Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import gradio as gr
 from huggingface_hub import hf_hub_download
 from ultralytics import YOLO
@@ -5,43 +6,58 @@ from PIL import Image
 import cv2
 import numpy as np
 
+
 # Download the YOLOv8 model for face detection
 model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
 model = YOLO(model_path)
 
+
 def process_video(video_path):
     # Open the video file
     cap = cv2.VideoCapture(video_path)
     unique_faces = set()
 
+
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
 
+
         # Convert the frame to PIL Image
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         pil_image = Image.fromarray(frame)
 
+
         # Detect faces in the frame
         output = model(pil_image)
         faces = output.pred[0]
 
+
         # Iterate over detected faces and add them to the set
         for face in faces:
             face_data = tuple(face.numpy())
             unique_faces.add(face_data)
 
+
     cap.release()
 
+
     return len(unique_faces)
 # Gradio interface
 iface = gr.Interface(
     fn=process_video,
     inputs=gr.inputs.Video(source="upload", label="Upload a Video"),
     outputs="number",
-    title="Unique Face Counter in Video",
-    description="Upload a video to count the total number of unique faces."
+    title="Unique Face Counter in Video",# Create the Gradio interface
+
+
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="Number of People Detected"),
+    title="People Counter",
+    description="Upload a video to count the number of people present."
 )
 
-iface.launch()
+
+if __name__ == "__main__":
+    iface.launch()