Upload app.py

app.py

@@ -926,6 +926,69 @@ def compute_summary(r1, r2, r3, r4):
             _block(CONC_PRESETS[0][1]), _block(CONC_PRESETS[1][1]))
 
 
+def export_summary_csv(r1, r2, r3, r4):
+    """One rectangular table: a row per tab plus a Mean row, concentrations
+    included on every row. Rectangular rather than sectioned so it loads
+    straight into pandas/Excel without hand-editing.
+
+    Returns (path_or_None, status_message).
+    """
+    results = [r1, r2, r3, r4]
+    valid = [(i + 1, r) for i, r in enumerate(results)
+             if r is not None and r.get("cell_count") is not None]
+    if not valid:
+        return None, "No data to export — run segmentation in at least one tab first."
+
+    def _avg(key):
+        vals = [r.get(key) for _, r in valid if r.get(key) is not None]
+        return (sum(vals) / len(vals)) if vals else 0.0
+
+    means = {k: _avg(k) for k in
+             ("cell_count", "live", "dead", "viab_percent", "confluency")}
+
+    header = ["Tab", "Total cells", "Live cells", "Dead cells",
+              "Viability (%)", "Confluency (%)"]
+    for name, mult in CONC_PRESETS:
+        header += [f"Live conc {name} [x{mult}] (cells/mL)",
+                   f"Dead conc {name} [x{mult}] (cells/mL)",
+                   f"Total conc {name} [x{mult}] (cells/mL)"]
+    header += ["Tabs averaged", "Note"]
+
+    def _row(label, total, live, dead, viab, conf, n_avg="", note=""):
+        row = [label,
+               f"{total:.0f}" if label != "Mean" else f"{total:.2f}",
+               f"{live:.0f}"  if label != "Mean" else f"{live:.2f}",
+               f"{dead:.0f}"  if label != "Mean" else f"{dead:.2f}",
+               f"{viab:.1f}", f"{conf:.1f}"]
+        for _, mult in CONC_PRESETS:
+            row += [f"{live * mult:.0f}", f"{dead * mult:.0f}", f"{total * mult:.0f}"]
+        row += [n_avg, note]
+        return row
+
+    rows = [_row(str(tab),
+                 r.get("cell_count") or 0.0, r.get("live") or 0.0,
+                 r.get("dead") or 0.0, r.get("viab_percent") or 0.0,
+                 r.get("confluency") or 0.0)
+            for tab, r in valid]
+
+    n = len(valid)
+    note = ("" if n == 4 else
+            "Fewer than 4 squares averaged - not the standard hemocytometer convention")
+    rows.append(_row("Mean", means["cell_count"], means["live"], means["dead"],
+                     means["viab_percent"], means["confluency"], str(n), note))
+
+    tmp = tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False, newline="")
+    w = csv.writer(tmp)
+    w.writerow(header)
+    w.writerows(rows)
+    tmp.close()
+
+    msg = f"Exported {n} tab{'s' if n > 1 else ''} + mean row."
+    if note:
+        msg += " ⚠ " + note
+    return tmp.name, msg
+
+
 # ---------------------------------------------------------------------------
 # Training data export — feature extraction per cell
 # ---------------------------------------------------------------------------
@@ -1859,6 +1922,26 @@ with gr.Blocks(
                      summary_conc_2x, summary_conc_10x]
         )
 
+        gr.Markdown("### Export")
+        with gr.Row():
+            summary_export_btn  = gr.Button("⬇️ Export summary CSV",
+                                            variant="secondary")
+            summary_export_info = gr.Textbox(label="Export status", lines=2,
+                                             interactive=False)
+        summary_export_file = gr.File(label="Download summary CSV", visible=False)
+
+        def on_export_summary(*tab_results):
+            path, msg = export_summary_csv(*tab_results)
+            if path is None:
+                return gr.update(visible=False), msg
+            return gr.update(value=path, visible=True), msg
+
+        summary_export_btn.click(
+            fn=on_export_summary,
+            inputs=result_states,
+            outputs=[summary_export_file, summary_export_info]
+        )
+
 
 
 if __name__ == "__main__":