app.py

from diffusers import DiffusionPipeline
import spaces
# import torch
import PIL.Image
import gradio as gr
import gradio.components as grc
import numpy as np
from huggingface_hub import from_pretrained_keras
import keras
import time
import os 



# options = ['Placeholder A', 'Placeholder B', 'Placeholder C']


# pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
# device = "cuda" if torch.cuda.is_available() else "cpu"
# pipeline = pipeline.to(device=device)

# @spaces.GPU
# def predict(steps, seed):
#     print("HI")
#     generator = torch.manual_seed(seed)
#     for i in range(1,steps):
#         yield pipeline(generator=generator, num_inference_steps=i).images[0]

# gr.Interface(
#     predict,
#     inputs=[
#         grc.Slider(0, 1000, label='Inference Steps', value=42, step=1),
#         grc.Slider(0, 2147483647, label='Seed', value=42, step=1),
#     ],
#     outputs=gr.Image(height=28, width=28, type="pil", elem_id="output_image"),
#     css="#output_image{width: 256px !important; height: 256px !important;}",
#     title="Model Problems: Infringing on MNIST!",
#     description="Opening the black box.",
# ).queue().launch()


from diffusers import StableDiffusionPipeline
import torch

modellist=['nathanReitinger/MNIST-diffusion-oneImage',
 'nathanReitinger/MNIST-diffusion',
#  'nathanReitinger/MNIST-GAN', 
#  'nathanReitinger/MNIST-GAN-noDropout'
 ]

# pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
# device = "cuda" if torch.cuda.is_available() else "cpu"
# pipeline = pipeline.to(device=device)


def getModel(model):
  model_id = model
  print(model_id)
  if 'diffusion' in model_id:
    pipe = DiffusionPipeline.from_pretrained(model_id)
    pipe = pipe.to("cpu")
    image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]

  else:
    pipe = DiffusionPipeline.from_pretrained('nathanReitinger/MNIST-diffusion')
    pipe = pipe.to("cpu")
    test = from_pretrained_keras('nathanReitinger/MNIST-GAN')
    image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]


  return image


import gradio as gr
interface = gr.Interface(fn=getModel, 
                        inputs=[gr.Dropdown(modellist)],
                         outputs="image", 
                        title='Model Problems (infringement)')

interface.launch()