Update app.py

app.py

@@ -25,19 +25,20 @@ face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_fronta
 with st.sidebar:
     st.title("VisionGuard: Mask Monitor")
     st.markdown("""
-    **VisionGuard** is a real-time AI system designed to help enforce health safety by detecting face mask compliance from images or webcam input.
+    **VisionGuard** is a real-time system powered by **Convolutional Neural Networks (CNNs)** to help promote public health by detecting face mask compliance.
 
     **How it works:**
     - Upload or capture an image
-    - The AI detects face(s) and checks for mask presence
-    - Results include confidence and visual feedback
+    - A CNN-based model detects face(s) and checks for mask presence
+    - Results include confidence levels and visual feedback
 
-    This tool was built with a vision to promote public health and awareness using AI.
+    This tool was built as a deep learning project to demonstrate practical applications of CNNs in image classification tasks.
 
     **Built by:** Thirupathirao • 2025
     """)
     st.info("For best results, use clear, front-facing images with good lighting.")
-    st.caption("Empowering safety through AI.")
+    st.caption("Empowering safety through deep learning.")
+
 
 # Resize image
 def resize_image(image, max_size=(400, 400)):
@@ -125,8 +126,9 @@ elif input_choice == "Use Webcam":
             if confidence is not None:
                 st.metric("Confidence Score", f"{confidence*100:.2f}%")
                 if "Mask" in label:
-                    st.success("✔️ You're following safety measures!")
+                    st.error("No mask detected! Please wear a mask in public spaces.")
                 else:
-                    st.error("⚠️ No mask detected! Please wear a mask in public spaces.")
+                    st.success("You're following safety measures!")
+
             else:
                 st.warning(label)