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README.md

@@ -1,10 +1,10 @@
 ---
-title: 868 AI Art Models Toy World (Gradio 4.x)
+title: 868 AI Art Models Toy World (Gradio 5.x)
 emoji: 🪅🌐
 colorFrom: green
 colorTo: gray
 sdk: gradio
-sdk_version: 4.39.0
+sdk_version: 5.0.1
 app_file: app.py
 pinned: false
 duplicated_from:

app.py

@@ -85,8 +85,7 @@ def gen_fn(model_index, prompt, nprompt="", height=None, width=None, steps=None,
     return result
 
 css="""
-.gradio-container {background-image: linear-gradient(#254150, #1e2f40, #182634) !important;
- color: #ffaa66 !important; font-family: 'IBM Plex Sans', sans-serif !important;
+.gradio-container {!important; font-family: 'IBM Plex Sans', sans-serif !important;
  text-align: center; max-width: 1200px; margin: 0 auto; !important;}
 h1 {font-size: 6em; color: #ffc99f; margin-top: 30px; margin-bottom: 30px;
  text-shadow: 3px 3px 0 rgba(0, 0, 0, 1) !important;}
@@ -98,19 +97,9 @@ h4 {display: inline-block; color: #ffffff !important; }
 .text-gray-500 {color: #ffc99f !important;}
 .gr-box {background-image: linear-gradient(#182634, #1e2f40, #254150) !important; border-top-color: #000000 !important;
  border-right-color: #ffffff !important; border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
-.gr-input {color: #ffc99f; !important; background-color: #254150 !important;}
-:root {--neutral-100: #000000 !important;}
-.gr-button {color: #ffffff !important; text-shadow: 1px 1px 0 rgba(0, 0, 0, 1) !important;
-background-image: linear-gradient(#76635a, #d2a489) !important; border-radius: 24px !important;
-border: solid 1px !important; border-top-color: #ffc99f !important; border-right-color: #000000 !important;
-border-bottom-color: #000000 !important; border-left-color: #ffc99f !important; padding: 6px 30px;}
-.gr-button:active {color: #ffc99f !important; font-size: 98% !important; text-shadow: 0px 0px 0 rgba(0, 0, 0, 1) !important;
- background-image: linear-gradient(#d2a489, #76635a) !important; border-top-color: #000000 !important;
-border-right-color: #ffffff !important; border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
-.gr-button:hover {filter: brightness(130%);}
 """
 
-with gr.Blocks(fill_width=True, css=css) as myface:
+with gr.Blocks(theme='John6666/YntecLight', fill_width=True, css=css) as myface:
     gr.HTML(f"""
      <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
               <div>
@@ -186,7 +175,7 @@ with gr.Blocks(fill_width=True, css=css) as myface:
         with gr.Column(scale=100):
             with gr.Group():
                 magic1 = gr.Textbox(label="Your Prompt", lines=4, elem_classes="gr-box") #Positive
-                with gr.Accordion("Advanced", open=False, visible=True, elem_classes="gr-box"):
+                with gr.Accordion("Advanced", open=False, visible=True):
                     neg_input = gr.Textbox(label='Negative prompt', lines=1, elem_classes="gr-box")
                     with gr.Row():
                         width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes="gr-box")
@@ -217,5 +206,5 @@ with gr.Blocks(fill_width=True, css=css) as myface:
     use_short.click(short_prompt, inputs=[input_text], outputs=magic1)
     see_prompts.click(text_it1, inputs=[input_text], outputs=magic1)
     
-myface.queue()
+#myface.queue()
 myface.launch(inline=True, show_api=False)

externalmod.py

@@ -9,7 +9,7 @@ import re
 import tempfile
 import warnings
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING, Callable, Literal
 
 import httpx
 import huggingface_hub
@@ -33,6 +33,7 @@ if TYPE_CHECKING:
     from gradio.interface import Interface
 
 
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
 server_timeout = 600
 
 
@@ -40,7 +41,7 @@ server_timeout = 600
 def load(
     name: str,
     src: str | None = None,
-    hf_token: str | None = None,
+    hf_token: str | Literal[False] | None = None,
     alias: str | None = None,
     **kwargs,
 ) -> Blocks:
@@ -51,7 +52,7 @@ def load(
     Parameters:
         name: the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
         src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
-        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.
+        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Will default to the locally saved token if not provided. Pass `token=False` if you don't want to send your token to the server. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide a token if you are loading a trusted private Space as it can be read by the Space you are loading.
         alias: optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
     Returns:
         a Gradio Blocks object for the given model
@@ -68,7 +69,7 @@ def load(
 def load_blocks_from_repo(
     name: str,
     src: str | None = None,
-    hf_token: str | None = None,
+    hf_token: str | Literal[False] | None = None,
     alias: str | None = None,
     **kwargs,
 ) -> Blocks:
@@ -92,7 +93,7 @@ def load_blocks_from_repo(
     if src.lower() not in factory_methods:
         raise ValueError(f"parameter: src must be one of {factory_methods.keys()}")
 
-    if hf_token is not None:
+    if hf_token is not None and hf_token is not False:
         if Context.hf_token is not None and Context.hf_token != hf_token:
             warnings.warn(
                 """You are loading a model/Space with a different access token than the one you used to load a previous model/Space. This is not recommended, as it may cause unexpected behavior."""
@@ -103,12 +104,16 @@ def load_blocks_from_repo(
     return blocks
 
