app.py

import numpy as np
import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import PIL
from PIL.ExifTags import TAGS
import html


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}/sd/generate", params)
        return response.json()
    
    def transform(self, params):
        response = self._post(f"{self.base}/sd/transform", params)
        return response.json()
    
    def controlnet(self, params):
        response = self._post(f"{self.base}/sd/controlnet", 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}/models/list")
        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 = """
#generate {
    height: 100%;
}
"""

with gr.Blocks(css=css) as demo:


    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
  
        with gr.Column(scale=1):
            gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).")

    with gr.Tab("txt2img"):
        with gr.Row():
            with gr.Column(scale=6, min_width=600):
                prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
            with gr.Column():
                text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Tab("Generation"):
                    with gr.Row():
                        with gr.Column(scale=1):
                            sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=[
                                "Euler",
                                "Euler a",
                                "LMS",
                                "Heun",
                                "DPM2",
                                "DPM2 a",
                                "DPM++ 2S a",
                                "DPM++ 2M",
                                "DPM++ SDE",
                                "DPM fast",
                                "DPM adaptive",
                                "LMS Karras",
                                "DPM2 Karras",
                                "DPM2 a Karras",
                                "DPM++ 2S a Karras",
                                "DPM++ 2M Karras",
                                "DPM++ SDE Karras",
                                "DDIM",
                                "PLMS",
                            ])
                            
                        with gr.Column(scale=1):
                            steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)

                    with gr.Row():
                        with gr.Column(scale=1):
                            width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                            height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
                        
                        with gr.Column(scale=1):
                            batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                            batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)

                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                    seed = gr.Number(label="Seed", value=-1)

                
            with gr.Column(scale=2):
                image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")

        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
    
    with gr.Tab("PNG Info"):
        def plaintext_to_html(text, classname=None):
            content = "<br>\n".join(html.escape(x) for x in text.split('\n'))

            return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"


        def get_exif_data(image):
            items = image.info

            info = ''
            for key, text in items.items():
                info += f"""
                <div>
                <p><b>{plaintext_to_html(str(key))}</b></p>
                <p>{plaintext_to_html(str(text))}</p>
                </div>
                """.strip()+"\n"

            if len(info) == 0:
                message = "Nothing found in the image."
                info = f"<div><p>{message}<p></div>"

            return info

        with gr.Row():
            with gr.Column():
                image_input = gr.Image(type="pil")
                
            with gr.Column():
                exif_output = gr.HTML(label="EXIF Data")

        image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)


demo.queue(concurrency_count=24)
demo.launch()