Update app.py

app.py

@@ -1,70 +1,56 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-
-def respond(
-    message,
-    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
-    """
+def respond(message, history: list[dict[str,str]], system_message, max_tokens, temperature, top_p, hf_token: gr.OAuthToken):
+    if not hf_token:
+        yield "⚠️ You must login first! (Bạn phải đăng nhập trước!)"
+        return
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
+    
     messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
+    messages.extend([m for m in history if m.get("role") in ["user","assistant"] and "content" in m])
     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,
-    ):
-        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,
-    type="messages",
-    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:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
+    try:
+        for message in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message.choices[0].delta.get("content","") if message.choices else ""
+            response += token
+            yield response
+    except Exception as e:
+        yield f"❌ Error: {e}"
+
+with gr.Blocks(css=".gradio-container {background-color: #1a1a1a; color: #eee;}") as demo:
+    with gr.Row():
+        with gr.Column(scale=3, min_width=300):
+            gr.Markdown("## ⚡ Login & Settings (Đăng nhập & Cài đặt)")
+            hf_token = gr.LoginButton(label="Login to HF (Đăng nhập HF)")
+            system_message = gr.Textbox(value="You are a friendly Chatbot. (Bạn là chatbot thân thiện.)", label="System message", lines=2)
+            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+        with gr.Column(scale=7):
+            chatbot = gr.Chatbot(label="AI Chat (Trò chuyện với AI)").style(height=600)
+            with gr.Row():
+                msg = gr.Textbox(placeholder="Type your message here... (Gõ tin nhắn vào đây)", lines=1)
+                submit = gr.Button("Send (Gửi)")
+    
+    submit.click(
+        respond,
+        inputs=[msg, chatbot, system_message, max_tokens, temperature, top_p, hf_token],
+        outputs=chatbot
+    )
+    msg.submit(
+        respond,
+        inputs=[msg, chatbot, system_message, max_tokens, temperature, top_p, hf_token],
+        outputs=chatbot
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()