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# import streamlit as st
# import torch
# import torchvision.transforms as T
# from PIL import Image
#
# # Assuming the necessary packages (featup, clip, etc.) are installed and accessible
# from featup.util import norm, unnorm
# from featup.plotting import plot_feats
#
# # Setup - ensure the repository content is accessible in the environment
#
# # Streamlit UI
# st.title("Feature Upsampling Demo")
#
# # File uploader
# uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
# if uploaded_file is not None:
# image = Image.open(uploaded_file).convert("RGB")
#
# # Image preprocessing
# input_size = 224
# transform = T.Compose([
# T.Resize(input_size),
# T.CenterCrop((input_size, input_size)),
# T.ToTensor(),
# norm
# ])
#
# image_tensor = transform(image).unsqueeze(0) # Assuming CUDA is available, .cuda()
#
# # Model selection
# model_option = st.selectbox(
# 'Choose a model for feature upsampling',
# ('dino16', 'dinov2', 'clip', 'resnet50')
# )
#
# if st.button('Upsample Features'):
# # Load the selected model
# upsampler = torch.hub.load("mhamilton723/FeatUp", model_option).cuda()
# hr_feats = upsampler(image_tensor)
# lr_feats = upsampler.model(image_tensor)
#
# # Plotting - adjust the plot_feats function or find an alternative to display images in Streamlit
# # This step will likely need customization to display within Streamlit's interface
# plot_feats(unnorm(image_tensor)[0], lr_feats[0], hr_feats[0])
import streamlit as st
import torch
def check_gpu_status():
# Check if CUDA (GPU support) is available in PyTorch
cuda_available = torch.cuda.is_available()
gpu_count = torch.cuda.device_count()
gpu_name = torch.cuda.get_device_name(0) if cuda_available else "Not Available"
return cuda_available, gpu_count, gpu_name
# Streamlit page configuration
st.title("PyTorch GPU Availability Test")
# Checking the GPU status
cuda_available, gpu_count, gpu_name = check_gpu_status()
# Displaying the results
if cuda_available:
st.success(f"GPU is available! 🎉")
st.info(f"Number of GPUs available: {gpu_count}")
st.info(f"GPU Name: {gpu_name}")
else:
st.error("GPU is not available. 😢")
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