import numpy as np
import gradio as gr
import requests
import time
import json
import base64
import os
from PIL import Image
from io import BytesIO

class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }

    def generate(self, params):
        response = self._post(f"{self.base}/sdxl/generate", params)
        return response.json()

    def get_job(self, job_id):
        response = self._get(f"{self.base}/job/{job_id}")
        return response.json()

    def wait(self, job):
        job_result = job

        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(0.25)
            job_result = self.get_job(job['job'])

        return job_result

    def list_models(self):
        response = self._get(f"{self.base}/sdxl/models")
        return response.json()

    def list_samplers(self):
        response = self._get(f"{self.base}/sdxl/samplers")
        return response.json()

    def _post(self, url, params):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response


def image_to_base64(image_path):
    # Open the image with PIL
    with Image.open(image_path) as image:
        # Convert the image to bytes
        buffered = BytesIO()
        image.save(buffered, format="PNG")  # You can change format to PNG if needed

        # Encode the bytes to base64
        img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string


prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))

def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    result = prodia_client.generate({
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })

    job = prodia_client.wait(result)

    return job["imageUrl"]

css = """
/* Overall Styling */
body {
    font-family: 'Arial', sans-serif;
}

.container {
    display: flex;
    flex-direction: column;
    gap: 20px;
}

/* Image Output Area */
#image-output-container {
    border: 2px solid #ccc;
    border-radius: 8px;
    overflow: hidden; 
}

#image-output {
    max-width: 100%;
    height: auto;
}

/* Settings Section */
#settings {
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); 
    gap: 20px;
}

.setting-group {
    border: 1px solid #ccc;
    padding: 20px;
    border-radius: 8px;
}

/* Button Styling */
#generate {
    background-color: #007bff; /* Example - use your preferred color */
    color: white;
    padding: 15px 25px;
    border: none;
    border-radius: 5px;
    cursor: pointer;
}

#generate:hover {
    background-color: #0056b3; /* Darker shade on hover */
}

/* Responsive Design - Adjust breakpoints as needed */
@media screen and (max-width: 768px) {
    #settings {
        grid-template-columns: 1fr; 
    }
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        gr.Markdown("<h1 style='text-align: center;'>Create Your Disney Pixar AI Poster</h1>", elem_id="title")

    with gr.Row(elem_id="image-output-container"):
        image_output = gr.Image(
            value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png",
            label="Generated Image",
            elem_id="image-output"
        )

    with gr.Row(elem_id="settings"):
        with gr.Column(scale=1, min_width=300, elem_classes="setting-group"):
            prompt = gr.Textbox(
                "space warrior, beautiful, female, ultrarealistic, soft lighting, 8k",
                placeholder="Enter your prompt here...", 
                show_label=False, 
                lines=3, 
                elem_id="prompt-input"
            )
            negative_prompt = gr.Textbox(
                placeholder="Enter negative prompts (optional)...", 
                show_label=False, 
                lines=3, 
                value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly"
            )
            text_button = gr.Button("Generate", variant='primary', elem_id="generate") 

        with gr.Column(scale=1, min_width=300, elem_classes="setting-group"):
            model = gr.Dropdown(
                interactive=True,
                value="sd_xl_base_1.0.safetensors [be9edd61]", 
                show_label=True, 
                label="Model", 
                choices=prodia_client.list_models()
            )
            sampler = gr.Dropdown(
                value="DPM++ 2M Karras", 
                show_label=True, 
                label="Sampling Method", 
                choices=prodia_client.list_samplers()
            )
            steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)

        with gr.Column(scale=1, min_width=300, elem_classes="setting-group"):
            width = gr.Slider(label="Width", minimum=512, maximum=1536, value=1024, step=8)
            height = gr.Slider(label="Height", minimum=512, maximum=1536, value=1024, step=8)
            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
            seed = gr.Number(label="Seed", value=-1)

    text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)

demo.queue(concurrency_count=24, max_size=32, api_open=False).launch(max_threads=128)