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

model_name = "Qwen/Qwen3-4B-Instruct-2507"

# Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

@spaces.GPU(duration=120)
def generate_response(prompt):
    # Prepare the model input
    messages = [
        {"role": "user", "content": prompt}
    ]
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    # Conduct text completion
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=1024  # Reduced for performance and safety
    )
    output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

    content = tokenizer.decode(output_ids, skip_special_tokens=True)
    return content

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Qwen Chatbot")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Input")
    clear = gr.Button("Clear")

    def respond(message, chat_history):
        if not message:
            return "", chat_history
        
        bot_response = generate_response(message)
        chat_history.append((message, bot_response))
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

# Launch the app
demo.launch()