import gradio as gr
from PIL import Image
from gradio_imageslider import ImageSlider

import requests
import base64
import numpy as np
import random
import io

URL = "http://localhost:5000/predictions"
HEADERS = {
    "Content-Type": "application/json",
}

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


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def generate(
    input_image: Image,
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    prior_num_inference_steps: int = 30,
    # prior_timesteps: List[float] = None,
    prior_guidance_scale: float = 4.0,
    decoder_num_inference_steps: int = 12,
    # decoder_timesteps: List[float] = None,
    decoder_guidance_scale: float = 0.0,
    num_images_per_prompt: int = 2,

) -> Image:
    payload = {
        "input": {
            "hdr": 0,
            "image": "http://localhost:7860/file=" + input_image,
            "steps": 20,
            "prompt": prompt,
            "scheduler": "DDIM",
            "creativity": 0.25,
            "guess_mode": False,
            "resolution": "original",
            "resemblance": 0.75,
            "guidance_scale": 7,
            "negative_prompt": "teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"
        }
    }
    response = requests.post(URL, headers=HEADERS, json=payload)
    json_response = response.json()
    if 'output' in json_response:
        base64_image = json_response["output"][0]
        image_data = base64.b64decode(
            base64_image.replace("data:image/png;base64,", ""))
        image_stream = io.BytesIO(image_data)
        return [Image.open(input_image), Image.open(image_stream)]
    raise gr.Error(json_response["status"])


examples = [
    ["An astronaut riding a green horse", "examples/image2.png"],
    ["A mecha robot in a favela by Tarsila do Amaral", "examples/image2.png"],
    ["The sprirt of a Tamagotchi wandering in the city of Los Angeles",
        "examples/image1.png"],
    ["A delicious feijoada ramen dish", "examples/image0.png"],
]

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="filepath")
            with gr.Group():
                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)
        with gr.Column():
            result = ImageSlider(label="Result", type="pil")
    with gr.Accordion("Advanced options", 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=1024,
                maximum=1024,
                step=512,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=1024,
                maximum=1024,
                step=512,
                value=1024,
            )
            num_images_per_prompt = gr.Slider(
                label="Number of Images",
                minimum=1,
                maximum=2,
                step=1,
                value=1,
            )
        with gr.Row():
            prior_guidance_scale = gr.Slider(
                label="Prior Guidance Scale",
                minimum=0,
                maximum=20,
                step=0.1,
                value=4.0,
            )
            prior_num_inference_steps = gr.Slider(
                label="Prior Inference Steps",
                minimum=10,
                maximum=30,
                step=1,
                value=20,
            )

            decoder_guidance_scale = gr.Slider(
                label="Decoder Guidance Scale",
                minimum=0,
                maximum=0,
                step=0.1,
                value=0.0,
            )
            decoder_num_inference_steps = gr.Slider(
                label="Decoder Inference Steps",
                minimum=4,
                maximum=12,
                step=1,
                value=10,
            )

    gr.Examples(
        examples=examples,
        inputs=[prompt, input_image],
        outputs=result,
        fn=generate,
        cache_examples=True,
    )

    inputs = [
        input_image,
        prompt,
        negative_prompt,
        seed,
        width,
        height,
        prior_num_inference_steps,
        # prior_timesteps,
        prior_guidance_scale,
        decoder_num_inference_steps,
        # decoder_timesteps,
        decoder_guidance_scale,
        num_images_per_prompt,
    ]
    gr.on(
        triggers=[prompt.submit, negative_prompt.submit, run_button.click],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )


if __name__ == "__main__":
    demo.queue(max_size=20).launch()