Update app.py

Comment out unnecessary sections and dependencies from colab notebook version

app.py

@@ -17,10 +17,9 @@ import gradio
 import torch
 import os
 
-# UNDER CONSTRUCTION (START)
+# UNDER CONSTRUCTION ---{{{
 import subprocess
-# UNDER CONSTRUCTION (END)
-
+# }}}---
 
 # FOR DEPLOYMENT: uncomment these and delete the notebook_login() below
 api_key = os.environ['api_key']
@@ -29,23 +28,25 @@ my_token = api_key
 # from huggingface_hub import notebook_login
 # notebook_login()
 
-import PIL
-from PIL import Image
+# NOT NEEDED FOR DEPLOYMENT ---{{{
+# import PIL
+# from PIL import Image
 
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows*cols
+# def image_grid(imgs, rows, cols):
+#     assert len(imgs) == rows*cols
 
-    w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols*w, rows*h))
-    grid_w, grid_h = grid.size
+#     w, h = imgs[0].size
+#     grid = Image.new('RGB', size=(cols*w, rows*h))
+#     grid_w, grid_h = grid.size
     
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i%cols*w, i//cols*h))
-    return grid
+#     for i, img in enumerate(imgs):
+#         grid.paste(img, box=(i%cols*w, i//cols*h))
+#     return grid
+# }}}---
 
 pretrained_model_name_or_path = "stabilityai/stable-diffusion-2"
 
-from IPython.display import Markdown
+# from IPython.display import Markdown
 from huggingface_hub import hf_hub_download
      
 
@@ -86,10 +87,10 @@ for repo_id_embeds in models_to_load:
     #!cp downloaded_embedding_folder
     #!cp downloaded_embedding_folder
 
-    # UNDER CONSTRUCTION (START)
+    # UNDER CONSTRUCTION ---{{{
     subprocess.call([f"cp {embeds_path} {downloaded_embedding_folder}"])
     subprocess.call([f"cp {token_path} {downloaded_embedding_folder}"])
-    # UNDER CONSTRUCTION (END)
+    # }}}---
 
     with open(f'{downloaded_embedding_folder}/token_identifier.txt', 'r') as file:
       placeholder_token_string = file.read()
@@ -97,9 +98,9 @@ for repo_id_embeds in models_to_load:
     # FOR DEPLOYMENT: address file system use
     #!wget -q -O $downloaded_embedding_folder/learned_embeds.bin $embeds_url
 
-    # UNDER CONSTRUCTION (START)
+    # UNDER CONSTRUCTION ---{{{
     subprocess.call([f"wget -q -O {downloaded_embedding_folder}/learned_embeds.bin {embeds_url}"])
-    # UNDER CONSTRUCTION (END)
+    # }}}---
 
   learned_embeds_path = f"{downloaded_embedding_folder}/learned_embeds.bin"
 
@@ -160,41 +161,44 @@ for repo_id_embeds in models_to_load:
 
 #@title 4. Print Available Concept Strings
 
-print("AVAILABLE CONCEPTS TO SELECT FROM")
-print("copy one and paste below under 'model'")
-print("------------------------------------------------------")
-# list(completed_concept_pipes)
-for model in completed_concept_pipes:
-  print(f"{model}")
-     
+# NOT NEEDED FOR DEPLOYMENT ---{{{
+# print("AVAILABLE CONCEPTS TO SELECT FROM")
+# print("copy one and paste below under 'model'")
+# print("------------------------------------------------------")
+# # list(completed_concept_pipes)
+# for model in completed_concept_pipes:
+#   print(f"{model}")
+# }}}---
 
 #@title 5. Optionally Test without Gradio
 
-model = "" #@param {type: "string"}
-prompt = "" #@param {type:"string"}
+# NOT NEEDED FOR DEPLOYMENT ---{{{
+# model = "" #@param {type: "string"}
+# prompt = "" #@param {type:"string"}
 
-if prompt and model:
-  if model not in completed_concept_pipes:
-    raise ValueError("Invalid Model Name")
+# if prompt and model:
+#   if model not in completed_concept_pipes:
+#     raise ValueError("Invalid Model Name")
 
-  model_token = model.split("/")[1]
-  prompt = f"{prompt} in the style of <{model_token}>"
+#   model_token = model.split("/")[1]
+#   prompt = f"{prompt} in the style of <{model_token}>"
 
-  if model == "sd-concepts-library/ahx-model-5":
-    prompt = f"{prompt} in the style of "
+#   if model == "sd-concepts-library/ahx-model-5":
+#     prompt = f"{prompt} in the style of "
 
-  num_samples = 1
-  num_rows = 1
+#   num_samples = 1
+#   num_rows = 1
 
-  all_images = [] 
-  pipe = completed_concept_pipes[model]
+#   all_images = [] 
+#   pipe = completed_concept_pipes[model]
 
-  for _ in range(num_rows):
-      images = pipe(prompt, num_images_per_prompt=num_samples, height=512, width=512, num_inference_steps=30, guidance_scale=7.5).images
-      all_images.extend(images)
+#   for _ in range(num_rows):
+#       images = pipe(prompt, num_images_per_prompt=num_samples, height=512, width=512, num_inference_steps=30, guidance_scale=7.5).images
+#       all_images.extend(images)
 
-  grid = image_grid(all_images, num_samples, num_rows)
-  grid
+#   grid = image_grid(all_images, num_samples, num_rows)
+#   grid
+# }}}---
      
 
 #@title 6. Define Custom CSS for Gradio