app.py

# app.py
import streamlit as st
try:
    import cv2
    st.write("OpenCV successfully imported!")
except ImportError as e:
    st.error(f"Error importing OpenCV: {e}")

import streamlit as st
import cv2
import numpy as np
from PIL import Image

# Load Haar Cascade
@st.cache_resource
def load_cascade():
    cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    if cascade.empty():
        raise Exception("Haar Cascade file not loaded!")
    return cascade

# Detect faces
def detect_faces(image, face_cascade):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
    return image, len(faces)

# Streamlit app
def main():
    st.title("Real-Time Face Detection App")
    st.write("Upload an image, and the app will detect faces!")

    # Load Haar Cascade
    face_cascade = load_cascade()

    # File uploader
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
    if uploaded_file is not None:
        # Read the image
        image = np.array(Image.open(uploaded_file))
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        # Detect faces
        result_image, face_count = detect_faces(image, face_cascade)

        # Convert BGR to RGB for display
        result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)

        # Display the image
        st.image(result_image, caption=f"Detected {face_count} face(s).", use_column_width=True)

if __name__ == "__main__":
    main()