Update app.py

app.py

@@ -3,30 +3,28 @@ import cv2
 import gradio as gr
 from PIL import Image
 
-
 # Load Haar Cascade classifier
 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):
-    img=np.array(gray_image)
+def detect_faces(image_np,slider):
     # Convert image to grayscale
-    gray_image = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
 
     # Detect faces
-    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=slider, 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,
+    return image_np, len(faces)
 
 # Create Gradio Interface
 iface = gr.Interface(
     fn=detect_faces,
-    inputs="image",
-    outputs="image",
+    inputs=["image",gr.Slider(minimum=1,maximum=2,step=.1,label= "adjust the scaleFactor")],
+    outputs=["image",gr.Label("faces count")],
     title="Face Detection",
     description="Upload an image, and the model will detect faces and draw bounding boxes around them."
 )