fancy

app.py

@@ -13,6 +13,7 @@ from PIL import Image
 from huggingface_hub import from_pretrained_keras
 from math import sqrt, ceil
 import numpy as np
+import gradio as gr
 
 modelieo=[
  'nathanReitinger/MNIST-diffusion',
@@ -77,9 +78,9 @@ def get_sims(gen_filepath, gen_label, file_path, hunting_time_limit):
     now = time.time()
     if now-start > hunting_time_limit:
       print(str(now-start) +  "s")
-      return lowest_image_path
+      return [lowest_image_path, lowest_score]
                 
-  return lowest_image_path
+  return [lowest_image_path, lowest_score]
 
 
 def digit_recognition(filename):
@@ -186,215 +187,35 @@ def TextToImage(Prompt,inference_steps, model):
     hunting_time_limit = abs(int(prompt))
 
   original_image, other_images = get_other(image, hunting_time_limit)
+  the_file = other_images[0]
+  the_rmse = other_images[1]
   ai_gen = Image.open(open(original_image, 'rb'))
-  training_data = Image.open(open(other_images, 'rb'))
-  return [ai_gen, training_data]
+  training_data = Image.open(open(the_file, 'rb'))
+
+  another_one = (training_data, "RMSE: " + str(round(the_rmse,5) ))
+
+  return [ai_gen, another_one]
+
+
+with gr.Blocks() as app:
+  
+  interface = gr.Interface(fn=TextToImage, 
+                          inputs=[gr.Textbox(show_label=True, label='How many seconds to hunt for copies?',), gr.Slider(1, 1000, label='Inference Steps', value=100, step=1), gr.Dropdown(modelieo)],
+                          outputs=gr.Gallery(label="Generated image", show_label=True, elem_id="gallery", columns=[2], rows=[1], object_fit="contain", height="auto"), 
+                          # css="#output_image{width: 256px !important; height: 256px !important;}",
+                          title='Unconditional Image Generation',
+                          )
+  gr.HTML(
+      "<hr>"
+      "<h1><center>Do machine learing models store protected content?</center></h1>" +
+      "<p><center><span style='color: red;'>Enter a time to hunt for copies (seconds), select a model, and hit submit!</center></p>" +
+      "<p><center><strong>These image generation models will give you a 'bespoke' generation ❤ of an MNIST hand-drawn digit</p>" +
+      "<p><center>then the program will search in training data (for <i>n</i> seconds) to find similar images: <a href='https://medium.com/@mygreatlearning/rmse-what-does-it-mean-2d446c0b1d0e'>RMSE<a>, lower is more similar</p>" +
+      "<p><a href='https://nathanreitinger.umiacs.io'>@nathanReitinger<a></p>"
+  )
+
+app.queue().launch()
+# interface.launch(share=True)
+
 
