import gradio as gr
import modin.pandas as pd
import torch
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import math

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo")
pipe = pipe.to(device)

def resize(value,img):
    img = Image.open(img)
    img = img.resize((value,value))
    return img

def infer(source_img, prompt, steps, seed, Strength):
    generator = torch.Generator(device).manual_seed(seed)  
    if int(steps * Strength) < 1:
        steps = math.ceil(1 / max(0.10, Strength))
    source_image = resize(512, source_img)
    source_image.save('source.png')
    image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
    return image

demo = gr.Interface(
    fn=infer,
    inputs=[
        gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."),
        gr.Textbox(label='Prompt Input Text.'),
        gr.Slider(1, 5, value=2, step=1, label='Number of Iterations'),
        gr.Slider(label="Seed", minimum=0, maximum=67, step=1, randomize=True),
        gr.Slider(label='Strength', minimum=0.1, maximum=1, step=.05, value=.5)
    ],
    outputs='image',
    title="Generative Images",
    description="Upload an Image, Use your Cam, or Paste an Image. Then enter a Prompt, then click submit.",
    article="<a href=\"https://Agent5.com\">Agent 5</a>",
    css="footer {visibility: hidden}"
)

demo.queue(max_size=10).launch()