Support gr.LoginButton for gr.load()

.changeset/four-wasps-hug.md

@@ -0,0 +1,5 @@
+---
+"gradio": minor
+---
+
+feat:Support `gr.LoginButton` for `gr.load()`

gradio/external.py

@@ -20,7 +20,7 @@
 from gradio_client.utils import encode_url_or_file_to_base64
 from packaging import version
 
-import gradio
+import gradio as gr
 from gradio import components, external_utils, utils
 from gradio.components.multimodal_textbox import MultimodalValue
 from gradio.context import Context
@@ -36,6 +36,7 @@
     from gradio.blocks import Blocks
     from gradio.chat_interface import ChatInterface
     from gradio.components.chatbot import MessageDict
+    from gradio.components.login_button import LoginButton
     from gradio.interface import Interface
 
 
@@ -47,7 +48,7 @@ def load(
     | None = None,
     token: str | None = None,
     hf_token: str | None = None,
-    accept_token: bool = False,
+    accept_token: bool | LoginButton = False,
     provider: PROVIDER_T | None = None,
     **kwargs,
 ) -> Blocks:
@@ -57,7 +58,7 @@ def load(
         name: the name of the model (e.g. "google/vit-base-patch16-224") or Space (e.g. "flax-community/spanish-gpt2"). This is the first parameter passed into the `src` function. Can also be formatted as {src}/{repo name} (e.g. "models/google/vit-base-patch16-224") if `src` is not provided.
         src: function that accepts a string model `name` and a string or None `token` and returns a Gradio app. Alternatively, this parameter takes one of two strings for convenience: "models" (for loading a Hugging Face model through the Inference API) or "spaces" (for loading a Hugging Face Space). If None, uses the prefix of the `name` parameter to determine `src`.
         token: optional token that is passed as the second parameter to the `src` function. If not explicitly provided, will use the HF_TOKEN environment variable or fallback to the locally-saved HF token when loading models but not Spaces (when loading Spaces, only provide a token if you are loading a trusted private Space as the token can be read by the Space you are loading). Find your HF tokens here: https://huggingface.co/settings/tokens.
-        accept_token: if True, a Textbox component is first rendered to allow the user to provide a token, which will be used instead of the `token` parameter when calling the loaded model or Space.
+        accept_token: if True, a Textbox component is first rendered to allow the user to provide a token, which will be used instead of the `token` parameter when calling the loaded model or Space. Can also provide an instance of a gr.LoginButton in the same Blocks scope, which allows the user to login with a Hugging Face account whose token will be used instead of the `token` parameter when calling the loaded model or Space.
         kwargs: additional keyword parameters to pass into the `src` function. If `src` is "models" or "Spaces", these parameters are passed into the `gr.Interface` or `gr.ChatInterface` constructor.
         provider: the name of the third-party (non-Hugging Face) providers to use for model inference (e.g. "replicate", "sambanova", "fal-ai", etc). Should be one of the providers supported by `huggingface_hub.InferenceClient`. This parameter is only used when `src` is "models"
     Returns:
@@ -93,15 +94,31 @@ def load(
     ):
         token = os.environ.get("HF_TOKEN")
 
+    if isinstance(src, Callable):
+        return src(name, token, **kwargs)
+
     if not accept_token:
-        if isinstance(src, Callable):
-            return src(name, token, **kwargs)
         return load_blocks_from_huggingface(
             name=name, src=src, hf_token=token, provider=provider, **kwargs
         )
-    else:
-        import gradio as gr
+    elif isinstance(accept_token, gr.LoginButton):
+        with gr.Blocks(fill_height=True) as demo:
+            if not accept_token.is_rendered:
+                accept_token.render()
 
+            @gr.render(triggers=[demo.load])
+            def create_blocks(oauth_token: gr.OAuthToken | None):
+                token_value = None if oauth_token is None else oauth_token.token
+                return load_blocks_from_huggingface(
+                    name=name,
+                    src=src,
+                    hf_token=token_value,
+                    provider=provider,
+                    **kwargs,
+                )
+
+        return demo
+    else:
         with gr.Blocks(fill_height=True) as demo:
             with gr.Accordion("Enter your token and press enter") as accordion:
                 textbox = gr.Textbox(
@@ -140,10 +157,12 @@ def load_token(token_value):
 
             @gr.render(inputs=[textbox], triggers=[textbox.submit])
             def create(token_value):
-                if isinstance(src, Callable):
-                    return src(name, token_value, **kwargs)
                 return load_blocks_from_huggingface(
-                    name=name, src=src, hf_token=token_value, **kwargs
+                    name=name,
+                    src=src,
+                    hf_token=token_value,
+                    provider=provider,
+                    **kwargs,
                 )
 
         return demo
@@ -152,43 +171,40 @@ def create(token_value):
 def load_blocks_from_huggingface(
     name: str,
     src: str,
-    hf_token: str | Literal[False] | None = None,
+    hf_token: str | None = None,
     alias: str | None = None,
     provider: PROVIDER_T | None = None,
     **kwargs,
 ) -> Blocks:
     """Creates and returns a Blocks instance from a Hugging Face model or Space repo."""
-    factory_methods: dict[str, Callable] = {
-        # for each repo type, we have a method that returns the Interface given the model name & optionally an hf_token
-        "huggingface": from_model,
-        "models": from_model,
-        "spaces": from_spaces,
-    }
-    if hf_token is not None and hf_token is not False:
+    if hf_token is not None:
         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."""
             )
         Context.hf_token = hf_token
 