 
-import gradio as gr
-interface = gr.Interface(fn=TextToImage, 
-                        inputs=[gr.Textbox(show_label=True, label='How many seconds to hunt for copies?',), gr.Slider(1, 1000, label='Inference Steps', value=100, step=1), gr.Dropdown(modelieo)],
-                        outputs=gr.Gallery(label="Generated image", show_label=True, elem_id="gallery", columns=[2], rows=[1], object_fit="contain", height="auto"), 
-                        # css="#output_image{width: 256px !important; height: 256px !important;}",
-                        title='Unconditional Image Generation')
-
-interface.launch()
-
-
-
-
-
-# import tensorflow as tf
-# from diffusers import DiffusionPipeline
-# import spaces
-# # import torch
-# import PIL.Image
-# from PIL import Image
-# from torch.autograd import Variable
-# import gradio as gr
-# import gradio.components as grc
-# import numpy as np
-# from huggingface_hub import from_pretrained_keras
-# from image_similarity_measures.evaluate import evaluation
-# import keras
-# import time
-# import requests
-# import matplotlib.pyplot as plt
-# import os 
-# from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
-# from gradio_imageslider import ImageSlider
-
-# # os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
-
-# # 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
-
-# 	(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)
-# 	image = None
-# 	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=1).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]
-
-# 	########################################### let's save this image for comparison to others
-# 	fig = plt.figure(figsize=(1, 1))
-# 	plt.subplot(1, 1, 0+1)
-# 	plt.imshow(image, cmap='gray')
-# 	plt.axis('off')
-# 	plt.savefig(file_path + 'generated_image.png')
-# 	plt.close()
-
-# 	API_URL = "https://api-inference.huggingface.co/models/farleyknight/mnist-digit-classification-2022-09-04"
-
-# 	# get a prediction on what number this is
-# 	def query(filename):
-# 		with open(filename, "rb") as f:
-# 			data = f.read()
-# 		response = requests.post(API_URL, data=data)
-# 		return response.json()
-
-# 	# use latest model to generate a new image, return path
-# 	ret = False 
-# 	output = None
-# 	while ret == False:
-# 		output = query(file_path + 'generated_image.png')
-# 		if 'error' in output:
-# 			time.sleep(10)
-# 			ret = False 
-# 		else:
-# 			ret = True
-# 	print(output)
-
-# 	low_score_log = ''
-# 	this_label_for_this_image = int(output[0]['label'])
-# 	low_score_log += "this image has been identified as a:" + str(this_label_for_this_image) + "\n" + str(output) + "\n"
-# 	print("===================")
-
-# 	lowest_score = 10000
-# 	lowest_image = None
-
-# 	for i in range(len(train_labels)):
-# 		# print(i)
-# 		if train_labels[i] == this_label_for_this_image:
-
-# 				###
-# 				# get a real image (of correct number)
-# 				###
-
-# 				# print(i)
-# 				to_check = train_images[i]
-# 				fig = plt.figure(figsize=(1, 1))
-# 				plt.subplot(1, 1, 0+1)
-# 				plt.imshow(to_check, cmap='gray')
-# 				plt.axis('off')
-# 				plt.savefig(file_path + 'real_deal.png')
-# 				plt.close()
-
-# 				# baseline = evaluation(org_img_path='results/real_deal.png', pred_img_path='results/real_deal.png', metrics=["rmse", "psnr"])
-# 				# print("---")
-
-# 				###
-# 				# check how close that real training data is to generated number
-# 				###
-# 				results = evaluation(org_img_path=file_path + 'real_deal.png', pred_img_path=file_path+'generated_image.png', metrics=["rmse", "psnr"])
-# 				if results['rmse'] < lowest_score:
-
-# 					lowest_score = results['rmse']
-# 					lowest_image = to_check
-
-# 					# image1 = np.array(Image.open(file_path + 'real_deal.png'))
-# 					# image2 = np.array(Image.open(file_path + 'generated_image.png'))
-# 					# img1 = torch.from_numpy(image1).float().unsqueeze(0).unsqueeze(0)/255.0
-# 					# img2 = torch.from_numpy(image2).float().unsqueeze(0).unsqueeze(0)/255.0
-# 					# img1 = Variable( img1,	requires_grad=False)
-# 					# img2 = Variable( img2,	requires_grad=True)
-# 					# ssim_score = ssim(img1, img2).item()
-
-# 					# # sys.exit()
-# 					# # l2 = distance.euclidean(image1, image2)
-
-# 					# low_score_log += 'rmse score:' + str(lowest_score) + "\n"
-# 					# low_score_log += 'ssim score:' + str(ssim_score) + "\n"
-# 					# low_score_log += 'found when:' + str(round( ((i/len(train_labels)) * 100),2 )) + '%' + "\n"
-
-# 					# low_score_log += "---------\n"
-					
-# 					# print(lowest_score, ssim_score, str(round( ((i/len(train_labels)) * 100),2 )) + '%')
-
-# 					# fig = plt.figure(figsize=(1, 1))
-# 					# plt.subplot(1, 1, 0+1)
-# 					# plt.imshow(to_check, cmap='gray')
-# 					# plt.axis('off')
-# 					# plt.savefig(file_path+str(i) + "--" + str(lowest_score) + '---most_close.png')
-# 					# plt.close()
-
-								
-# 	# f = open(file_path + "score_log.txt", "w+")
-# 	# f.write(low_score_log)
-# 	# f.close()
-
-# 	print("Done!")
-
-
-# 	############################################ return image that you just generated
-# 	return [image, lowest_image]
-
-
-# import gradio as gr
-# output = "image"
-# interface = gr.Interface(fn=getModel, inputs=[gr.Dropdown(modellist)], css="#output_image{width: 256px !important; height: 256px !important;}", outputs=output,  title='Model Problems (infringement)') # outputs="image",
-# interface.launch(debug=True)

requirements.txt

@@ -2,24 +2,11 @@
 
 diffusers==0.27.2
 gradio==4.28.3
-<<<<<<< HEAD
 huggingface-hub==0.22.2
 image-similarity-measures==0.3.6
 matplotlib==3.8.4
 numpy==1.25.2
 pillow==10.3.0
 requests==2.31.0
-=======
-gradio_imageslider==0.0.20
-huggingface-hub==0.22.2
-image-similarity-measures==0.3.6
-keras==2.11.0
-matplotlib==3.8.4
-numpy==1.25.2
-pillow==10.3.0
-pytorch-msssim==1.0.0
-requests==2.31.0
-spaces==0.26.2
->>>>>>> 0fceaca51b594336a55134745a393f595fe283f7
 tensorflow==2.11.0
 torch==2.2.2