Create app.py

app.py

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+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+from supervision import Detections
+import cv2
+import gradio as gr
+from PIL import Image
+import numpy as np
+
+model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
+model = YOLO(model_path)
+
+def detect_faces(image):
+    output = model(image)
+    results = Detections.from_ultralytics(output[0])
+
+    im = np.array(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)
+
+    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=1.1, minNeighbors=5, minSize=(5, 5))
+    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)
+
+interface = gr.Interface(
+    fn=detect_faces,
+    inputs="image",
+    outputs=["image","image"],
+    title="Face Detection Deep Learning",
+    description="Upload an image, and the model will detect faces and draw bounding boxes around them.",
+)
+interface.launch()