Update app.py

app.py

@@ -5,30 +5,29 @@ from keras.models import load_model
 from keras.preprocessing.image import img_to_array
 from PIL import Image
 
-# Set page config
-st.set_page_config(page_title="Face Mask Detection", layout="centered")
+# Page settings
+st.set_page_config(page_title="Smart Face Mask Scanner", layout="centered")
 
-# Load model once
+# Load model
 @st.cache_resource
 def load_model_cached():
-    return load_model("Face Detector.keras")  # Make sure this is trained on cropped face images
+    return load_model("Face Detector.keras")
 
 model = load_model_cached()
 
-# Load Haar Cascade for face detection
+# Load Haar Cascade
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# Function to detect face and predict
+# Detection and Prediction
 def detect_and_predict(image_input):
     image_np = np.array(image_input.convert("RGB"))
     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-
     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
 
     if len(faces) == 0:
         return image_input, None, "No face detected"
 
-    x, y, w, h = faces[0]  # Just take the first detected face
+    x, y, w, h = faces[0]
     face_roi = image_np[y:y+h, x:x+w]
     face_pil = Image.fromarray(face_roi).resize((200, 200))
     img_array = img_to_array(face_pil) / 255.0
@@ -38,32 +37,39 @@ def detect_and_predict(image_input):
     confidence = (1 - prediction) if prediction < 0.5 else prediction
     label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
 
-    # Draw rectangle and label
     color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
     cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
-    cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)",(x, y - 10),
-                cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 2)
+    cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)", (x, y - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
     return Image.fromarray(image_np), confidence, label
 
-# App UI
-st.title("😷 Smart Face Mask Detection App")
-st.markdown("Upload a face image or use your webcam to check if a mask is being worn.")
+# App Header
+st.title("😷 Smart Face Mask Detection System")
+st.markdown("""
+### 📢 Keep yourself and others safe!
+This app allows you to **instantly check if a person is wearing a mask** by uploading an image or using your webcam.
+""")
 
-# Tabs
-tab1, tab2 = st.tabs(["📤 Upload Image", "📷 Use Webcam"])
+# Tabs with emphasized titles
+tab1, tab2 = st.tabs([
+    "🖼️ **:blue[Upload Image]**",
+    "📷 **:green[Use Webcam]**"
+])
 
+# Upload Image Tab
 with tab1:
+    st.markdown("#### :blue[Upload a photo to detect mask status]")
     uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
     if uploaded_file:
         try:
             image_input = Image.open(uploaded_file)
-            st.image(image_input, caption="Uploaded Image", use_container_width=True)
+            st.image(image_input, caption="Uploaded Image", width=300)
 
             with st.spinner("Analyzing..."):
                 result_img, confidence, label = detect_and_predict(image_input)
 
-            st.image(result_img, caption="Detection Result", use_container_width=True)
+            st.image(result_img, caption="Detection Result", width=300)
             if confidence is not None:
                 st.metric("Confidence", f"{confidence*100:.2f}%")
                 if "Mask" in label:
@@ -76,17 +82,19 @@ with tab1:
         except Exception as e:
             st.error(f"❌ Error: {str(e)}")
 
+# Webcam Tab
 with tab2:
+    st.markdown("#### :green[Take a picture using webcam to detect mask]")
     camera_image = st.camera_input("Take a picture")
     if camera_image:
         try:
             image_input = Image.open(camera_image)
-            st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
+            st.image(image_input, caption="Webcam Snapshot", width=300)
 
             with st.spinner("Analyzing..."):
                 result_img, confidence, label = detect_and_predict(image_input)
 
-            st.image(result_img, caption="Detection Result", use_container_width=True)
+            st.image(result_img, caption="Detection Result", width=300)
             if confidence is not None:
                 st.metric("Confidence", f"{confidence*100:.2f}%")
                 if "Mask" in label:
@@ -97,4 +105,4 @@ with tab2:
                 st.warning(label)
 
         except Exception as e:
-            st.error(f"❌ Error: {str(e)}")
+            st.error(f"❌ Error: {str(e)}")