Update app.py

app.py

@@ -1,64 +1,41 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
+from huggingface_hub import list_models
+
+
+def hello(profile: gr.OAuthProfile | None) -> str:
+    # ^ expect a gr.OAuthProfile object as input to get the user's profile
+    # if the user is not logged in, profile will be None
+    if profile is None:
+        return "I don't know you."
+    return f"Hello {profile.name}"
+
+
+def list_private_models(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None) -> str:
+    # ^ expect a gr.OAuthToken object as input to get the user's token
+    # if the user is not logged in, oauth_token will be None
+    if oauth_token is None:
+        return "Please log in to list private models."
+    models = [
+        f"{model.id} ({'private' if model.private else 'public'})"
+        for model in list_models(author=profile.username, token=oauth_token.token)
+    ]
+    return "Models:\n\n" + "\n - ".join(models) + "."
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        "# Gradio OAuth Space"
+        "\n\nThis Space is a demo for the **Sign in with Hugging Face** feature. "
+        "Duplicate this Space to get started."
+        "\n\nFor more details, check out:"
+        "\n- https://www.gradio.app/guides/sharing-your-app#o-auth-login-via-hugging-face"
+        "\n- https://huggingface.co/docs/hub/spaces-oauth"
+    )
     gr.LoginButton()
-    demo.launch()
+    # ^ add a login button to the Space
+    m1 = gr.Markdown()
+    m2 = gr.Markdown()
+    demo.load(hello, inputs=None, outputs=m1)
+    demo.load(list_private_models, inputs=None, outputs=m2)
+
+demo.launch()