-    if src == "spaces" and hf_token is None:
-        hf_token = False  # Since Spaces can read the token, we don't want to pass it in unless the user explicitly provides it
-    blocks: gradio.Blocks = factory_methods[src](
-        name, hf_token=hf_token, alias=alias, provider=provider, **kwargs
-    )
+    if src == "spaces":
+        # Spaces can read the token, so we don't want to pass it in unless the user explicitly provides it
+        token = False if hf_token is None else hf_token
+        blocks = from_spaces(
+            name, hf_token=token, alias=alias, provider=provider, **kwargs
+        )
+    else:
+        blocks = from_model(
+            name, hf_token=hf_token, alias=alias, provider=provider, **kwargs
+        )
     return blocks
 
 
 def from_model(
     model_name: str,
-    hf_token: str | Literal[False] | None,
+    hf_token: str | None,
     alias: str | None,
     provider: PROVIDER_T | None = None,
     **kwargs,
 ) -> Blocks:
     headers = {"X-Wait-For-Model": "true"}
-    if hf_token is False:
-        hf_token = None
     client = huggingface_hub.InferenceClient(
         model=model_name, headers=headers, token=hf_token, provider=provider
     )
@@ -288,6 +304,7 @@ def custom_post_binary(data):
                 "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."
             ]
         ]
+        postprocess = lambda x: x.summary_text  # noqa: E731
         fn = client.summarization
     # Example: distilbert-base-uncased-finetuned-sst-2-english
     elif p == "text-classification":
@@ -451,6 +468,7 @@ def query_huggingface_inference_endpoints(*data):
             data = preprocess(*data)
         try:
             data = fn(*data)  # type: ignore
+            print("data after fn", data)
         except huggingface_hub.utils.HfHubHTTPError as e:  # type: ignore
             if "429" in str(e):
                 raise TooManyRequestsError() from e
@@ -466,10 +484,11 @@ def query_huggingface_inference_endpoints(*data):
         "outputs": outputs,
         "title": model_name,
         "examples": examples,
+        "cache_examples": False,
     }
 
     kwargs = dict(interface_info, **kwargs)
-    interface = gradio.Interface(**kwargs)
+    interface = gr.Interface(**kwargs)
     return interface
 
 
@@ -565,7 +584,7 @@ def from_spaces_blocks(space: str, hf_token: str | None | Literal[False]) -> Blo
             predict_fns.append(endpoint.make_end_to_end_fn(helper))
         else:
             predict_fns.append(None)
-    return gradio.Blocks.from_config(client.config, predict_fns, client.src)  # type: ignore
+    return gr.Blocks.from_config(client.config, predict_fns, client.src)  # type: ignore
 
 
 def from_spaces_interface(
@@ -610,7 +629,7 @@ def fn(*data):
 
     kwargs = dict(config, **kwargs)
     kwargs["_api_mode"] = True
-    interface = gradio.Interface(**kwargs)
+    interface = gr.Interface(**kwargs)
     return interface
 
 
@@ -784,7 +803,7 @@ def load_chat(
         raise ImportError(
             "To use OpenAI API Client, you must install the `openai` package. You can install it with `pip install openai`."
         ) from e
-    from gradio.chat_interface import ChatInterface
+    from gr.chat_interface import ChatInterface
 
     client = OpenAI(api_key=token, base_url=base_url)
     start_message = (
@@ -796,7 +815,7 @@ def open_api(message: str | MultimodalValue, history: list | None) -> str | None
         history = history or start_message
         if len(history) > 0 and isinstance(history[0], (list, tuple)):
             history = ChatInterface._tuples_to_messages(history)
-        conversation = format_conversation(history, message)
+        conversation = format_conversation(history, message)  # type: ignore
         return (
             client.chat.completions.create(
                 model=model,
@@ -812,7 +831,7 @@ def open_api_stream(
         history = history or start_message
         if len(history) > 0 and isinstance(history[0], (list, tuple)):
             history = ChatInterface._tuples_to_messages(history)
-        conversation = format_conversation(history, message)
+        conversation = format_conversation(history, message)  # type: ignore
         stream = client.chat.completions.create(
             model=model,
             messages=conversation,  # type: ignore
@@ -839,7 +858,7 @@ def open_api_stream(
         open_api_stream if streaming else open_api,
         type="messages",
         multimodal=bool(file_types),
-        textbox=gradio.MultimodalTextbox(file_types=supported_extensions)
+        textbox=gr.MultimodalTextbox(file_types=supported_extensions)
         if file_types
         else None,
         **kwargs,