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Update app.py

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import numpy as np
import cv2
import streamlit as st
import insightface
from insightface.app import FaceAnalysis
from PIL import Image
from nudenet import NudeDetector

# Initialize the FaceAnalysis model
app = FaceAnalysis(name='buffalo_l')
app.prepare(ctx_id=0, det_size=(640, 640))  # Ensure the model runs on the CPU if there's limited GPU

# Load the face swapping model (downloading on-demand)
swapper = insightface.model_zoo.get_model('insightface/inswapper_128.onnx', download=True)

# Initialize the NudeDetector model for nudity detection
detector = NudeDetector()

# Function to detect adult content in images
def check_for_adult_content(image):
    # Convert PIL image to a numpy array
    img = np.array(image)

    # Use NudeDetector to predict nudity
    results = detector.detect(img)  # Returns a list of dictionaries

    # Check if any of the results contain explicit labels
    explicit_labels = [
        'EXPOSED_ANUS',
        'EXPOSED_BREAST_F',
        'EXPOSED_GENITALIA_F',
        'EXPOSED_GENITALIA_M'
    ]

    # Iterate through results and check for explicit content
    for item in results:
        if 'label' in item and item['label'] in explicit_labels:
            return True  # Detected explicit content

    return False  # No explicit content detected

# Face swapping function with added sharpening
def swap_faces(destination_image, source_image):
    # Load the destination and source images from Streamlit inputs
    img = cv2.cvtColor(np.array(destination_image), cv2.COLOR_RGB2BGR)
    test = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)

    # Detect faces in the destination and source images
    faces = app.get(img)
    test_faces = app.get(test)

    if not faces or not test_faces:
        return "No faces detected in one or both images."

    test_face = test_faces[0]

    # Perform face swapping with error handling
    res = img.copy()
    try:
        for face in faces:
            res = swapper.get(res, face, test_face, paste_back=True)
    except MemoryError:
        return "Memory error: Face swapping operation failed due to memory overload."

    # Apply sharpening for a clearer image
    kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
                       [0, -1, 0]])
    sharpened_res = cv2.filter2D(res, -1, kernel)

    # Commented smoothing (blurring) for testing purposes
    smoothed_res = cv2.GaussianBlur(res, (5, 5), 0)

    # Convert the result to RGB for display in Streamlit
    sharpened_res_rgb = cv2.cvtColor(sharpened_res, cv2.COLOR_BGR2RGB)

    return sharpened_res_rgb

# Streamlit app layout
def main():
    st.set_page_config(page_title="SnapSwap", page_icon=":camera:")

    # CSS for styling
    st.markdown("""
        <style>
            .header {
                font-size: 2em;
                font-weight: bold;
                color: #4e73df;
            }
            .subtitle {
                font-size: 1.2em;
                color: #4e73df;
            }
            .content {
                font-size: 1em;
            }
        </style>
    """, unsafe_allow_html=True)

    # Page Header (outside sidebar)
    st.header("SnapSwap :camera:")
    st.write("Welcome to the face swapping app! Upload two images and watch the magic happen!")

    # Sidebar for file uploads and instructions
    with st.sidebar:
        st.subheader("Upload Images for Face Swapping and hit Swap Faces button")
        source_image = st.file_uploader("Upload Source Image", type=["jpg", "png", "jpeg"])
        destination_image = st.file_uploader("Upload Destination Image", type=["jpg", "png", "jpeg"])

        if destination_image and source_image:
            # Check if destination image contains adult content
            destination_img = Image.open(destination_image)
            if check_for_adult_content(destination_img):
                st.error("The destination image does not fall within ethical guidelines.")
            else:
                st.write("Both images uploaded! Click below to swap faces.")
                if st.button("Swap Faces"):
                    with st.spinner("Processing..."):
                        # Read images with PIL (Streamlit works with PIL images)
                        source_img = Image.open(source_image)

                        # Call the face swapping function
                        result = swap_faces(destination_img, source_img)

                        if isinstance(result, str):
                            st.error(result)  # Display error message if no faces detected or memory error
                        else:
                            st.session_state.result = result  # Save the result in session state
                    st.success("Face swapping complete!")

