Update app.py

app.py

@@ -1,8 +1,42 @@
 import gradio as gr
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+from supervision import Detections
+from PIL import Image
+import numpy as np
+import cv2
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Download and load the model
+model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
+model = YOLO(model_path)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text", live=True)
+# Define prediction function
+def detect_faces(image):
+    # Run inference
+    output = model(image)
+    detections = Detections.from_ultralytics(output[0])
+
+    # Convert PIL image to OpenCV format
+    image_np = np.array(image)
+    image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+
+    # Draw bounding boxes
+    for box in detections.xyxy:
+        x1, y1, x2, y2 = map(int, box)
+        cv2.rectangle(image_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+    # Convert back to PIL image
+    result_image = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
+    return result_image
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=detect_faces,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Image(type="pil", label="Detected Faces"),
+    title="Face Detection with YOLOv8",
+    description="Drag and drop an image or click to upload. The model will detect faces using YOLOv8.",
+    live=False
+)
 
 demo.launch()