Add project description and temporary slider to Gradio interface

- Introduce OpenSight project description in an accordion
- Add a temporary slider for demonstration purposes
- Modify result tile to display "SOON" instead of real/fake status
- Remove top margin from main container for tighter layout

app.py

@@ -243,7 +243,7 @@ def generate_results_html(results):
 
     <link href="https://unpkg.com/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
     
-    <div class="container mx-auto mt-4">
+    <div class="container mx-auto">
         <div class="grid xl:grid-cols-5 md:grid-cols-5 grid-cols-1 gap-1">
             <!-- Tile 1: SwinV2/detect -->
             <div
@@ -429,7 +429,7 @@ def generate_results_html(results):
                         <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="3" stroke="currentColor" class="w-4 h-4 mr-2 -ml-3 group-hover:animate group-hover:animate-pulse">
                             {'<path stroke-linecap="round" stroke-linejoin="round" d="M9 12.75 11.25 15 15 9.75M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" />' if results[4][-1] == 'REAL' else '<path stroke-linecap="round" stroke-linejoin="round" d="m9.75 9.75 4.5 4.5m0-4.5-4.5 4.5M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" />'}
                         </svg>
-                        <p class="whitespace-nowrap text-lg leading-normal font-bold text-center self-center align-middle py-px">{results[4][-1]}</p>
+                        <p class="whitespace-nowrap text-lg leading-normal font-bold text-center self-center align-middle py-px">SOON</p>
                     </span>
                 </div>
                 <div>
@@ -481,6 +481,15 @@ with gr.Blocks() as iface:
             confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
             inputs = [image_input, confidence_slider]
         with gr.Column(scale=2):
+            with gr.Accordion("Project OpenSight", open=True):
+                gr.Markdown("## Open source SOTA image moderation and detection tool.")
+                temp_slider = gr.Slider(
+                    0, 1,
+                    value=0.1,
+                    step=0.1,
+                    interactive=True,
+                    label="Slide me",
+                )
             image_output = gr.Image(label="Processed Image", visible=False)
             # Custom HTML component to display results in 5 columns
             results_html = gr.HTML(label="Model Predictions")