Update app.py

app.py

@@ -1,19 +1,21 @@
 import gradio as gr
 import cv2
-import numpy as np
-from ultralytics import YOLO
 import tempfile
+from ultralytics import YOLO
 
 # Load YOLOv8 model
 model = YOLO("yolov8n.pt")
 
 # Function for object detection + tracking
 def detect_and_track(video_file):
+    if video_file is None:
+        return None
+
     cap = cv2.VideoCapture(video_file.name)
     output_path = tempfile.mktemp(suffix=".mp4")
 
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    fps = cap.get(cv2.CAP_PROP_FPS)
+    fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 
@@ -30,16 +32,20 @@ def detect_and_track(video_file):
 
     cap.release()
     out.release()
-    return output_path
+    return output_path  # must return path for gr.Video
 
 # Gradio UI
-demo = gr.Interface(
-    fn=detect_and_track,
-    inputs=gr.File(label="Upload a Video", file_types=[".mp4", ".avi", ".mov"]),
-    outputs=gr.Video(label="Tracked Output"),
-    title="FullTrack AI - Object Tracking",
-    description="Upload a video to run YOLOv8 tracking with trajectory predictions."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎯 FullTrack AI - Object Tracking\nUpload a video to run YOLOv8 tracking with trajectory predictions.")
+
+    with gr.Row():
+        with gr.Column():
+            video_in = gr.File(label="Upload a Video", file_types=[".mp4", ".avi", ".mov"])
+            btn = gr.Button("Submit", variant="primary")
+        with gr.Column():
+            video_out = gr.Video(label="Tracked Output")
+
+    btn.click(detect_and_track, inputs=video_in, outputs=video_out)
 
 if __name__ == "__main__":
     demo.launch()