Update app.py

app.py

@@ -5,7 +5,9 @@ import cv2
 import gradio as gr
 from PIL import Image
 import numpy as np
+from transformers import pipeline
 
+pipe = pipeline("image-classification", model="NTQAI/pedestrian_gender_recognition")
 model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
 model = YOLO(model_path)
 
@@ -17,6 +19,8 @@ def detect_faces(image):
     for i in results:
         im = cv2.rectangle(im, (int(i[0][0]),int(i[0][1])), (int(i[0][2]),int(i[0][3])), (0,0,255), 2)
 
+    label_out = pipe(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")
@@ -24,12 +28,12 @@ def detect_faces(image):
     for (x, y, w, h) in faces:
         cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
     
-    return (image_np,im)
+    return (image_np,im,label_out[0]['label'])
 
 interface = gr.Interface(
     fn=detect_faces,
     inputs=gr.Image(label='Upload Image'),
-    outputs=[gr.Image(label='Original'),gr.Image(label='Deep learning')],
+    outputs=[gr.Image(label='Original'),gr.Image(label='Deep learning'),'text'],
     title="Face Detection Deep Learning",
     description="Upload an image, and the model will detect faces and draw bounding boxes around them.",
 )