Update app.py

app.py

@@ -5,20 +5,24 @@ from keras.models import load_model
 from keras.preprocessing.image import img_to_array
 from PIL import Image
 
-# Page settings
-st.set_page_config(page_title="Smart Face Mask Scanner", layout="centered")
-
-# Load model
+# 🌟 Page Config
+st.set_page_config(
+    page_title="Smart Face Mask Scanner 😷",
+    layout="centered",
+    page_icon="😷"
+)
+
+# 🧠 Load model
 @st.cache_resource
 def load_model_cached():
-    return load_model("Face Detector.keras")
+    return load_model("Face_Detector.keras", compile=False)  
 
 model = load_model_cached()
 
-# Load Haar Cascade
+# 🔍 Haar Cascade for face detection
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# Detection and Prediction
+# 🧪 Detection Function
 def detect_and_predict(image_input):
     image_np = np.array(image_input.convert("RGB"))
     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
@@ -44,64 +48,77 @@ def detect_and_predict(image_input):
 
     return Image.fromarray(image_np), confidence, label
 
-# App Header
-st.markdown("<h2>😷 Smart Face Mask Detection App</h2>", unsafe_allow_html=True)
+# 🎨 Custom Styles
 st.markdown("""
-This app allows you to **instantly check if a person is wearing a mask** by uploading an image or using your webcam.
-""")
-
-# Tabs with emphasized titles
-tab1, tab2 = st.tabs([
-    "🖼️ **:blue[Upload Image]**",
-    "📷 **:green[Use Webcam]**"
-])
-
-# Upload Image Tab
+    <style>
+        .main {
+            background-color: #f0f4f8;
+            padding: 1rem;
+            border-radius: 15px;
+        }
+        h2 {
+            text-align: center;
+            color: #2c3e50;
+        }
+        .stTabs [data-baseweb="tab"] {
+            background-color: #e3f2fd;
+            border-radius: 10px;
+            padding: 10px;
+        }
+        .stTabs [aria-selected="true"] {
+            background-color: #1976d2;
+            color: white;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# 📌 App Header
+st.markdown("<h2>🛡️ Smart Face Mask Scanner</h2>", unsafe_allow_html=True)
+st.markdown("<p style='text-align:center;'>Upload an image or use your webcam to check if a person is wearing a face mask.</p>", unsafe_allow_html=True)
+
+# 🔄 Tabs
+tab1, tab2 = st.tabs(["📤 Upload Image", "📷 Use Webcam"])
+
+# --- Upload Image Tab ---
 with tab1:
-    st.markdown("#### :blue[Upload a photo to detect mask status]")
+    st.subheader("📤 Upload a photo")
     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", width=300)
-
-            with st.spinner("Analyzing..."):
-                result_img, confidence, label = detect_and_predict(image_input)
-
-            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:
-                    st.success(label)
-                else:
-                    st.error(label)
-            else:
-                st.warning(label)
+        image_input = Image.open(uploaded_file)
+        st.image(image_input, caption="📷 Uploaded Image", use_column_width=True)
+
+        with st.spinner("🔍 Analyzing..."):
+            result_img, confidence, label = detect_and_predict(image_input)
 
-        except Exception as e:
-            st.error(f"❌ Error: {str(e)}")
+        st.image(result_img, caption="🔍 Detection Result", use_column_width=True)
 
-# Webcam Tab
+        if confidence is not None:
+            st.metric("🧠 Confidence", f"{confidence*100:.2f}%")
+            if "Mask" in label:
+                st.success(label)
+            else:
+                st.error(label)
+        else:
+            st.warning(label)
+
+# --- Webcam Tab ---
 with tab2:
-    st.markdown("#### :green[Take a picture using webcam to detect mask]")
-    camera_image = st.camera_input("Take a picture")
+    st.subheader("📷 Use your cam")
+    camera_image = st.camera_input("Take a snapshot")
     if camera_image:
-        try:
-            image_input = Image.open(camera_image)
-            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", width=300)
-            if confidence is not None:
-                st.metric("Confidence", f"{confidence*100:.2f}%")
-                if "Mask" in label:
-                    st.success(label)
-                else:
-                    st.error(label)
-            else:
-                st.warning(label)
+        image_input = Image.open(camera_image)
+        st.image(image_input, caption="📸 Captured Image", use_column_width=True)
+
+        with st.spinner("🔍 Analyzing..."):
+            result_img, confidence, label = detect_and_predict(image_input)
 
-        except Exception as e:
-            st.error(f"❌ Error: {str(e)}")
+        st.image(result_img, caption="🔍 Detection Result", use_column_width=True)
+
+        if confidence is not None:
+            st.metric("🧠 Confidence", f"{confidence*100:.2f}%")
+            if "Mask" in label:
+                st.success(label)
+            else:
+                st.error(label)
+        else:
+            st.warning(label)