Update app.py

app.py

@@ -1,34 +1,13 @@
 import gradio as gr
-import cv2
 
-# Load the pre-trained Haar Cascade classifier for face detection
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+def snap(image, video):
+    return [image, video]
 
-def detect_faces(image):
-    # Convert RGB image to OpenCV BGR format
-    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    
-    # Convert to grayscale for face detection
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    # Perform face detection
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-    # Draw rectangles around detected faces
-    for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
-
-    # Convert back to RGB for display
-    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-# Use gr.Video with live=True for live webcam feed
-webcam_interface = gr.Interface(
-    fn=detect_faces,
-    inputs=gr.Video(streaming=True, live=True),  # Use `live=True` instead of `source="webcam"`
-    outputs="image",
-    title="Live Webcam Face Detection",
-    description="Displays the live feed from your webcam and detects faces in real-time."
+demo = gr.Interface(
+    snap,
+    [gr.Image(sources=["webcam"]), gr.Video(sources=["webcam"])],
+    ["image", "video"],
 )
 
-# Launch the Gradio app
-webcam_interface.launch()
+if __name__ == "__main__":
+    demo.launch()