app.py

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)