Update app.py

app.py

@@ -2,7 +2,6 @@ import cv2
 import torch
 import gradio as gr
 import numpy as np
-import matplotlib.pyplot as plt
 from ultralytics import YOLO
 
 # Load YOLOv8 model
@@ -30,7 +29,6 @@ def process_video(video):
 
     # Track detected objects by their bounding box coordinates
     detected_boxes = set()
-    total_detections = 0
 
     while True:
         # Read a frame from the video
@@ -58,7 +56,6 @@ def process_video(video):
                 if detection_box not in detected_boxes:
                     # Add the box to the set to avoid repeating the detection
                     detected_boxes.add(detection_box)
-                    total_detections += 1
 
                     # Annotate the frame with bounding boxes
                     annotated_frame = results[0].plot()  # Plot the frame with bounding boxes
@@ -68,26 +65,24 @@ def process_video(video):
 
                     # Add this frame to the list of frames with detections
                     frames_with_detections.append(annotated_frame_rgb)
-                    detection_counts.append(total_detections)
+
+                    # Yield the latest frame immediately for Gradio's real-time display
+                    yield annotated_frame_rgb
 
     # Release resources
     input_video.release()
 
-    # Return the frames with detections for display
-    return frames_with_detections
-
 # Create a Gradio Blocks interface
 with gr.Blocks() as demo:
     # Define a file input for video upload
     video_input = gr.Video(label="Upload Video")
     
-    # Define the output area to show processed frames
+    # Define the output area to show processed frames (gallery for continuous update)
     gallery_output = gr.Gallery(label="Detection Album", show_label=True, columns=3)  # Display images in a row (album)
 
     # Define the function to update frames in the album
     def update_gallery(video):
-        detected_frames = process_video(video)
-        return detected_frames  # Return all frames with detections
+        return process_video(video)  # Return frames one by one as they are detected
 
     # Connect the video input to the gallery update
     video_input.change(update_gallery, inputs=video_input, outputs=gallery_output)