import streamlit as st
from PIL import Image
import numpy as np
import cv2
import os
import zipfile
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import urllib.request

# --- Download files if not already present ---
def download_file(url, dest):
    if not os.path.exists(dest):
        urllib.request.urlretrieve(url, dest)

# Replace with actual file URLs
download_file("https://your-server.com/face_detector.zip", "face_detector.zip")
download_file("https://your-server.com/css.zip", "css.zip")
download_file("https://your-server.com/images.zip", "images.zip")
download_file("https://your-server.com/mask_detector.h5", "mask_detector.h5")

# --- Extract zip files only once ---
def extract_zip_once(zip_path, extract_to):
    if not os.path.exists(extract_to):
        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
            zip_ref.extractall(extract_to)

# Extract required folders
extract_zip_once("face_detector.zip", "face_detector")
extract_zip_once("css.zip", "css")
extract_zip_once("images.zip", "images")

# --- Streamlit Config ---
st.set_page_config(page_title='Face Mask Detector', page_icon='😷', layout='centered', initial_sidebar_state='expanded')

# --- Local CSS ---
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

# --- Mask Detection on Image ---
def mask_image():
    global RGB_img

    # Load face detector model
    prototxtPath = os.path.join("face_detector", "deploy.prototxt")
    weightsPath = os.path.join("face_detector", "res10_300x300_ssd_iter_140000.caffemodel")
    net = cv2.dnn.readNet(prototxtPath, weightsPath)

    # Load face mask detector model
    model = load_model("mask_detector.h5")

    # Read image
    image_path = os.path.join("images", "out.jpg")
    image = cv2.imread(image_path)
    (h, w) = image.shape[:2]

    # Detect faces
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
    net.setInput(blob)
    detections = net.forward()

    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > 0.5:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            face = image[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            face = cv2.resize(face, (224, 224))
            face = img_to_array(face)
            face = preprocess_input(face)
            face = np.expand_dims(face, axis=0)

            (mask, withoutMask) = model.predict(face)[0]
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            label = f"{label}: {max(mask, withoutMask) * 100:.2f}%"

            cv2.putText(image, label, (startX, startY - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

    RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# --- Streamlit App ---
def mask_detection():
    local_css(os.path.join("css", "styles.css"))
    st.markdown('<h1 align="center">😷 Face Mask Detection</h1>', unsafe_allow_html=True)
    st.sidebar.markdown("# Mask Detection on?")
    choice = st.sidebar.selectbox("Choose among the given options:", ["Image", "Webcam"])

    if choice == 'Image':
        st.markdown('<h2 align="center">Detection on Image</h2>', unsafe_allow_html=True)
        st.markdown("### Upload your image here ⬇")
        image_file = st.file_uploader("", type=['jpg', 'jpeg', 'png'])

        if image_file is not None:
            img = Image.open(image_file)
            save_path = os.path.join("images", "out.jpg")
            img.save(save_path)
            st.image(image_file, caption='', use_column_width=True)
            st.markdown('<h3 align="center">Image uploaded successfully!</h3>', unsafe_allow_html=True)

            if st.button('Process'):
                mask_image()
                st.image(RGB_img, use_column_width=True)

    elif choice == 'Webcam':
        st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True)
        st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True)

# Run the app
mask_detection()