gitglubber/SliderSpace

1import gradio as gr2import spaces3from transformers import AutoModelForCausalLM, AutoTokenizer4 5model_name = "gitglubber/Slider"6 7# Load the tokenizer and the model8tokenizer = AutoTokenizer.from_pretrained(model_name)9model = AutoModelForCausalLM.from_pretrained(10    model_name,11    torch_dtype="auto",12    device_map="auto"13)14 15@spaces.GPU(duration=120)16def generate_response(prompt):17    # Prepare the model input18    messages = [19        {"role": "user", "content": prompt}20    ]21    text = tokenizer.apply_chat_template(22        messages,23        tokenize=False,24        add_generation_prompt=True,25    )26    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)27 28    # Conduct text completion29    generated_ids = model.generate(30        **model_inputs,31        max_new_tokens=1024  # Reduced for performance and safety32    )33    output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 34 35    content = tokenizer.decode(output_ids, skip_special_tokens=True)36    return content37 38# Create Gradio interface39with gr.Blocks() as demo:40    gr.Markdown("# Qwen Chatbot")41    chatbot = gr.Chatbot()42    msg = gr.Textbox(label="Input")43    clear = gr.Button("Clear")44 45    def respond(message, chat_history):46        if not message:47            return "", chat_history48        49        bot_response = generate_response(message)50        chat_history.append((message, bot_response))51        return "", chat_history52 53    msg.submit(respond, [msg, chatbot], [msg, chatbot])54    clear.click(lambda: None, None, chatbot, queue=False)55 56# Launch the app57demo.launch()