RootScope v4: manuscript model (LightGBM per round, 3 seeds, layer + soft-neighbor context, radial prior)

README.md

@@ -27,10 +27,7 @@ pericycle, stele, xylem, phloem.
    checked against the species table shipped with the package.
 3. Press Run. Segmentation and the DINOv2 embeddings run on the GPU; a
    700x700 image with about 200 cells takes about 20 to 45 seconds.
-4. Read the report under the table: it says how far to trust the result,
-   whether the root has an exodermis and why, and lists any warnings.
-5. Download the overlay image, the CSV of per-cell predictions and the
-   report.
+4. Download the overlay image and the CSV of per-cell predictions.
 
 One image at a time. For batches use the command line: `rootscope --tif-dir`.
 

app.py

@@ -464,10 +464,10 @@ def _render(results, model_name):
         stats += (f" · xylem poles: {int(row['n_xylem_poles'])} · "
                   f"phloem poles: {int(row['n_phloem_poles'])}")
 
-    report_path = workdir / f"{stem}_{model_name}_report.txt"
-    report = ("```\n" + report_path.read_text() + "```") if report_path.exists() else ""
+    # The per-image trust report (*_report.txt) is not shown on the Space (Nina, 2026-10-08);
+    # the command-line package still writes it.
     return (str(overlay_path), summary, stats,
-            gr.DownloadButton(value=str(csv_path), visible=True), report)
+            gr.DownloadButton(value=str(csv_path), visible=True))
 
 
 def run_rootscope(tif_path, tif_file, um_per_px, which_model, label_cells,
@@ -535,7 +535,7 @@ def run_rootscope(tif_path, tif_file, um_per_px, which_model, label_cells,
     zip_path = workdir / f"{stem}_rootscope_results.zip"
     with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as z:
         for f in sorted(workdir.iterdir()):
-            if f.suffix in (".png", ".csv", ".txt", ".json"):
+            if f.suffix in (".png", ".csv", ".json") and not f.name.endswith("_report.txt"):
                 z.write(f, arcname=f.name)
 
     available = [m for m in MODEL_CHOICES
@@ -545,18 +545,18 @@ def run_rootscope(tif_path, tif_file, um_per_px, which_model, label_cells,
     results = {"workdir": str(workdir), "stem": stem, "df": df,
                "n_layers": n_layers, "um_per_px": float(um_per_px),
                "available": available}
-    overlay_path, summary, stats, csv_button, report = _render(results, chosen)
+    overlay_path, summary, stats, csv_button = _render(results, chosen)
     return (overlay_path, summary, stats, csv_button,
             gr.DownloadButton(value=str(zip_path), visible=True),
             gr.update(choices=available, value=chosen),
-            results, report)
+            results)
 
 
 def switch_model(results, model_name):
     """Swap the displayed model without recomputing, since every model's overlay and
     CSV was already written to the run's temp dir."""
     if not results or model_name not in results.get("available", []):
-        return gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
+        return gr.update(), gr.update(), gr.update(), gr.update()
     return _render(results, model_name)
 
 
@@ -631,8 +631,7 @@ with gr.Blocks(title=TITLE, **_blocks_kwargs) as demo:
                 placeholder="e.g. Pisum sativum",
                 info="Not required. If given, it is checked against the species "
                      "table: for a training species it settles whether the root "
-                     "has an exodermis; otherwise the image decides and the "
-                     "report says how sure that call is.",
+                     "has an exodermis; otherwise the image decides.",
             )
             with gr.Accordion("Advanced", open=False):
                 smooth_layers = gr.Checkbox(
@@ -681,7 +680,6 @@ with gr.Blocks(title=TITLE, **_blocks_kwargs) as demo:
             stats_md = gr.Markdown()
             summary = gr.Dataframe(label="Cells per type", interactive=False,
                                    wrap=True)
-            report_md = gr.Markdown(label="How far to trust this result")
             with gr.Row():
                 csv_btn = gr.DownloadButton("Download predictions (CSV)",
                                             visible=False)
@@ -714,11 +712,10 @@ with gr.Blocks(title=TITLE, **_blocks_kwargs) as demo:
         run_rootscope,
         [tif_state, tif_upload, um_per_px, model_pick, label_cells, max_rounds,
          species, smooth_layers],
-        [overlay, summary, stats_md, csv_btn, zip_btn, model_pick, results_state,
-         report_md],
+        [overlay, summary, stats_md, csv_btn, zip_btn, model_pick, results_state],
     )
     model_pick.change(switch_model, [results_state, model_pick],
-                      [overlay, summary, stats_md, csv_btn, report_md])
+                      [overlay, summary, stats_md, csv_btn])
     if SHOW_DIAGNOSTICS:
         diag_btn.click(run_diagnostics, None, diag_out)
         env_btn.click(env_report, None, diag_out)