Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
 import spaces
 from huggingface_hub import InferenceClient
+from huggingface_hub.errors import HfHubHTTPError, InferenceTimeoutError
 from transformers import pipeline
 
 LOCAL_MODEL = "Qwen/Qwen3-0.6B"
-REMOTE_MODEL = "openai/gpt-oss-20b"
+REMOTE_MODEL = "openai/nopenope"
 
 pipe = pipeline(
     "text-generation",
@@ -15,18 +16,22 @@ pipe = pipeline(
 
 fancy_css = """
 .gradio-container {
-    width: 96% !important;
-    max-width: none !important;
+    width: 95% !important;
+    max-width: 1400px !important;
+    margin: 0 auto;
 }
+
 #app-title {
     text-align: center;
     margin-bottom: 4px;
 }
+
 #app-subtitle {
     text-align: center;
     color: var(--body-text-color-subdued);
     margin-bottom: 24px;
 }
+
 #chat-container {
     width: 100%;
     border: 1px solid var(--border-color-primary);
@@ -34,15 +39,18 @@ fancy_css = """
     padding: 16px;
     box-shadow: 0 2px 8px rgba(0, 0, 0, 0.06);
 }
+
 #model-note {
     font-size: 0.9em;
     color: var(--body-text-color-subdued);
     margin-top: 8px;
 }
+
 @media (max-width: 768px) {
     .gradio-container {
         width: 98% !important;
     }
+
     #chat-container {
         padding: 8px;
     }
@@ -68,6 +76,35 @@ def local_generate(
     return outputs[0]["generated_text"][-1]["content"]
 
 
+def remote_generate(
+    messages,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token,
+):
+    client = InferenceClient(
+        token=hf_token.token,
+        model=REMOTE_MODEL,
+    )
+
+    response = ""
+
+    for chunk in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        choices = chunk.choices
+
+        if choices and choices[0].delta.content:
+            response += choices[0].delta.content
+
+        yield response
+
+
 def respond(
     message,
     history: list[dict[str, str]],
@@ -75,14 +112,14 @@ def respond(
     max_tokens,
     temperature,
     top_p,
-    use_local_model,
+    model_mode,
     hf_token: gr.OAuthToken,
 ):
     messages = [{"role": "system", "content": system_message}]
     messages.extend(history)
     messages.append({"role": "user", "content": message})
 
-    if use_local_model:
+    if model_mode == "Local Model":
         print("[MODE] local")
 
         response = local_generate(
@@ -92,37 +129,76 @@ def respond(
             top_p,
         )
 
-        yield response
+        yield f"**Backend: Local Model**\n\n{response}"
         return
 
-    print("[MODE] api")
-
     if hf_token is None or not getattr(hf_token, "token", None):
+        if model_mode == "Automatic Failover":
+            print("[FAILOVER] No Hugging Face token. Using local model.")
+
+            response = local_generate(
+                messages,
+                max_tokens,
+                temperature,
+                top_p,
+            )
+
+            yield (
+                "**Remote model unavailable. "
+                "Automatically switched to Local Model.**\n\n"
+                + response
+            )
+            return
+
         yield "⚠️ Please log in with your Hugging Face account first."
         return
 
-    client = InferenceClient(
-        token=hf_token.token,
-        model=REMOTE_MODEL,
-    )
+    if model_mode == "Remote API":
+        print("[MODE] api")
 
-    response = ""
+        try:
+            for response in remote_generate(
+                messages,
+                max_tokens,
+                temperature,
+                top_p,
+                hf_token,
+            ):
+                yield f"**Backend: Remote API**\n\n{response}"
 
-    for chunk in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = chunk.choices
-        token = ""
+        except (InferenceTimeoutError, HfHubHTTPError) as error:
+            yield f"⚠️ Remote API error: {error}"
 
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+        return
 
-        response += token
-        yield response
+    print("[MODE] automatic")
+
+    try:
+        for response in remote_generate(
+            messages,
+            max_tokens,
+            temperature,
+            top_p,
+            hf_token,
+        ):
+            yield f"**Backend: Remote API**\n\n{response}"
+
+    except (InferenceTimeoutError, HfHubHTTPError) as error:
+        print(f"[FAILOVER] Remote model failed: {error}")
+        print("[FAILOVER] Switching to local model.")
+
+        response = local_generate(
+            messages,
+            max_tokens,
+            temperature,
+            top_p,
+        )
+
+        yield (
+            "**Remote model failed. "
+            "Automatically switched to Local Model.**\n\n"
+            + response
+        )
 
 
 chatbot = gr.ChatInterface(
@@ -153,9 +229,14 @@ chatbot = gr.ChatInterface(
             step=0.05,
             label="Top-p (nucleus sampling)",
         ),
-        gr.Checkbox(
-            label="Use Local Model",
-            value=False,
+        gr.Radio(
+            choices=[
+                "Remote API",
+                "Local Model",
+                "Automatic Failover",
+            ],
+            value="Remote API",
+            label="Model mode",
         ),
     ],
 )
@@ -171,7 +252,7 @@ with gr.Blocks(css=fancy_css) as demo:
     )
 
     gr.Markdown(
-        "A fancier version of the standard Huggging Face chatbot template.",
+        "Compare a remotely hosted LLM with a model running locally on the Space.",
         elem_id="app-subtitle",
     )
 
@@ -179,10 +260,11 @@ with gr.Blocks(css=fancy_css) as demo:
         chatbot.render()
 
         gr.Markdown(
-            "Use **Additional inputs** to switch between the API model and the locally executed model.",
+            "Use **Additional inputs** to choose between the remote API, "
+            "the locally executed model, or automatic failover.",
             elem_id="model-note",
         )
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()