from app.logger_config import logger as logging
from app.utils import (
    debug_current_device,
    get_current_device
)
import os
import gradio as gr
import spaces
import torch




@spaces.GPU
def gpu_compute(name):
    logging.debug("=== Start of gpu_compute() ===")
    debug_current_device()
    tensor,device_name = compute(name)
    logging.debug("=== End of gpu_compute() ===")
    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"

def cpu_compute(name):
    logging.debug("=== Start of cpu_compute() ===")
    debug_current_device()

    tensor,device_name = compute(name)

    logging.debug("=== End of cpu_compute() ===")
    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"

def compute(name) :
    # Get device info
    device, device_name = get_current_device()
    # Create a tensor
    tensor = torch.tensor([len(name)], dtype=torch.float32, device=device)
    logging.debug(f"Tensor created: {tensor}")
    # Optional: free GPU memory
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        logging.debug("GPU cache cleared")
    return tensor, device_name


block = gr.Blocks()

with block as demo:
     with gr.Row():
         input_text = gr.Text()
         output_text = gr.Text()
     with gr.Row():
        gpu_button = gr.Button("GPU compute")
        cpu_button = gr.Button("CPU compute")

        gpu_button.click(fn=gpu_compute, inputs=[input_text],outputs=[output_text])
        cpu_button.click(fn=cpu_compute, inputs=[input_text],outputs=[output_text])

with gr.Blocks() as demo:
    block.render()

if __name__ == "__main__":
    demo.queue(max_size=10, api_open=False).launch(show_api=False)