Update app.py

app.py

@@ -1,35 +1,100 @@
 import streamlit as st
 import numpy as np
+import cv2
 from keras.models import load_model
-from keras.preprocessing import image
+from keras.preprocessing.image import img_to_array
 from PIL import Image
-import os
 
-# Load the trained model
-model = load_model("Face Detector.keras")
+# Set page config
+st.set_page_config(page_title="Face Mask Detection", layout="centered")
 
-st.title("😷 Face Mask Detection App")
-st.write("Upload an image and check if the person is wearing a mask.")
+# Load model once
+@st.cache_resource
+def load_model_cached():
+    return load_model("Face Detection.keras")  # Make sure this is trained on cropped face images
 
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+model = load_model_cached()
 
-if uploaded_file is not None:
-    # Show uploaded image
-    img = Image.open(uploaded_file)
-    st.image(img, caption="Uploaded Image", width=200)
+# Load Haar Cascade for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-    # Preprocess image
-    img = img.resize((200, 200))
-    img = image.img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = img / 255.0
+# Function to detect face and predict
+def detect_and_predict(image_input):
+    image_np = np.array(image_input.convert("RGB"))
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
 
-    # Predict
-    prediction = model.predict(img)[0][0]
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
 
-    # Result
-    if prediction < 0.5:
-        st.success("✅ Mask is Detected")
-    else:
-        st.error("🚫 Mask is NOT Detected")
+    if len(faces) == 0:
+        return image_input, None, "No face detected"
+
+    x, y, w, h = faces[0]  # Just take the first detected face
+    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"
+
+    # 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)
+
+    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.")
+
+# Tabs
+tab1, tab2 = st.tabs(["📤 Upload Image", "📷 Use Webcam"])
+
+with tab1:
+    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)
+
+            with st.spinner("Analyzing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
+            st.image(result_img, caption="Detection Result", use_container_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)
+
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")
+
+with tab2:
+    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)
+
+            with st.spinner("Analyzing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
+            st.image(result_img, caption="Detection Result", use_container_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)
+
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")