app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

model_name = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def predict(message, history):
    # Build conversation context
    chat_history = ""
    for human, ai in history:
        chat_history += f"User: {human}\nBot: {ai}\n"
    chat_history += f"User: {message}\nBot:"

    inputs = tokenizer.encode(chat_history, return_tensors="pt")
    outputs = model.generate(
        inputs,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_p=0.9,
        top_k=50
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    bot_reply = response.split("Bot:")[-1].strip()
    return bot_reply

# Use only universally supported args
gr.ChatInterface(
    fn=predict,
    title="💬 My Chatbot",
    description="A simple CPU-friendly chatbot using Qwen/Qwen3-0.6B.",
    examples=["Hello!", "What's your name?", "Tell me a fun fact."],
).launch()