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Fai-Fuzer-v0.3
File size: 7,865 Bytes 
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import os
import math
import gradio as gr
import numpy as np
import requests
import json
import base64
from PIL import Image
from io import BytesIO
import runpod
from enum import Enum


api_key = os.getenv("FAI_API_KEY")
api = os.getenv("FAI_API")


def image_to_base64(image):
    # Open the image file
    with image:
        # Create a buffer to hold the binary data
        buffered = BytesIO()
        # Save the image in its original format to the buffer
        #print(image.format)
        image.save(buffered, format="PNG")
        # Get the byte data from the buffer
        binary_image_data = buffered.getvalue()
        # Encode the binary data to a base64 string
        base64_image = base64.b64encode(binary_image_data).decode("utf-8")
        return base64_image
    
     
def process(data, api, api_key):
    
    runpod.api_key = api_key
    input_payload = {"input": data }

    try:
        endpoint = runpod.Endpoint(api)
        run_request = endpoint.run(input_payload)

        # Initial check without blocking, useful for quick tasks
        status = run_request.status()
        print(f"Initial job status: {status}")

        if status != "COMPLETED":
            # Polling with timeout for long-running tasks
            output = run_request.output(timeout=60)
        else:
            output = run_request.output()
        print(f"Job output: {output}")
    except Exception as e:
        print(f"An error occurred: {e}")
        

    image_data = output['image']
    # Decode the Base64 string
    image_bytes = base64.b64decode(image_data)
    # Convert binary data to image
    image = Image.open(BytesIO(image_bytes))
    
    return image

def process_generate(fore, prompt, image_width, image_height, intensity, mode, refprompt):    
    
    print(f"MODE: {mode}, INTENSITY: {intensity}, WIDTH: {image_width}, HEIGHT: {image_height}")
    

    forestr = image_to_base64(fore.convert("RGBA"))
    data = {
        "foreground_image64": forestr,
        "prompt" : prompt,
        "mode" : mode,
        "intensity" : float(intensity),
        "width" : int(image_width),
        "height" : int(image_height),
        "refprompt" : refprompt
    }
    
    image = process(data, api, api_key)

    return image


class Stage(Enum):
    FIRST_STAGE = "first-stage"
    SECOND_STAGE = "refiner"
    FULL = "full"

css="""#disp_image {
    text-align: center; /* Horizontally center the content */
}
#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
#share-btn-container:hover {background-color: #060606}
#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
#share-btn * {all: unset}
#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
#share-btn-container .wrap {display: none !important}
#share-btn-container.hidden {display: none!important}
#duplicate-button {
    margin-left: auto;
    color: #fff;
    background: #1565c0;
  }
        """
block = gr.Blocks(css=css, title="## F.ai Lanthos").queue()
with block:
    gr.HTML("""
            <center><h1 style="color:#000">Fotographer AI Lanthos</h1></center>""")
    
    gr.HTML('''
        <div>
        <a style="display:inline-block; margin-left: .5em" href="https://app.fotographer.ai/home"><img src="https://img.shields.io/badge/2310.15110-f9f7f7?logo=data:image/png;base64,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"></a>
        <a style="display:inline-block; margin-left: .5em" href='https://app.fotographer.ai/home'><img src='https://img.shields.io/github/stars/SUDO-AI-3D/zero123plus?style=social' /></a>
        Check out our App<a href="https://app.fotographer.ai/home">Fotographer.ai</a>! 
        </div>
        ''')

    with gr.Row():
        gr.Markdown("### F.ai Lanthos: Real Composite Photography in 2 minutes!")
    with gr.Row():
        fore = gr.Image(source='upload', type="pil", label="Foreground Image", height=400)
        with gr.Column():
            
            result_gallery = gr.Image(label='Output')  #gr.Gallery(height=400, object_fit='contain', label='Outputs')
    with gr.Row():
        prompt = gr.Textbox(label="Prompt")
        with gr.Column():
            refprompt = gr.Textbox(label="Refiner Prompt")
    with gr.Row():
        mode = gr.Radio(choices=[e.value for e in Stage],
                            value=Stage.FULL.value,
                            label="Generation Mode", type='value')
        with gr.Column():
            image_width = gr.Slider(label="Image Width", minimum=256, maximum=1500, value=1024, step=64)
            image_height = gr.Slider(label="Image Height", minimum=256, maximum=1500, value=1024, step=64)
            
    with gr.Row():
        intensity = gr.Slider(label="Refiner Strength", minimum=1, maximum=7, value=3, step=0.5)
        generate_button = gr.Button(value="Generate")
        



    ips = [fore, prompt, image_width, image_height, intensity, mode, refprompt]
    generate_button.click(fn=process_generate, inputs=ips, outputs=[result_gallery])


block.launch()