app.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# Load the pre-trained model
model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

chat_history_ids = None

# Function to handle the chat logic
def chat(user_input, history=[]):
    global chat_history_ids
    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')

    if chat_history_ids is not None:
        input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
    else:
        input_ids = new_input_ids

    chat_history_ids = model.generate(
        input_ids,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
        temperature=0.7,
        top_p=0.9,
        no_repeat_ngram_size=3,
        top_k=50,
        do_sample=True,
    )

    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    history.append((user_input, response))
    return history, history

# 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
def launch_gradio_interface():
    with gr.Interface(fn=chat,
                      inputs=[gr.Textbox(placeholder="Say something...", elem_id="user-input"), gr.State([])],
                      outputs=[gr.Chatbot(), gr.State()],
                      title="Smart AI Assistant",
                      live=True) as demo:
        
        # Adding custom HTML
        demo.add_component(gr.HTML(custom_html), row=0, col=0)

        demo.launch()

launch_gradio_interface()