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app.py

@@ -129,17 +129,26 @@ def apply_csv(frame_json, x_col, y_col, duration_col, image, image_name):
         return gr.update(), gr.update()
     try:
         frame = pd.read_json(frame_json, orient="split")
-        id_col = next((column for column in frame.columns if str(column).strip().lower() == "id"), None)
-        if id_col is not None:
-            if not image_name:
-                raise ValueError("The uploaded image name is unavailable for matching the CSV id column.")
-            image_path = Path(image_name)
-            accepted_ids = {image_path.name, image_path.stem}
-            frame = frame[frame[id_col].astype(str).str.strip().isin(accepted_ids)]
-            if frame.empty:
-                raise ValueError(
-                    f'No gaze rows matched image "{image_path.name}" in the "{id_col}" column.'
-                )
+        # ``utf-8-sig`` CSV exports can leave a BOM in the first header, so
+        # normalize it before locating the required ID column. Never fall back
+        # to importing every row: this file can contain gaze for many images.
+        id_col = next(
+            (column for column in frame.columns
+             if str(column).lstrip("\ufeff").strip().casefold() == "id"),
+            None,
+        )
+        if id_col is None:
+            raise ValueError('CSV must contain an "id" column to import gaze for the selected image.')
+        if not image_name:
+            raise ValueError("The uploaded image name is unavailable for matching the CSV id column.")
+        image_path = Path(image_name)
+        accepted_ids = {image_path.name.casefold(), image_path.stem.casefold()}
+        gaze_ids = frame[id_col].astype(str).str.strip().str.casefold()
+        frame = frame[gaze_ids.isin(accepted_ids)]
+        if frame.empty:
+            raise ValueError(
+                f'No gaze rows matched image "{image_path.name}" in the "{id_col}" column.'
+            )
         x, y = frame[x_col].astype(float).to_numpy(), frame[y_col].astype(float).to_numpy()
         duration = (frame[duration_col].astype(float).to_numpy()
                     if duration_col and duration_col != NO_DURATION else np.ones(len(x)))
@@ -147,8 +156,7 @@ def apply_csv(frame_json, x_col, y_col, duration_col, image, image_name):
         if len(x) and min(x) >= 0 and min(y) >= 0 and max(x) <= 1.05 and max(y) <= 1.05: x, y = x*w, y*h
         duration = duration / max(float(duration.max()), 1e-8)
         points = [(float(np.clip(a, 0, w-1)), float(np.clip(b, 0, h-1)), float(c)) for a,b,c in zip(x,y,duration)]
-        scope = f' for "{Path(image_name).name}"' if id_col is not None else ""
-        gr.Info(f"Loaded {len(points)} gaze points from CSV{scope}.")
+        gr.Info(f'Loaded {len(points)} gaze points for "{Path(image_name).name}" from CSV.')
         return points, draw_points(image, points)
     except Exception as exc:
         gr.Warning(f"Could not apply CSV: {exc}")
@@ -232,7 +240,7 @@ with gr.Blocks(title="GazeCorrect") as demo:
                 weight = gr.Slider(.1, 1, value=1, step=.1, label="Next click weight")
                 clear_button = gr.Button("Clear gaze", variant="secondary")
             with gr.Accordion("Import gaze CSV", open=False):
-                gr.Markdown("Upload a CSV, then choose the columns that contain fixation X, Y, and optional duration/weight. If it has an `id` column, only rows whose ID matches the uploaded image filename (or filename without its extension) are imported.")
+                gr.Markdown("Upload a CSV with an `id` column, then choose the fixation X, Y, and optional duration/weight columns. Only rows whose ID matches the uploaded image filename (or filename without its extension) are imported.")
                 csv_file = gr.File(label="Choose CSV", file_types=[".csv", ".tsv", ".txt"])
                 with gr.Row(visible=False) as csv_mapping:
                     x_column = gr.Dropdown(label="X column")