import spaces
import gradio as gr
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel, FlowMatchEulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from PIL import Image
import numpy as np
import random


# Only initialize GPU after spaces import
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Constants
#BASE_MODEL = "black-forest-labs/FLUX.1-dev"
#LORA_MODEL = "MegaTronX/SuicideGirl-FLUX"  # Replace with your LoRA path 
MAX_SEED = np.iinfo(np.int32).max



pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("MegaTronX/SuicideGirl-FLUX", weight_name="SuicideGirls.safetensors")
pipe.fuse_lora(lora_scale=0.8)
pipe.to("cuda")


# Initialize model and scheduler
'''if torch.cuda.is_available():
    transformer = FluxTransformer2DModel.from_single_file(
        "https://huggingface.co/MegaTronX/SuicideGirl-FLUX/blob/main/SuicideGirls.safetensors",
        torch_dtype=torch.bfloat16
    )
    pipe = FluxPipeline.from_pretrained(
        BASE_MODEL,
        transformer=transformer,
        torch_dtype=torch.bfloat16
    )
    pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
        pipe.scheduler.config, use_beta_sigmas=True
    )
    pipe.to("cuda")
    
    # Load and apply LoRA weights
    pipe.load_lora_weights(LORA_MODEL)
'''


@spaces.GPU
def generate_image(
    prompt,
    width=768,
    height=1024,
    guidance_scale=3.5,
    num_inference_steps=24,
    seed=-1,
    num_images=1,
    progress=gr.Progress(track_tqdm=True)
):
    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    images = pipe(
        prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        output_type="pil",
        max_sequence_length=512,
        num_images_per_prompt=num_images,
    ).images
    
    return images, seed

# Gradio Interface
with gr.Blocks() as demo:
    gr.HTML("<h1><center>Flux LoRA Image Generator</center></h1>")
    
    with gr.Group():
        prompt = gr.Textbox(label='Enter Your Prompt', lines=3)
        generate_button = gr.Button("Generate", variant='primary')
        
        with gr.Row():
            image_output = gr.Gallery(label="Generated Images", columns=2, preview=True)
            seed_output = gr.Number(label="Seed Used")
        
        with gr.Accordion("Advanced Options", open=False):
            width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768)
            height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1024)
            guidance_scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, step=0.1, value=3.5)
            num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=24)
            seed = gr.Slider(label="Seed (-1 for random)", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
            num_images = gr.Slider(label="Number of Images", minimum=1, maximum=4, step=1, value=1)
    
    generate_button.click(
        fn=generate_image,
        inputs=[prompt, width, height, guidance_scale, num_inference_steps, seed, num_images],
        outputs=[image_output, seed_output]
    )

demo.launch()