Update app.py

app.py

@@ -7,24 +7,24 @@ from PIL import Image
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 # slider=gr.Slider(minimum=1,maximum=2,step=.1,label="Adjust the scale factor.")
 # Face Detection Function
-def detect_faces(image_np,slider):
+def detect_faces(image_np):
     img=np.array(gray_image)
     # Convert image to grayscale
     gray_image = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
 
     # Detect faces
-    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=slider, minNeighbors=5, minSize=(30, 30))
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
 
     # Draw rectangles around faces
     for (x, y, w, h) in faces:
         cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-    return img, len(faces)
+    return img,
 
 # Create Gradio Interface
 iface = gr.Interface(
     fn=detect_faces,
-    inputs=["image",gr.Slider(minimum=1,maximum=2,step=.1,label="Adjust the scale factor.")],
+    inputs="image",
     outputs="image",
     title="Face Detection",
     description="Upload an image, and the model will detect faces and draw bounding boxes around them."