Update app.py

app.py

@@ -12,7 +12,7 @@ import threading
 # Global status and result dictionaries.
 status = {
     "logs": "",
-    "progress": 0,  # 0 to 100
+    "progress": 0,  # from 0 to 100
     "finished": False
 }
 result = {
@@ -24,7 +24,7 @@ result = {
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"[INFO] Using device: {device}")
 
-# Try to load the RAFT model. If it fails, we fall back to Farneback.
+# Try to load the RAFT model. If it fails, fall back to OpenCV Farneback.
 try:
     print("[INFO] Attempting to load RAFT model from torch.hub...")
     raft_model = torch.hub.load("princeton-vl/RAFT", "raft_small", pretrained=True, trust_repo=True)
@@ -37,7 +37,7 @@ except Exception as e:
     raft_model = None
 
 def append_log(msg):
-    """Helper to append a log message to the global status."""
+    """Append a log message to the global status and print it."""
     global status
     status["logs"] += msg + "\n"
     print(msg)
@@ -45,7 +45,7 @@ def append_log(msg):
 def background_process(video_file, zoom):
     """
     Runs the full processing: generates a motion CSV using RAFT (or Farneback)
-    and then stabilizes the video. Updates the global status and result.
+    and then stabilizes the video. Updates global status and result.
     """
     global status, result
 
@@ -110,7 +110,7 @@ def background_process(video_file, zoom):
                 prev_gray = curr_gray
 
             # Compute median magnitude and angle.
-            mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1], angleInDegrees=True)
+            mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1], angleInDegrees=True)
             median_mag = np.median(mag)
             median_ang = np.median(ang)
             # Compute zoom factor: fraction of pixels moving away from the center.
@@ -203,7 +203,7 @@ def start_processing(video_file, zoom):
     """Starts background processing in a new thread."""
     thread = threading.Thread(target=background_process, args=(video_file, zoom), daemon=True)
     thread.start()
-    return "Processing started."
+    return "[INFO] Processing started..."
 
 def poll_status():
     """
@@ -218,7 +218,7 @@ def poll_status():
 # Build the Gradio UI.
 with gr.Blocks() as demo:
     gr.Markdown("# AI-Powered Video Stabilization")
-    gr.Markdown("Upload a video and select a zoom factor. Click **Process Video** to start processing in the background. Then click **Refresh Status** to update the logs and progress (once processing finishes, the stabilized video will be shown).")
+    gr.Markdown("Upload a video and select a zoom factor. Processing starts automatically and progress/logs update every 2 seconds.")
 
     with gr.Row():
         with gr.Column():
@@ -230,11 +230,11 @@ with gr.Blocks() as demo:
             stabilized_video = gr.Video(label="Stabilized Video")
             logs_output = gr.Textbox(label="Logs", lines=15)
             progress_bar = gr.Slider(label="Progress", minimum=0, maximum=100, value=0, interactive=False)
-            refresh_button = gr.Button("Refresh Status")
 
-    # When "Process Video" is clicked, start processing.
+    # When "Process Video" is clicked, start processing in the background.
     start_button.click(fn=start_processing, inputs=[video_input, zoom_slider], outputs=[logs_output])
-    # When "Refresh Status" is clicked, update logs, progress, and videos.
-    refresh_button.click(fn=poll_status, inputs=[], outputs=[original_video, stabilized_video, logs_output, progress_bar])
+
+    # Automatically poll status every 2 seconds.
+    auto_poll = gr.Autoupdate(interval=2000, function=poll_status, outputs=[original_video, stabilized_video, logs_output, progress_bar])
 
 demo.launch()