import os
import gradio as gr
import PIL.Image
import numpy as np
import random
import torch
import subprocess
from diffusers import StableDiffusionPipeline

model_id = "dicoo_model"

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float)

def predict(prompt, steps=50, seed=42, guidance_scale=7.5):
    print("prompt:", prompt)
    image = pipe(prompt, num_inference_steps=steps, guidance_scale=7.5).images[0]
    return image

random_seed = random.randint(0, 2147483647)
gr.Interface(
    predict,
    inputs=[
        gr.inputs.Textbox(label='Prompt', default='a lovely <dicoo> in red dress and hat, in the snowy and brightly night, with many brightly buildings'),
        gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1),
        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
        gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1),
    ],
    outputs=gr.Image(shape=[512, 512], type="pil", elem_id="output_image"),
    css="#output_image{width: 256px}",
    title="Demo of dicoo-finetuned-diffusion-model using Intel Neural Compressor 🧨",
    description="This Spaces app is same as <a href=\"https://huggingface.co/spaces/Intel/dicoo_diffusion\">Intel/dicoo_diffusion</a>, created by Intel AIA/AIPC team with the model fine-tuned with one shot (one image) for a newly introduced object \"dicoo\". To replicate the model fine-tuning, please refer to the code sample in <a href=\"https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion\">Intel Neural Compressor</a>. You may also refer to our <a href=\"https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13\">blog</a> for more details.",
).launch()