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)}")