Update app.py

app.py

@@ -2,21 +2,25 @@ import cv2
 import gradio as gr
 
 def detect_faces(image):
-    # Convert to grayscale for detection
+    # Convert the image to grayscale for better detection
     gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    # Load the Haar cascade for face detection
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    # Detect faces
+    
+    # Load the pre-trained face detection model from OpenCV
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+    
+    # Detect faces in the image
     faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
-    # Draw rectangles around each face
+    
+    # Draw rectangles around the faces
     for (x, y, w, h) in faces:
-        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 3)
-    # Return image and the count of faces
-    return image, f"Number of faces detected: {len(faces)}"
+        cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
+    
+    # Return the modified image and the number of faces detected
+    return image, len(faces)
 
 iface = gr.Interface(fn=detect_faces,
-                     inputs=gr.components.Image(tool="editor", type="pil"),
-                     outputs=[gr.components.Image(type="pil"), gr.components.Textbox()],
-                     title="Face Detection App",
-                     description="This app detects faces in real-time. Upload or capture an image using your webcam.")
+                     inputs=gr.components.Image(source="webcam", type="pil"),
+                     outputs=[gr.components.Image(type="pil"), gr.components.Label()],
+                     title="Face Detection",
+                     description="This app detects faces in real-time from your webcam.")
 iface.launch()