 
-def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwargs):
+def from_model(
+    model_name: str, hf_token: str | Literal[False] | None, alias: str | None, **kwargs
+):
     model_url = f"https://huggingface.co/{model_name}"
     api_url = f"https://api-inference.huggingface.co/models/{model_name}"
     print(f"Fetching model from: {model_url}")
 
-    headers = {"Authorization": f"Bearer {hf_token}"} if hf_token is not None else {}
+    headers = (
+        {} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"}
+    )
     response = httpx.request("GET", api_url, headers=headers)
     if response.status_code != 200:
         raise ModelNotFoundError(
@@ -371,7 +376,11 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
     def query_huggingface_inference_endpoints(*data, **kwargs):
         if preprocess is not None:
             data = preprocess(*data)
-        data = fn(*data, **kwargs)  # type: ignore
+        try:
+            data = fn(*data, **kwargs)  # type: ignore
+        except huggingface_hub.utils.HfHubHTTPError as e:
+            if "429" in str(e):
+                raise TooManyRequestsError() from e
         if postprocess is not None:
             data = postprocess(data)  # type: ignore
         return data
@@ -383,7 +392,7 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
         "inputs": inputs,
         "outputs": outputs,
         "title": model_name,
-    #    "examples": examples,
+        #"examples": examples,
     }
 
     kwargs = dict(interface_info, **kwargs)
@@ -394,19 +403,12 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
 def from_spaces(
     space_name: str, hf_token: str | None, alias: str | None, **kwargs
 ) -> Blocks:
-    client = Client(
-        space_name,
-        hf_token=hf_token,
-        download_files=False,
-        _skip_components=False,
-    )
-
     space_url = f"https://huggingface.co/spaces/{space_name}"
 
     print(f"Fetching Space from: {space_url}")
 
     headers = {}
-    if hf_token is not None:
+    if hf_token not in [False, None]:
         headers["Authorization"] = f"Bearer {hf_token}"
 
     iframe_url = (
@@ -443,8 +445,7 @@ def from_spaces(
                 "Blocks or Interface locally. You may find this Guide helpful: "
                 "https://gradio.app/using_blocks_like_functions/"
             )
-        if client.app_version < version.Version("4.0.0b14"):
-            return from_spaces_blocks(space=space_name, hf_token=hf_token)
+        return from_spaces_blocks(space=space_name, hf_token=hf_token)
 
 
 def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
@@ -489,7 +490,7 @@ def from_spaces_interface(
     config = external_utils.streamline_spaces_interface(config)
     api_url = f"{iframe_url}/api/predict/"
     headers = {"Content-Type": "application/json"}
-    if hf_token is not None:
+    if hf_token not in [False, None]:
         headers["Authorization"] = f"Bearer {hf_token}"
 
     # The function should call the API with preprocessed data
@@ -529,7 +530,7 @@ def gr_Interface_load(
     src: str | None = None,
     hf_token: str | None = None,
     alias: str | None = None,
-    **kwargs,
+    **kwargs, # ignore
 ) -> Blocks:
     try:
         return load_blocks_from_repo(name, src, hf_token, alias)
@@ -543,8 +544,8 @@ def list_uniq(l):
 
 
 def get_status(model_name: str):
-    from huggingface_hub import InferenceClient
-    client = InferenceClient(timeout=10)
+    from huggingface_hub import AsyncInferenceClient
+    client = AsyncInferenceClient(token=HF_TOKEN, timeout=10)
     return client.get_model_status(model_name)
 
 
@@ -563,22 +564,49 @@ def is_loadable(model_name: str, force_gpu: bool = False):
 
 def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
     from huggingface_hub import HfApi
-    api = HfApi()
+    api = HfApi(token=HF_TOKEN)
     default_tags = ["diffusers"]
     if not sort: sort = "last_modified"
     limit = limit * 20 if check_status and force_gpu else limit * 5
     models = []
     try:
-        model_infos = api.list_models(author=author, task="text-to-image",
+        model_infos = api.list_models(author=author, #task="text-to-image",
                                        tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
     except Exception as e:
         print(f"Error: Failed to list models.")
         print(e)
         return models
     for model in model_infos:
-        if not model.private and not model.gated:
+        if not model.private and not model.gated or HF_TOKEN is not None:
            loadable = is_loadable(model.id, force_gpu) if check_status else True
            if not_tag and not_tag in model.tags or not loadable: continue
            models.append(model.id)
            if len(models) == limit: break
     return models
+
+
+def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
+    from PIL import Image, PngImagePlugin
+    import json
+    try:
+        metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
+        if steps > 0: metadata["num_inference_steps"] = steps
+        if cfg > 0: metadata["guidance_scale"] = cfg
+        if seed != -1: metadata["seed"] = seed
+        if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
+        metadata_str = json.dumps(metadata)
+        info = PngImagePlugin.PngInfo()
+        info.add_text("metadata", metadata_str)
+        image.save(savefile, "PNG", pnginfo=info)
+        return str(Path(savefile).resolve())
+    except Exception as e:
+        print(f"Failed to save image file: {e}")
+        raise Exception(f"Failed to save image file:") from e
+
+
+def randomize_seed():
+    from random import seed, randint
+    MAX_SEED = 2**32-1
+    seed()
+    rseed = randint(0, MAX_SEED)
+    return rseed

requirements.txt

@@ -1,3 +1,3 @@
-transformers
+transformers==4.44.0
 numpy<2
 torch==2.2.0