write up small description of the task

app.py

@@ -23,18 +23,6 @@ EXAMPLE_IMAGES = [
     "./examples/f416de7491a31951f79b3cee75b002f4d1bf0162.jpg",
 ]
 
-TRUE_LABELS = {
-    "./examples/0b820b71670c039dd0a51333d1c919f471a9e940.jpg": 1,
-    "./examples/7f6ccae485af121e0b6ee733022e226ee6b0c65f.jpg": 1,
-    "./examples/8eaaa7a400aa79d36c2440a4aa101cc14256cda4.jpg": 0,
-    "./examples/94fa32b29cc1c00403176c0795fffa3cfaa0f20e.jpg": 1,
-    "./examples/730431efa2f79927156dcc4382819e9a6cc2c5bb.jpg": 0,
-    "./examples/a1c001f6b242c72d3066f15ac6eb059ea72d30ba.jpg": 0,
-    "./examples/a106469bbfda4cdc5a9da7ac0152927bf1b4a92d.jpg": 0,
-    "./examples/c3d660212bf2a11c994e0eadff13770a9927b731.jpg": 1,
-    "./examples/f416de7491a31951f79b3cee75b002f4d1bf0162.jpg": 0,
-}
-
 
 def display_image(image):
     return image
@@ -137,6 +125,23 @@ if __name__ == "__main__":
             </p>       
             """
         )
+        gr.Markdown("""
+            ## 🔒 Privacy-Preserving Cancer Detection with Fully Homomorphic Encryption (FHE)
+
+            This demo lets you upload a microscopic tissue image and get a “Cancerous” or “Not Cancerous” verdict—**without ever revealing your raw data**.
+            Under the hood, we run the deep learning classifier from your image, **encrypt** it with fully homomorphic encryption (FHE), and run the classification entirely on ciphertext.
+            That means all computation happens on encrypted values: the server never sees your clear-text image or features, yet still returns an accurate diagnosis in milliseconds.  
+                    
+            ### Steps
+
+            1. **Generate** your public/private keys
+            2. **Upload or select** a reference cell image  
+            3. **Generate & encrypt** its embedding 
+            4. **Set a decision threshold** and click “Check”  
+            5. **See the verdict** (“Cancerous” vs. “Not Cancerous”)
+
+            Experience cutting-edge FHE in action: robust cancer screening with patient privacy built in.
+        """)
         with gr.Row():
             gr.Markdown("## Setup Phase: 🔐 Generate the FHE public and secret keys.")
         with gr.Row():