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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 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", compile=False)
model = load_model_cached()
# 🔍 Haar Cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# 🧪 Detection Function
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
# 🎨 Custom Styles
st.markdown("""
<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.subheader("📤 Upload a photo")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file:
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)
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)
# --- Webcam Tab ---
with tab2:
st.subheader("📷 Use your cam")
camera_image = st.camera_input("Take a snapshot")
if camera_image:
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)
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)
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