Upload 2 files

app.py

@@ -125,9 +125,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as demo:
         num_imagesone = gr.Slider(1, max_imagesone, value=max_imagesone, step=1, label='Nobody gets to see this label so I can put here whatever I want!', visible=False)
         
         with gr.Row():
-            gen_button = gr.Button('Generate', scale=3)
-            stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
-        gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
+            gen_button = gr.Button('Generate', variant='primary', scale=3)
+            #stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
+        #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
         
         with gr.Row():
             output = [gr.Image(label='', show_download_button=True, elem_classes="outputone",
@@ -140,8 +140,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as demo:
             gen_event = gr.on(triggers=[gen_button.click, txt_input.submit],
                                fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
                                inputs=[img_in, num_imagesone, model_choice, txt_input, neg_input,
-                                       height, width, steps, cfg, seed], outputs=[o])
-            stop_button.click(lambda: gr.update(interactive = False), None, stop_button, cancels=[gen_event])
+                                       height, width, steps, cfg, seed], outputs=[o],
+                                       concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button
+            #stop_button.click(lambda: gr.update(interactive = False), None, stop_button, cancels=[gen_event])
         with gr.Row():
             gr.HTML(
     """
@@ -170,8 +171,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as demo:
         
         with gr.Row():
             gen_button2 = gr.Button(f'Generate up to {int(max_images)} images in up to 3 minutes total', scale=3)
-            stop_button2 = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
-        gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
+            #stop_button2 = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
+        #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
         gr.HTML(
         """
             <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
@@ -195,8 +196,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as demo:
             gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
                                fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
                                inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
-                                       height2, width2, steps2, cfg2, seed2], outputs=[o])
-            stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
+                                       height2, width2, steps2, cfg2, seed2], outputs=[o],
+                                       concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button
+            #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
         with gr.Row():
             gr.HTML(
     """

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)
 
 
@@ -563,20 +564,20 @@ 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)