Update app.py

app.py

@@ -3,7 +3,7 @@ import numpy as np
 import cv2
 from PIL import Image
 
-def detect_faces(image ) : 
+def detect_faces(image , slider ) : 
     # detect faces 
     # convert image in to numpy array
     image_np = np.array(image)
@@ -11,15 +11,17 @@ def detect_faces(image ) :
     gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
     # use detectmultiscale function to detect faces using haar cascade 
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +     "haarcascade_frontalface_default.xml")
-    faces = face_cascade.detectMultiScale(gray_image,   scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+    faces = face_cascade.detectMultiScale(gray_image,   scaleFactor=slider, minNeighbors=5, minSize=(30, 30))
     # draw rectangle along detected faces
     for (x, y, w, h) in faces:
         cv2.rectangle(image_np, (x, y), (x+w, y+h), (255, 0, 0), 5)        
     
     return image_np 
 
+slider = gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")
+
 iface = gr.Interface(  fn=detect_faces,
-    inputs="image",
+    inputs=["image","slider"],
     outputs="image",
     title="Face Detection using Haar Cascade Classifier ",
     description="Upload an image,and the model will detect faces and draw bounding boxes around them.",