Upload app.py with huggingface_hub

app.py

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+
+import torch
+import pandas
+import gradio
+import PIL
+import huggingface_hub
+import huggingface_hub.hf_api
+import json
+import requests
+# import openai
+# openai.api_key = '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'
+class HFace_Pluto(object):
+  #
+  # initialize the object
+  def __init__(self, name="Pluto",*args, **kwargs):
+    super(HFace_Pluto, self).__init__(*args, **kwargs)
+    self.author = "Duc Haba"
+    self.name = name
+    self._ph()
+    self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__))
+    self._pp("Code name", self.name)
+    self._pp("Author is", self.author)
+    self._ph()
+    #
+    # define class var for stable division
+    self._device = 'cuda'
+    self._steps = [3,8,21,55,89,144]
+    self._guidances = [1.1,3.0,5.0,8.0,13.0,21.0]
+    self._models = ['CompVis/stable-diffusion-v1-4', #default
+      'stabilityai/stable-diffusion-2-1', #1 latest as of feb. 28, 2023
+      'dreamlike-art/dreamlike-diffusion-1.0', #2 ilike
+      'prompthero/openjourney-v2',   #3 ilike
+      'itecgo/sd-lexica_6k-model', #4
+      'nitrosocke/mo-di-diffusion',
+      'coreco/seek.art_MEGA',
+      'andite/anything-v4.0', #7 anime
+      'nitrosocke/Nitro-Diffusion',
+      '22h/vintedois-diffusion-v0-1', #9 ilike
+      'Lykon/DreamShaper', #10 ilike
+      'rrustom/stable-architecture-diffusers',  # 11
+      'hakurei/waifu-diffusion',   #anime style
+      'wavymulder/portraitplus',   #13 ilike
+      'dreamlike-art/dreamlike-photoreal-2.0', #no check
+      'johnslegers/epic-diffusion', #15 ilike good example
+      'nitrosocke/Arcane-Diffusion' #16 ilike
+      ] 
+    self._seed = 667 # sum of walnut in ascii (or Angle 667)
+    self._width = 512
+    self._height = 512
+    self._step = 50
+    self._guidances = 7.5
+    #self._generator = torch.Generator(device='cuda')
+    self.pipes = []
+    self.prompts = []
+    self.images = []
+    self.seeds = []
+    self.fname_id = 0
+    self.dname_img = "img_colab/"
+    return
+  #
+  # pretty print output name-value line
+  def _pp(self, a, b):
+    print("%34s : %s" % (str(a), str(b)))
+    return
+  #
+  # pretty print the header or footer lines
+  def _ph(self):
+    print("-" * 34, ":", "-" * 34)
+    return
+  # 
+  # fetch huggingface file
+  def fetch_hface_files(self,
+    hf_names,
+    hf_space="duchaba/skin_cancer_diagnose",
+    local_dir="/content/"):
+    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
+    try:
+      for f in hf_names:
+        lo = local_dir + f
+        huggingface_hub.hf_hub_download(repo_id=hf_space, filename=f,
+          use_auth_token=True,repo_type=huggingface_hub.REPO_TYPE_SPACE,
+          force_filename=lo)
+    except:
+      self._pp("*Error", f)
+    return
+  #
+  #
+  def push_hface_files(self,
+    hf_names,
+    hf_space="duchaba/skin_cancer_diagnose",
+    local_dir="/content/"):
+    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
+    try:
+      for f in hf_names:
+        lo = local_dir + f
+        huggingface_hub.upload_file(
+          path_or_fileobj=lo, 
+          path_in_repo=f, 
+          repo_id=hf_space,
+          repo_type=huggingface_hub.REPO_TYPE_SPACE)
+    except:
+      self._pp("*Error", f)
+    return
+  #
+  def write_file(self,fname, txt):
+    f = open(fname, "w")
+    f.writelines("\n".join(txt))
+    f.close()
+    return
+  def draw_it(self,prompt):
+    url = 'lion.png'
+    img = PIL.Image.open(url)
+    return img
+  # 
+# add module/method
+#
+import functools
+def add_method(cls):
+  def decorator(func):
+    @functools.wraps(func) 
+    def wrapper(*args, **kwargs): 
+      return func(*args, **kwargs)
+    setattr(cls, func.__name__, wrapper)
+    return func # returning func means func can still be used normally
+  return decorator
+
+# instantiate the class
+monty = HFace_Pluto('Monty')
+
+# use magic prompt model
+monty.gpt2_pipe = transformers.pipeline('text-generation', 
+  model='Gustavosta/MagicPrompt-Stable-Diffusion', 
+  tokenizer='gpt2')
+
+# fetch prompt
+@add_method(HFace_Pluto)
+def _print_response(self, response):
+  for x in response:
+    print(x['generated_text'])
+  return
+#
+@add_method(HFace_Pluto)
+def fetch_prompt(self, prompt, max_num=1, max_length=240, is_print=False):
+  response = self.gpt2_pipe(prompt, 
+    max_length=max_length, 
+    num_return_sequences=max_num)
+  #
+  if (is_print):
+    self._print_response(response)
+  return response
+
+# use pluto _pp for interface testing
+# iface = gradio.Interface(fn=pluto.draw_it, inputs="text", outputs="image",
+#   flagging_options=["Excellent", "Good", "Not Bad"])
+iface = gradio.Interface(fn=monty.fetch_prompt, inputs="text", outputs="text",
+  flagging_options=[])
+
+# Launch it
+iface.launch()