Update app.py

app.py

@@ -1,31 +1,38 @@
 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):
-    img=np.array(gray_image)
-    # Convert image to grayscale
+def detect_faces(image_np, slider):
+    # Convert image to numpy array
+    img = np.array(image_np)
+
+    # Convert 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 around faces
+    # Draw rectangles on original image
     for (x, y, w, h) in faces:
-        cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-    return img, len(faces)
+    return img, f"Faces detected: {len(faces)}"
 
 # Create Gradio Interface
 iface = gr.Interface(
     fn=detect_faces,
-    inputs=["image",gr.Slider(minimum=1,maximum=2,step=.1,label="Adjust the scale factor.")],
-    outputs=["image",gr.Label( "Face counts")],
+    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")
+    ],
     title="Face Detection",
     description="Upload an image, and the model will detect faces and draw bounding boxes around them."
 )