Merge branch 'main' of https://huggingface.co/spaces/de-Rodrigo/Embeddings

app.py

@@ -1048,7 +1048,19 @@ def run_model(model_name):
         
         # -------------------------------------------------------------------------
         # 4. Cálculo de distancias y scatter plot: Distance vs F1 (usando PC1 y PC2 globales)
-        model_options = ["es-digital-paragraph-degradation-seq", "es-digital-line-degradation-seq", "es-digital-seq", "es-digital-rotation-degradation-seq", "es-digital-zoom-degradation-seq", "es-render-seq"]
+        model_options = [
+            "es-digital-paragraph-degradation-seq", 
+            "es-digital-line-degradation-seq", 
+            "es-digital-seq", 
+            "es-digital-rotation-degradation-seq", 
+            "es-digital-zoom-degradation-seq", 
+            "es-render-seq",
+            "es-digital-seq_filtered_deus",
+            "es-digital-seq_filtered_liceo",
+            "es-digital-seq_filtered_lusitano",
+            "es-digital-seq_filtered_monterraso",
+            "es-digital-seq_filtered_patria"
+        ]
         model_options_with_default = [""]
         model_options_with_default.extend(model_options)
 
@@ -1269,13 +1281,15 @@ def run_model(model_name):
                         'label': df_heatmap['name']
                     })
                     invisible_renderer = heatmap_fig.circle('x', 'y', size=10, source=source_points, fill_alpha=0, line_alpha=0.5)
-                    
+                    # school = "patria"
                     if select_extra_dataset_hm != "-":
                         df_extra = df_all["synthetic"][df_all["synthetic"]["source"] == select_extra_dataset_hm].copy()
                         df_extra["x"] = df_extra[x_comp]
                         df_extra["y"] = df_extra[y_comp]
                         if 'name' not in df_extra.columns:
                             df_extra["name"] = df_extra["img"].apply(lambda x: x.split("/")[-1].replace(".png", "") if isinstance(x, str) else x)
+                        # mask = df_extra["name"].str.contains(school, case=False, na=False)
+                        # df_extra = df_extra[mask].copy()
                         source_extra_points = ColumnDataSource(data={
                             'x': df_extra['x'],
                             'y': df_extra['y'],