Update app.py

app.py

@@ -2,6 +2,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 
+# Load model
 model_name = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -32,12 +33,35 @@ def chat(user_input, history=[]):
     history.append((user_input, response))
     return history, history
 
-chatbot = gr.Chatbot()
-demo = gr.Interface(
-    fn=chat,
-    inputs=[gr.Textbox(placeholder="Say something..."), gr.State([])],
-    outputs=[chatbot, gr.State()],
-    title="DialoGPT Chat Assistant",
-)
+# Custom HTML Header
+custom_html = """
+<div style="text-align:center; padding: 20px; background-color: #1e1e2f; color: white; border-radius: 12px;">
+    <h1>🤖 Smart AI Assistant</h1>
+    <p>Talk to DialoGPT and experience AI conversation in real time.</p>
+</div>
+"""
 
+# Gradio Interface Setup
+with gr.Blocks(theme="soft") as demo:
+    # Display the HTML header
+    gr.HTML(custom_html)
+    
+    # Chatbot UI
+    chatbot = gr.Chatbot()
+
+    # Input Box and State for history
+    with gr.Row():
+        msg = gr.Textbox(placeholder="Say something...", elem_id="user-input")
+        state = gr.State([])
+
+    # Button for sending message
+    send_button = gr.Button("Send")
+
+    # Button click event to handle chat
+    send_button.click(fn=chat, inputs=[msg, state], outputs=[chatbot, state])
+    
+    # Optional: Pressing 'Enter' key to send the message
+    msg.submit(fn=chat, inputs=[msg, state], outputs=[chatbot, state])
+
+# Launch the Gradio interface
 demo.launch()