Update app.py

app.py

@@ -96,15 +96,15 @@ def process_video(input_video, player_stats=True, ball_stats=True):
 # Gradio Interface
 title="Football Match Analytics with YOLO and OpenCV"
 description="""
-    This tool processes football game videos to detect players and referees, track the ball, assign players to teams using color pixel clustering, and compute ball possession. 
+    This demo processes football game videos to detect players and referees, track the ball, assign players to teams using color pixel clustering, and compute ball possession per team. 
     
-    It also estimates camera movement with Optical Flow, applies perspective transformation for scene depth, and calculates real-time speed and total distance traveled by each player and the ball.
+    It also estimates camera movement with Lucas-Kanade optical flow and applies perspective transformation to calculate the real-time speed and total distance traveled by each player and the ball.
 
-    The YOLO model was fine-tuned with https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc
+    The YOLO detection model was fine-tuned with this dataset: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/dataset
 
-    Original Reference: https://www.youtube.com/watch?v=neBZ6huolkg
+    Original Tutorial Reference: https://www.youtube.com/watch?v=neBZ6huolkg
     
-    **Note**: the space is running on CPU, so inferencing new video may take a bit of time. (Avg time during test: 1min processing per 5 second of video)"""
+    **Note**: this space is running on a CPU, so inferencing new video may take some time. (Avg time during test: 1min processing per 5 second of video)"""
 
 examples = [["input_videos/121364_0_small.mp4", True, True]]