        # Instructions on how to use the app (below upload buttons and swap button)
        st.subheader("How to Use the App:")
        st.markdown("""
            1. **Upload the destination and source images**: Select the images you want to use for face swapping.
            2. **Click on the \"Swap Faces\" button**: The app will process the images and swap the faces.
            3. **View the output**: The swapped face image will appear after processing.
        """)

    # Main content area for displaying the swapped face image
    if 'result' in st.session_state:
        st.image(st.session_state.result, caption="Swapped Face", use_container_width=True)

    # Footer with credits
    st.markdown("Developed with ❤ by Aklavya")

# Run the app
if __name__ == '__main__':
    main()

	import numpy as np
	import cv2
	import streamlit as st
	import insightface
	from insightface.app import FaceAnalysis
	from PIL import Image
	from nudenet import NudeDetector

	# Initialize the FaceAnalysis model
	app = FaceAnalysis(name='buffalo_l')
	app.prepare(ctx_id=0, det_size=(640, 640)) # Ensure the model runs on the CPU if there's limited GPU

	# Load the face swapping model (downloading on-demand)
	swapper = insightface.model_zoo.get_model('insightface/inswapper_128.onnx', download=True)

	# Initialize the NudeDetector model for nudity detection
	detector = NudeDetector()

	# Function to detect adult content in images
	def check_for_adult_content(image):
	# Convert PIL image to a numpy array
	img = np.array(image)

	# Use NudeDetector to predict nudity
	results = detector.detect(img) # Returns a list of dictionaries

	# Check if any of the results contain explicit labels
	explicit_labels = [
	'EXPOSED_ANUS',
	'EXPOSED_BREAST_F',
	'EXPOSED_GENITALIA_F',
	'EXPOSED_GENITALIA_M'
	]

	# Iterate through results and check for explicit content
	for item in results:
	if 'label' in item and item['label'] in explicit_labels:
	return True # Detected explicit content

	return False # No explicit content detected

	# Face swapping function with added sharpening
	def swap_faces(destination_image, source_image):
	# Load the destination and source images from Streamlit inputs
	img = cv2.cvtColor(np.array(destination_image), cv2.COLOR_RGB2BGR)
	test = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)

	# Detect faces in the destination and source images
	faces = app.get(img)
	test_faces = app.get(test)

	if not faces or not test_faces:
	return "No faces detected in one or both images."

	test_face = test_faces[0]

	# Perform face swapping with error handling
	res = img.copy()
	try:
	for face in faces:
	res = swapper.get(res, face, test_face, paste_back=True)
	except MemoryError:
	return "Memory error: Face swapping operation failed due to memory overload."

	# Apply sharpening for a clearer image
	kernel = np.array([[0, -1, 0],
	[-1, 5, -1],
	[0, -1, 0]])
	sharpened_res = cv2.filter2D(res, -1, kernel)

	# Commented smoothing (blurring) for testing purposes
	smoothed_res = cv2.GaussianBlur(res, (5, 5), 0)

	# Convert the result to RGB for display in Streamlit
	sharpened_res_rgb = cv2.cvtColor(sharpened_res, cv2.COLOR_BGR2RGB)

	return sharpened_res_rgb

	# Streamlit app layout
	def main():
	st.set_page_config(page_title="SnapSwap", page_icon=":camera:")

	# CSS for styling
	st.markdown("""
	<style>
	.header {
	font-size: 2em;
	font-weight: bold;
	color: #4e73df;
	}
	.subtitle {
	font-size: 1.2em;
	color: #4e73df;
	}
	.content {
	font-size: 1em;
	}
	</style>
	""", unsafe_allow_html=True)

	# Page Header (outside sidebar)
	st.header("SnapSwap :camera:")
	st.write("Welcome to the face swapping app! Upload two images and watch the magic happen!")

	# Sidebar for file uploads and instructions
	with st.sidebar:
	st.subheader("Upload Images for Face Swapping and hit Swap Faces button")
	source_image = st.file_uploader("Upload Source Image", type=["jpg", "png", "jpeg"])
	destination_image = st.file_uploader("Upload Destination Image", type=["jpg", "png", "jpeg"])

	if destination_image and source_image:
	# Check if destination image contains adult content
	destination_img = Image.open(destination_image)
	if check_for_adult_content(destination_img):
	st.error("The destination image does not fall within ethical guidelines.")
	else:
	st.write("Both images uploaded! Click below to swap faces.")
	if st.button("Swap Faces"):
	with st.spinner("Processing..."):
	# Read images with PIL (Streamlit works with PIL images)
	source_img = Image.open(source_image)

	# Call the face swapping function
	result = swap_faces(destination_img, source_img)

	if isinstance(result, str):
	st.error(result) # Display error message if no faces detected or memory error
	else:
	st.session_state.result = result # Save the result in session state
	st.success("Face swapping complete!")

	# Instructions on how to use the app (below upload buttons and swap button)
	st.subheader("How to Use the App:")
	st.markdown("""
	1. Upload the destination and source images: Select the images you want to use for face swapping.
	2. Click on the \"Swap Faces\" button: The app will process the images and swap the faces.
	3. View the output: The swapped face image will appear after processing.
	""")

	# Main content area for displaying the swapped face image
	if 'result' in st.session_state:
	st.image(st.session_state.result, caption="Swapped Face", use_container_width=True)

	# Footer with credits
	st.markdown("Developed with ❤ by Aklavya")

	# Run the app
	if __name__ == '__main__':
	main()