import gradio as gr

# Função para carregar o modelo e executar inferência com parâmetros personalizados
def predict(input_text, temperature, max_length, top_p, top_k):
    # Carregue o modelo
    model = gr.load("models/meta-llama/Llama-3.3-70B-Instruct")
    
    # Use os parâmetros passados para configurar o modelo
    return model(input_text, temperature=temperature, max_length=max_length, top_p=top_p, top_k=top_k)

# Interface Gradio
with gr.Blocks() as demo:
    gr.Markdown("# Modelo Llama 3.3 - Ajuste de Parâmetros")
    
    with gr.Row():
        input_text = gr.Textbox(label="Texto de entrada", placeholder="Digite seu texto aqui")
    
    with gr.Row():
        temperature = gr.Slider(0, 1, value=0.7, label="Temperature")
        max_length = gr.Slider(1, 2048, value=512, step=1, label="Comprimento Máximo")
        top_p = gr.Slider(0, 1, value=0.9, label="Top-p (nucleus sampling)")
        top_k = gr.Slider(1, 100, value=50, step=1, label="Top-k")

    with gr.Row():
        output = gr.Textbox(label="Saída do Modelo")
    
    with gr.Row():
        submit_btn = gr.Button("Executar")

    submit_btn.click(
        predict,
        inputs=[input_text, temperature, max_length, top_p, top_k],
        outputs=output,
    )

demo.launch()