import spaces
import time
import os
import gradio as gr
import torch
from einops import rearrange
from PIL import Image
from transformers import pipeline
from flux.cli import SamplingOptions
from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from flux.util import load_ae, load_clip, load_flow_model, load_t5
from pulid.pipeline_flux import PuLIDPipeline
from pulid.utils import resize_numpy_image_long

NSFW_THRESHOLD = 0.85

def get_models(name: str, device: torch.device, offload: bool):
    t5 = load_t5(device, max_length=128)
    clip = load_clip(device)
    model = load_flow_model(name, device="cpu" if offload else device)
    model.eval()
    ae = load_ae(name, device="cpu" if offload else device)
    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
    return model, ae, t5, clip, nsfw_classifier

class FluxGenerator:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.offload = True  # Enable offloading for free tier
        self.model_name = "flux-schnell"  # Use flux-schnell
        self.model, self.ae, self.t5, self.clip, self.nsfw_classifier = get_models(
            self.model_name,
            device=self.device,
            offload=self.offload,
        )
        self.pulid_model = PuLIDPipeline(self.model, "cuda", weight_dtype=torch.bfloat16)
        self.pulid_model.load_pretrain()

flux_generator = FluxGenerator()

@spaces.GPU
@torch.inference_mode()
def generate_image(
    prompt,
    id_image,
    seed,
    width=512,  # Reduced for free tier
    height=512,  # Reduced for free tier
    num_steps=4,  # Optimized for schnell
    id_weight=1.0,
):
    flux_generator.t5.max_length = 128
    seed = int(seed) if seed != -1 else torch.Generator(device="cpu").seed()

    opts = SamplingOptions(
        prompt=prompt,
        width=width,
        height=height,
        num_steps=num_steps,
        guidance=0.0,  # No guidance for schnell
        seed=seed,
    )

    print(f"Generating '{opts.prompt}' with seed {opts.seed}")
    t0 = time.perf_counter()

    # Process ID image if provided
    if id_image is not None:
        id_image = resize_numpy_image_long(id_image, 512)  # Smaller size for memory
        id_embeddings, uncond_id_embeddings = flux_generator.pulid_model.get_id_embedding(id_image, cal_uncond=False)
    else:
        id_embeddings = None
        uncond_id_embeddings = None

    # Prepare noise and schedule
    x = get_noise(
        1,
        opts.height,
        opts.width,
        device=flux_generator.device,
        dtype=torch.bfloat16,
        seed=opts.seed,
    )
    timesteps = get_schedule(
        opts.num_steps,
        x.shape[-1] * x.shape[-2] // 4,
        shift=True,
    )

    if flux_generator.offload:
        flux_generator.t5, flux_generator.clip = flux_generator.t5.to(flux_generator.device), flux_generator.clip.to(flux_generator.device)
    inp = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=opts.prompt)

    if flux_generator.offload:
        flux_generator.t5, flux_generator.clip = flux_generator.t5.cpu(), flux_generator.clip.cpu()
        torch.cuda.empty_cache()
        flux_generator.model = flux_generator.model.to(flux_generator.device)

    # Denoise
    x = denoise(
        flux_generator.model,
        **inp,
        timesteps=timesteps,
        guidance=opts.guidance,
        id=id_embeddings,
        id_weight=id_weight,
        start_step=0,
        uncond_id=uncond_id_embeddings,
        true_cfg=1.0,  # No true CFG for schnell
    )

    if flux_generator.offload:
        flux_generator.model.cpu()
        torch.cuda.empty_cache()
        flux_generator.ae.decoder.to(x.device)

    # Decode
    x = unpack(x.float(), opts.height, opts.width)
    with torch.autocast(device_type=flux_generator.device.type, dtype=torch.bfloat16):
        x = flux_generator.ae.decode(x)

    if flux_generator.offload:
        flux_generator.ae.decoder.cpu()
        torch.cuda.empty_cache()

    t1 = time.perf_counter()
    print(f"Done in {t1 - t0:.1f}s.")

    # Convert to PIL
    x = x.clamp(-1, 1)
    x = rearrange(x[0], "c h w -> h w c")
    img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())

    # NSFW check
    nsfw_score = [x["score"] for x in flux_generator.nsfw_classifier(img) if x["label"] == "nsfw"][0]
    if nsfw_score < NSFW_THRESHOLD:
        return img, str(opts.seed)
    else:
        return None, f"Image may contain NSFW content (score: {nsfw_score})"

def create_demo():
    with gr.Blocks() as demo:
        gr.Markdown("# PuLID with FLUX.1 Schnell Demo")
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", value="A person in a futuristic city")
                id_image = gr.Image(label="Reference Image (ID)")
                seed = gr.Textbox(label="Seed (-1 for random)", value="-1")
                width = gr.Slider(256, 1024, 512, step=16, label="Width")
                height = gr.Slider(256, 1024, 512, step=16, label="Height")
                num_steps = gr.Slider(1, 4, 4, step=1, label="Number of Steps")
                id_weight = gr.Slider(0.0, 2.0, 1.0, step=0.05, label="ID Weight")
                generate_btn = gr.Button("Generate")
            with gr.Column():
                output_image = gr.Image(label="Generated Image")
                seed_output = gr.Textbox(label="Used Seed")
        
        generate_btn.click(
            fn=generate_image,
            inputs=[prompt, id_image, seed, width, height, num_steps, id_weight],
            outputs=[output_image, seed_output]
        )
    return demo

if __name__ == "__main__":
    import huggingface_hub
    huggingface_hub.login(os.getenv("HF_TOKEN"))
    demo = create_demo()
    demo.launch()