Update app.py

app.py

@@ -414,7 +414,7 @@ class VideoTagger:
     
         if progress is not None:
             if is_first_load:
-                progress(0.0, desc="Loading model...")
+                progress(0.0, desc="Loading model (first run may take a while)...")
             else:
                 progress(0.0, desc="Opening video...")
     
@@ -435,13 +435,14 @@ class VideoTagger:
         if total_frames <= 0:
             total_frames = 1
     
+        # Number of frames we will actually process (sampled every N frames)
         frames_to_process = max(1, (total_frames + frame_interval - 1) // frame_interval)
     
         aggregated_general: Dict[str, float] = {}
         aggregated_character: Dict[str, float] = {}
     
-        frame_idx = 0
-        processed_frames = 0
+        frame_idx = 0                 # index over all video frames
+        processed_frames = 0          # count of sampled frames fully processed by the model
         batch_tensors: List[np.ndarray] = []
     
         try:
@@ -450,25 +451,44 @@ class VideoTagger:
                 if not ret:
                     break
     
-                # Only process every N-th frame
                 if frame_idx % frame_interval == 0:
-                    # frame is BGR uint8 from OpenCV
+                    # This is a sampled frame
+                    sampled_index = processed_frames + len(batch_tensors) + 1  # 1-based index among sampled frames
                     arr = self._prepare_frame_bgr(frame)  # (H, W, 3) float32
                     batch_tensors.append(arr)
     
-                    # While building the FIRST batch, keep user informed
-                    if progress is not None and processed_frames == 0:
-                        frames_in_first_batch = min(self.batch_size, frames_to_process)
+                    if progress is not None:
+                        # Show which sampled frame we're preparing, and which raw video frame it is.
+                        ratio = min(
+                            (processed_frames + len(batch_tensors)) / frames_to_process,
+                            0.99,
+                        )
                         progress(
-                            0.0,
+                            ratio,
                             desc=(
-                                f"Collecting frames for first batch "
-                                f"({len(batch_tensors)}/{frames_in_first_batch})..."
+                                f"Preparing sampled frame {sampled_index}/{frames_to_process} "
+                                f"(video frame {frame_idx + 1}/{total_frames})..."
                             ),
                         )
     
-                    # If batch is full, run inference
+                    # If batch is full, run inference on it
                     if len(batch_tensors) >= self.batch_size:
+                        # Inform the user we're now running the model on this batch
+                        if progress is not None:
+                            start_sample = processed_frames + 1
+                            end_sample = processed_frames + len(batch_tensors)
+                            ratio = min(
+                                (processed_frames + len(batch_tensors)) / frames_to_process,
+                                0.99,
+                            )
+                            progress(
+                                ratio,
+                                desc=(
+                                    f"Running model on batch: sampled frames "
+                                    f"{start_sample}-{end_sample}/{frames_to_process}..."
+                                ),
+                            )
+    
                         num_done = self._run_batch_and_aggregate(
                             batch_tensors,
                             general_thresh=general_thresh,
@@ -484,8 +504,8 @@ class VideoTagger:
                             progress(
                                 ratio,
                                 desc=(
-                                    f"Processing frames {processed_frames}/"
-                                    f"{frames_to_process}..."
+                                    f"Finished processing sampled frames "
+                                    f"{processed_frames}/{frames_to_process}..."
                                 ),
                             )
     
@@ -495,6 +515,21 @@ class VideoTagger:
     
         # Process any leftover frames in the last partial batch
         if batch_tensors:
+            if progress is not None:
+                start_sample = processed_frames + 1
+                end_sample = processed_frames + len(batch_tensors)
+                ratio = min(
+                    (processed_frames + len(batch_tensors)) / frames_to_process,
+                    0.99,
+                )
+                progress(
+                    ratio,
+                    desc=(
+                        f"Running model on final batch: sampled frames "
+                        f"{start_sample}-{end_sample}/{frames_to_process}..."
+                    ),
+                )
+    
             num_done = self._run_batch_and_aggregate(
                 batch_tensors,
                 general_thresh=general_thresh,
@@ -509,8 +544,8 @@ class VideoTagger:
                 progress(
                     ratio,
                     desc=(
-                        f"Processing frames {processed_frames}/"
-                        f"{frames_to_process} (final batch)..."
+                        f"Finished processing all sampled frames "
+                        f"{processed_frames}/{frames_to_process}..."
                     ),
                 )
     
@@ -822,7 +857,7 @@ with gr.Blocks(title=TITLE) as demo:
 custom_theme = gr.themes.Default(
     primary_hue=gr.themes.colors.blue,
     secondary_hue=gr.themes.colors.slate,
-    radius_size=gr.themes.sizes.radius_lg,
+    radius_size=gr.themes.sizes.radius_xl,
     font=[gr.themes.GoogleFont("Raleway")],
 )