fancy

app.py

@@ -1,6 +1,6 @@
 # notes https://huggingface.co/spaces/Joeythemonster/Text-To-image-AllModels/blob/main/app.py
 
-
+import tensorflow as tf
 from diffusers import DiffusionPipeline
 import spaces
 # import torch
@@ -13,7 +13,7 @@ import keras
 import time
 import os 
 
-os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
+# os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
 
 # options = ['Placeholder A', 'Placeholder B', 'Placeholder C']
 
@@ -24,31 +24,32 @@ os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
 
 # @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]
+#		 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.",
+#		 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'
+	'nathanReitinger/MNIST-diffusion',
+#	'nathanReitinger/MNIST-GAN', 
+#	'nathanReitinger/MNIST-GAN-noDropout'
  ]
 
 # pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
@@ -57,27 +58,29 @@ modellist=['nathanReitinger/MNIST-diffusion-oneImage',
 
 
 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]
+	model_id = model
 
-  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]
+	(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
+	RANDO = str(time.time())
+	file_path = 'tester/' + model_id.replace("/", "-") + "/" + RANDO + '/'
+	os.makedirs(file_path)
+	train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
+	train_images = (train_images - 127.5) / 127.5	# Normalize the images to [-1, 1]
 
+	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
+	return image
 
 
 import gradio as gr
-interface = gr.Interface(fn=getModel, 
-                        inputs=[gr.Dropdown(modellist)],
-                         outputs="image", 
-                        title='Model Problems (infringement)')
-
+interface = gr.Interface(fn=getModel, inputs=[gr.Dropdown(modellist)], css="#output_image{width: 256px !important; height: 256px !important;}", outputs="image",  title='Model Problems (infringement)')
 interface.launch()