import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Загрузка токенизатора и модели
model_name = "GoidaAlignment/GOIDA-0.5B"  # Укажите путь к вашей модели
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
    outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Интерфейс Gradio
with gr.Blocks() as demo:
    gr.Markdown("# Введите запрос, и модель ответит.")
    
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Ваш запрос", lines=4, placeholder="Введите текст")
        with gr.Column():
            output = gr.Textbox(label="Ответ модели", lines=6, interactive=False)
    
    submit_button = gr.Button("Сгенерировать")
    submit_button.click(generate_response, inputs=prompt_input, outputs=output)

# Запуск приложения
if __name__ == "__main__":
    demo.launch()