from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# Загрузка модели и токенизатора
model_path = "verge4646/autotrain-qwen-1737303151"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype="auto"
).eval()

# Функция генерации ответа
def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]
    
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    
    messages.append({"role": "user", "content": message})

    # Создание входных данных для модели
    input_ids = tokenizer.apply_chat_template(
        conversation=messages, 
        tokenize=True, 
        add_generation_prompt=True, 
        return_tensors="pt"
    )

    # Генерация ответа
    output_ids = model.generate(
        input_ids.to('cpu'),
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p
    )
    response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
    return response

# Определение интерфейса Gradio
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

if __name__ == "__main__":
    demo.launch()