Update app.py

app.py

@@ -22,8 +22,8 @@ description_markdown = """
 """
 
 description_html = """
-<div style="background-color: #0b0f1a; color: white; padding: 10px; border-radius: 5px; box-shadow: 0 0 10px rgba(11,15,26,1); display: inline-flex; flex-direction: column; justify-content: center; align-items: center; margin: auto;">
-    <ul style="background-color: #1e2936; border-radius: 5px; color: white; padding: 10px; box-shadow: 0 0 10px rgba(0,0,0,0.3); padding-left: 20px; text-align: left; list-style-position: inside;">
+<div style="background-color: #0b0f1a; color: white !important; padding: 10px; border-radius: 5px; box-shadow: 0 0 10px rgba(11,15,26,1); display: inline-flex; flex-direction: column; justify-content: center; align-items: center; margin: auto;">
+    <ul style="background-color: #1e2936; border-radius: 5px; color: white !important; padding: 10px; box-shadow: 0 0 10px rgba(0,0,0,0.3); padding-left: 20px; text-align: left; list-style-position: inside;">
         <li>This tool combines a U-Net Segmentation Model with a ResNet-50 for Classification.</li>
         <li><strong>Usage:</strong> Just drag a pelvic x-ray into the box and hit run.</li>
         <li><strong>Process:</strong> The input image will be segmented and cropped to the SIJ before classification.</li>
@@ -113,7 +113,7 @@ def predict_image(input_image, input_file):
 
 
 
-with gr.Blocks(css=css, title="Anatomy Aware axSpA") as iface:
+with gr.Blocks(title="Anatomy Aware axSpA") as iface:
 
     gr.Markdown("# Anatomy-Aware Image Classification for radiographic axSpA")
     #gr.Markdown(description_markdown)