import os
import cv2
import streamlit as st
from PIL import Image

def load_cascade():
    # Specify the path to the Haar Cascade XML file
    cascade_path = 'haarcascade_frontalface_default.xml'

    # Check if the Haar Cascade file exists
    if not os.path.exists(cascade_path):
        raise Exception(f"Haar Cascade file not found at {cascade_path}. Please upload the file.")
    
    # Load the Haar Cascade classifier
    face_cascade = cv2.CascadeClassifier(cascade_path)
    
    # Check if the file was successfully loaded
    if face_cascade.empty():
        raise Exception(f"Failed to load Haar Cascade file from {cascade_path}.")
    
    return face_cascade

def main():
    st.title("Face Detection App")

    # Load Haar Cascade for face detection
    face_cascade = load_cascade()

    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])

    if uploaded_file is not None:
        # Read the image file
        image = Image.open(uploaded_file)
        image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # Detect faces in the image
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

        # Draw rectangles around faces
        for (x, y, w, h) in faces:
            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # Convert image back to RGB and display it
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        st.image(image, caption="Processed Image", use_column_width=True)

        if len(faces) > 0:
            st.success("Face detection successful!")
        else:
            st.warning("No faces detected.")

if __name__ == "__main__":
    main()