import gradio as gr
import requests
import random
import os
from io import BytesIO
from PIL import Image
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1344


class APIClient:
    def __init__(self, api_key=os.getenv("API_KEY"), base_url="inference.prodia.com"):
        self.headers = {
            "Content-Type": "application/json",
            "Accept": "image/jpeg",
            "Authorization": f"Bearer {api_key}"
        }
        self.base_url = f"https://{base_url}"

    def _post(self, url, json=None):
        r = requests.post(url, headers=self.headers, json=json)
        r.raise_for_status()

        return Image.open(BytesIO(r.content)).convert("RGBA")

    def job(self, config):
        body = {"type": "v2.job.sd3.txt2img", "config": config}
        return self._post(f"{self.base_url}/v2/job", json=body)


def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    image = generative_api.job(
        {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "width": width,
            "height": height,
            "guidance_scale": guidance_scale,
            "steps": num_inference_steps,
            # "refiner": True
            "seed": seed
        }
    )

    return image, seed


generative_api = APIClient()

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: 580px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
            # Demo [Stable Diffusion 3 - Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
            Learn more about [Stable Diffusion 3](https://stability.ai/news/stable-diffusion-3). Powered by [Prodia API](https://prodia.com).
            """)

        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)

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

            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=64,
                    value=1024,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=5.0,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

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

demo.launch()