import gradio as gr
import numpy as np
import random
from diffusers import SemanticStableDiffusionPipeline
import torch



device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "runwayml/stable-diffusion-v1-5"  # Replace to the model you would like to use

torch_dtype = torch.float32


pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32)
pipe = pipe.to(device)
out = pipe(
    prompt="a photo of the face of a woman",
    num_images_per_prompt=1,
    guidance_scale=7,
    editing_prompt=[
        "smiling, smile",  # Concepts to apply
        "glasses, wearing glasses",
        "curls, wavy hair, curly hair",
        "beard, full beard, mustache",
    ],
    reverse_editing_direction=[False, False, False, False],  # Direction of guidance i.e. increase all concepts
    edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
    edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
    edit_threshold=[
        0.99,
        0.975,
        0.925,
        0.96,
    ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
    edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
    edit_mom_beta=0.6,  # Momentum beta
    edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
)


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        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, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )

          

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result],
    )

if __name__ == "__main__":
    demo.launch()