Update app.py

app.py

@@ -1,30 +1,15 @@
-import gradio as gr
-import numpy as np 
+import numpy as np
 import cv2
+import gradio as gr
 from PIL import Image
-
-def detect_faces(image , slider ) : 
-    # detect faces 
-    # convert image in to numpy array
-    image_np = np.array(image)
-    # convert image into gray 
-    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=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","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.",
-    )
-
+def detect_faces(image):
+ image_np= np.array(image)
+ gray_image= cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+ face_cascade= cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+ faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.74, minNeighbors=5, minSize=(30, 30))
+ for (x, y, w, h) in faces:
+     cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 9)
+ return image_np
+
+iface= gr.Interface(fn=detect_faces,inputs="image",outputs="image",title="Face Detection",description="Upload an image, and the model will detect faces and draw bounding boxes around them.",)
 iface.launch()