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app.py

@@ -33,6 +33,7 @@ from astropy.io import fits
 from astropy.visualization import make_lupton_rgb
 from matplotlib.patches import Ellipse
 
+import enhance
 from galaxy_ellipse import measure_ellipse
 
 IMG_SIZE = 320
@@ -179,14 +180,20 @@ def predict_prob(rgb: np.ndarray, use_tta: bool) -> float:
 
 # --- Figure ---
 
-def render(rgb: np.ndarray, geo: dict):
-    """Two-panel figure: enhanced RGB | enhanced RGB + ellipse overlay."""
+def render(rgb: np.ndarray, geo: dict, mode: str = "Original", gray: bool = False):
+    """Two-panel figure: enhanced RGB | enhanced RGB + ellipse overlay.
+
+    The enhancement is purely visual; the ellipse geometry was measured on the
+    original Lupton RGB, so it still lines up after any realce.
+    """
+    shown = enhance.apply(rgb, mode, gray)
+    title = "Imagen (Lupton grz)" if mode == "Original" else f"Imagen — {mode}"
     fig, axes = plt.subplots(1, 2, figsize=(8, 4.2))
-    axes[0].imshow(rgb)
-    axes[0].set_title("Imagen (Lupton grz)")
+    axes[0].imshow(shown)
+    axes[0].set_title(title)
     axes[0].axis("off")
 
-    axes[1].imshow(rgb)
+    axes[1].imshow(shown)
     axes[1].set_title("Imagen + elipse")
     axes[1].axis("off")
     if geo.get("status") == "ok":
@@ -209,21 +216,23 @@ def render(rgb: np.ndarray, geo: dict):
 
 # --- Gradio callback ---
 
-def classify(text: str, layer: str, pixscale: float, use_tta: bool):
+def classify(text: str, layer: str, pixscale: float, use_tta: bool, mode: str, gray: bool):
     try:
         ra, dec, label = resolve_coords(text)
     except Exception as exc:
-        return None, f"No pude resolver la entrada: {exc}"
+        return None, f"No pude resolver la entrada: {exc}", None, None
 
     try:
         rgb = fetch_rgb(ra, dec, layer, float(pixscale))
     except Exception as exc:
-        return None, f"No pude bajar el cutout: {exc}"
+        return None, f"No pude bajar el cutout: {exc}", None, None
 
+    # Prediction and geometry use the ORIGINAL Lupton RGB; the realce is only
+    # for display and is re-applied (without re-running the model) on change.
     prob = predict_prob(rgb, use_tta)
     pred = int(prob >= THR)
     geo = measure_ellipse(rgb)
-    fig = render(rgb, geo)
+    fig = render(rgb, geo, mode, gray)
 
     ring_txt = (
         f"{geo['ring_radius']:.0f} px (candidato exploratorio)"
@@ -247,7 +256,14 @@ def classify(text: str, layer: str, pixscale: float, use_tta: bool):
         f"_La predicción es una sugerencia de apoyo, no la verdad. La elipse verde es el "
         f"ajuste de la galaxia; el anillo naranja punteado es un candidato exploratorio._"
     )
-    return fig, summary
+    return fig, summary, rgb, geo
+
+
+def rerender(rgb, geo, mode: str, gray: bool):
+    """Re-apply a realce to the already-fetched image (no model re-run)."""
+    if rgb is None or geo is None:
+        return None
+    return render(rgb, geo, mode, gray)
 
 
 EXAMPLES = [
@@ -264,6 +280,8 @@ with gr.Blocks(title="Anillos en galaxias — Legacy Survey") as demo:
         "**URL del visor** de Legacy Survey. Bajo el cutout grz, lo proceso igual que en "
         "el entrenamiento y corro el modelo (ConvNeXt-Base / Zoobot)."
     )
+    st_rgb = gr.State(None)
+    st_geo = gr.State(None)
     with gr.Row():
         with gr.Column(scale=2):
             inp = gr.Textbox(label="Objeto / ra,dec / URL", placeholder="NGC 7796")
@@ -274,13 +292,23 @@ with gr.Blocks(title="Anillos en galaxias — Legacy Survey") as demo:
                 pixscale = gr.Number(value=0.262, label="Pixscale (arcsec/px)")
             tta = gr.Checkbox(value=False, label="TTA (6 vistas, más lento y estable)")
             btn = gr.Button("Clasificar", variant="primary")
+            mode = gr.Radio(
+                enhance.MODES, value="Original", label="Realce (visual, no afecta la predicción)"
+            )
+            gray = gr.Checkbox(value=False, label="Escala de grises (B/N)")
             gr.Examples(EXAMPLES, inputs=[inp])
         with gr.Column(scale=3):
             out_img = gr.Plot(label="Imagen + elipse")
             out_md = gr.Markdown()
 
-    btn.click(classify, [inp, layer, pixscale, tta], [out_img, out_md])
-    inp.submit(classify, [inp, layer, pixscale, tta], [out_img, out_md])
+    inputs = [inp, layer, pixscale, tta, mode, gray]
+    outputs = [out_img, out_md, st_rgb, st_geo]
+    btn.click(classify, inputs, outputs)
+    inp.submit(classify, inputs, outputs)
+
+    # Changing the realce re-renders the already-fetched image instantly.
+    mode.change(rerender, [st_rgb, st_geo, mode, gray], out_img)
+    gray.change(rerender, [st_rgb, st_geo, mode, gray], out_img)
 
 
 if __name__ == "__main__":