Update app.py

app.py

@@ -1,108 +1,108 @@
-import streamlit as st
-import numpy as np
-import cv2
-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
-@st.cache_resource
-def load_model_cached():
-    return load_model("Face Detector.keras")
-
-model = load_model_cached()
-
-# Load Haar Cascade
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-# 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]
-    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
-    img_array = np.expand_dims(img_array, axis=0)
-
-    prediction = model.predict(img_array)[0][0]
-    confidence = (1 - prediction) if prediction < 0.5 else prediction
-    label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
-
-    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, 0.5, color, 2)
-
-    return Image.fromarray(image_np), confidence, label
-
-# App Header
-st.markdown("<h2>😷 Smart Face Mask Detection App</h2>", unsafe_allow_html=True)
-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
-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", 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)
-
-        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", 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)
-
-        except Exception as e:
-            st.error(f"❌ Error: {str(e)}")
-
+import streamlit as st
+import numpy as np
+import cv2
+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
+@st.cache_resource
+def load_model_cached():
+    return load_model("Face%20Detector.keras")
+
+model = load_model_cached()
+
+# Load Haar Cascade
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# 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]
+    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
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)[0][0]
+    confidence = (1 - prediction) if prediction < 0.5 else prediction
+    label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
+
+    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, 0.5, color, 2)
+
+    return Image.fromarray(image_np), confidence, label
+
+# App Header
+st.markdown("<h2>😷 Smart Face Mask Detection App</h2>", unsafe_allow_html=True)
+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
+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", 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)
+
+        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", 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)
+
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")
+