Update app.py

app.py

@@ -1,40 +1,34 @@
+You said:
 import numpy as np
 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, slider):
-    # Convert image to numpy array
-    img = np.array(image_np)
-
-    # Convert to grayscale
+def detect_faces(image_np,slider):
+    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))
 
-    # Draw rectangles on original image
+    # Draw rectangles around faces
     for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-    return img, f"Faces detected: {len(faces)}"
+    return img, len(faces)
 
 # Create Gradio Interface
 iface = gr.Interface(
     fn=detect_faces,
-    inputs=[
-        gr.Image(type="numpy", label="Upload Image"),
-        gr.Slider(minimum=1.1, maximum=2.0, step=0.1, label="Adjust the scale factor.")
-    ],
-    outputs=[
-        gr.Image(label="Detected Faces"),
-        gr.Label(label="Face Count")
-    ],
+    inputs=["image",gr.Slider(minimum=1,maximum=2,step=.1,label="Adjust the scale factor.")],
+    outputs=["image",gr.Label( "Face counts")],
     title="Face Detection",
     description="Upload an image, and the model will detect faces and draw bounding boxes around them."
 )
 
-iface.launch()
+iface.launch()