app.py

from app.logger_config import logger as logging
import os
import gradio as gr
import spaces
import torch



logging.info("-----------info------------")
logging.debug("-----------debug------------")

@spaces.GPU(progress=gr.Progress(track_tqdm=True))
def greet(name):
    logging.debug("=== Start of greet() ===")

    # Check GPU availability
    has_gpu = torch.cuda.is_available()
    logging.debug(f"GPU available: {has_gpu}")

    if has_gpu:
        device = torch.device("cuda")
        device_name = torch.cuda.get_device_name(0)
        memory_allocated = torch.cuda.memory_allocated(0) / (1024 ** 2)
        memory_reserved = torch.cuda.memory_reserved(0) / (1024 ** 2)
        memory_total = torch.cuda.get_device_properties(0).total_memory / (1024 ** 2)
        capability = torch.cuda.get_device_capability(0)
        current_device = torch.cuda.current_device()

        # Detailed GPU logs
        logging.debug(f"GPU name          : {device_name}")
        logging.debug(f"Current device ID : {current_device}")
        logging.debug(f"CUDA capability   : {capability}")
        logging.debug(f"Memory allocated  : {memory_allocated:.2f} MB")
        logging.debug(f"Memory reserved   : {memory_reserved:.2f} MB")
        logging.debug(f"Total memory      : {memory_total:.2f} MB")
    else:
        device = torch.device("cpu")
        device_name = "CPU (no GPU detected)"
        logging.debug("No GPU detected, using CPU")

    # Create tensor
    tensor = torch.tensor([len(name)], dtype=torch.float32, device=device)
    logging.debug(f"Tensor created: {tensor}")

    # Optional GPU cleanup
    if has_gpu:
        torch.cuda.empty_cache()
        logging.debug("GPU cache cleared")

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

demo = gr.Interface(
    fn=greet,
    inputs="text",
    outputs="text",
    title="GPU Info Demo",
    description="Retourne un tenseur et le nom du GPU utilisé"
)

demo.launch()