import gradio as gr
import numpy as np
import spaces
import torch
import random
from peft import PeftModel
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from image_gen_aux import DepthPreprocessor

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# Initialize models without moving to CUDA yet
pipe = FluxControlPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Depth-dev", 
    torch_dtype=torch.bfloat16
)
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")

@spaces.GPU
def load_lora(lora_path):
    if not lora_path.strip():
        return "Please provide a valid LoRA path"
    try:
        # Move to GPU within the wrapped function
        pipe.to("cuda")
        
        # Unload any existing LoRA weights first
        try:
            pipe.unload_lora_weights()
        except:
            pass
            
        # Load new LoRA weights
        pipe.load_lora_weights(lora_path)
        return f"Successfully loaded LoRA weights from {lora_path}"
    except Exception as e:
        return f"Error loading LoRA weights: {str(e)}"

@spaces.GPU
def unload_lora():
    try:
        pipe.to("cuda")
        pipe.unload_lora_weights()
        return "Successfully unloaded LoRA weights"
    except Exception as e:
        return f"Error unloading LoRA weights: {str(e)}"

@spaces.GPU
def infer(control_image, prompt, seed=42, randomize_seed=False, width=1024, height=1024, 
          guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    try:
        # Move pipeline to GPU within the wrapped function
        pipe.to("cuda")
        
        # Process control image
        control_image = processor(control_image)[0].convert("RGB")
        
        # Generate image
        image = pipe(
            prompt=prompt,
            control_image=control_image,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=torch.Generator("cuda").manual_seed(seed),
        ).images[0]
        
        return image, seed
    except Exception as e:
        return None, f"Error during inference: {str(e)}"

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 Depth [dev] with LoRA Support
12B param rectified flow transformer structural conditioning tuned, guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)

        # LoRA controls
        with gr.Row():
            lora_path = gr.Textbox(
                label="HuggingFace LoRA Path",
                placeholder="e.g., Borcherding/FLUX.1-dev-LoRA-AutumnSpringTrees"
            )
            load_lora_btn = gr.Button("Load LoRA")
            unload_lora_btn = gr.Button("Unload LoRA")
        
        lora_status = gr.Textbox(label="LoRA Status", interactive=False)

        control_image = gr.Image(label="Upload the image for control", type="pil")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)
        error_message = gr.Textbox(label="Error", visible=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=30,
                    step=0.5,
                    value=10,
                )
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

    # Event handlers
    load_lora_btn.click(
        fn=load_lora,
        inputs=[lora_path],
        outputs=[lora_status]
    )
    
    unload_lora_btn.click(
        fn=unload_lora,
        inputs=[],
        outputs=[lora_status]
    )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[control_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

demo.launch()