Update app.py

app.py

@@ -1,4 +1,45 @@
 import streamlit as st
+import torch
+import torchvision.transforms as T
+from PIL import Image
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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])