Update app.py

app.py

@@ -1,69 +1,107 @@
+from __future__ import annotations
+
+from collections.abc import Generator
+
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+MODEL_ID = "MarkChenX/lfm2-quantum-128m-sft"
+
 
 def respond(
-    message,
+    message: str,
     history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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(token=hf_token.token, model="MarkChenX/lfm2-quantum-128m-sft")
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+    hf_token: gr.OAuthToken | None,
+) -> Generator[str, None, None]:
+    """Generate and stream a response from the Hugging Face model."""
 
-    messages = [{"role": "system", "content": system_message}]
+    if hf_token is None:
+        yield "Please sign in with Hugging Face before sending a message."
+        return
 
-    messages.extend(history)
+    messages = [
+        {
+            "role": "system",
+            "content": system_message.strip() or "You are a friendly chatbot.",
+        },
+        *history,
+        {"role": "user", "content": message},
+    ]
 
-    messages.append({"role": "user", "content": message})
+    client = InferenceClient(
+        model=MODEL_ID,
+        token=hf_token.token,
+    )
 
     response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = 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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
+    try:
+        for chunk in client.chat_completion(
+            messages=messages,
+            max_tokens=int(max_tokens),
+            temperature=float(temperature),
+            top_p=float(top_p),
+            stream=True,
+        ):
+            if not chunk.choices:
+                continue
+
+            token = chunk.choices[0].delta.content or ""
+            response += token
+            yield response
+
+    except Exception as exc:
+        # Keep the existing partial response visible if generation fails.
+        error_message = f"\n\nGeneration failed: {exc}"
+        yield response + error_message
+
+
+with gr.Blocks(title="LFM2 Quantum Chat") as demo:
     with gr.Sidebar():
+        gr.Markdown("### Authentication")
         gr.LoginButton()
-    chatbot.render()
+
+    gr.ChatInterface(
+        fn=respond,
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a friendly chatbot.",
+                label="System message",
+                lines=3,
+            ),
+            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",
+            ),
+        ],
+    )
 
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    # Spaces already exposes the application publicly.
+    # Disabling SSR avoids launching the experimental Node.js SSR process.
+    demo.launch(
+        share=False,
+        ssr_mode=False,
+    )