Slider(
                                label="Width", 
                                minimum=256, 
                                maximum=1536, 
                                step=64, 
                                value=1024
                            )
                            height_sticker = gr.Slider(
                                label="Height", 
                                minimum=256, 
                                maximum=1536, 
                                step=64, 
                                value=1024
                            )
                        with gr.Row():
                            randomize_seed_sticker = gr.Checkbox(True, label="Randomize seed")
                            seed_sticker = gr.Slider(
                                label="Seed", 
                                minimum=0, 
                                maximum=MAX_SEED, 
                                step=1, 
                                value=0, 
                                randomize=True
                            )
            # Set up interactions for Sticker tab
            gallery_sticker.select(
                update_selection_sticker,
                inputs=[width_sticker, height_sticker],
                outputs=[prompt_sticker, selected_info_sticker, selected_index_sticker, width_sticker, height_sticker]
            )
            gr.on(
                triggers=[generate_button_sticker.click, prompt_sticker.submit],
                fn=run_lora_sticker,
                inputs=[
                    prompt_sticker, image_strength_sticker, cfg_scale_sticker, steps_sticker,
                    selected_index_sticker, randomize_seed_sticker, seed_sticker,
                    width_sticker, height_sticker
                ],
                outputs=[result_sticker, seed_sticker, progress_bar_sticker]
            )

app.queue()
app.launch(share=True)