Replace default_display with an input_metadata field with simpler semantics.

examples/Model use

@@ -42,31 +42,22 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": {
-          "dataframes": {
-            "df": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_test": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_train": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            }
-          },
-          "other": {},
-          "relations": []
-        },
+        "display": null,
         "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              }
+            },
+            "other": {},
+            "relations": []
+          }
+        ],
         "meta": {
           "inputs": {
             "bundle": {
@@ -128,6 +119,7 @@
         "collapsed": null,
         "display": null,
         "error": null,
+        "input_metadata": [],
         "meta": {
           "inputs": {},
           "name": "Import Parquet",
@@ -587,54 +579,54 @@
               ],
               "data": [
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-                  "[1.48271036 1.10563457 1.98858833 1.82286644]",
-                  "[1.4503304958343506, 1.1575173139572144, 1.996051549911499, 1.8715994358062744]"
+                  "[0.49691743 0.61873293 0.90698647 0.94486356]",
+                  "[1.49691749 1.61873293 1.90698647 1.94486356]",
+                  "[1.4993021488189697, 1.6404846906661987, 1.923316240310669, 1.9422152042388916]"
                 ],
                 [
-                  "[0.54914117 0.03810108 0.87531954 0.73044223]",
-                  "[1.54914117 1.03810108 1.87531948 1.73044229]",
-                  "[1.5054945945739746, 1.0799291133880615, 1.8784711360931396, 1.7745399475097656]"
+                  "[0.56922203 0.98222166 0.76851749 0.28615737]",
+                  "[1.56922197 1.9822216  1.76851749 1.28615737]",
+                  "[1.5835213661193848, 1.9884355068206787, 1.7694181203842163, 1.2917503118515015]"
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-                  "[0.26661873 0.45946234 0.13510543 0.81294441]",
-                  "[1.26661873 1.4594624  1.13510537 1.81294441]",
-                  "[1.2893636226654053, 1.4012629985809326, 1.1273266077041626, 1.7483277320861816]"
+                  "[0.90817457 0.89270043 0.38583666 0.66566533]",
+                  "[1.90817451 1.89270043 1.3858366  1.66566539]",
+                  "[1.9053494930267334, 1.9083378314971924, 1.3998609781265259, 1.6636812686920166]"
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-                  "[0.9630242  0.76359051 0.24853623 0.76881069]",
-                  "[1.96302414 1.76359057 1.24853623 1.76881075]",
-                  "[1.9627504348754883, 1.808237075805664, 1.2652870416641235, 1.7989287376403809]"
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-                  "[1.52046251 1.45887971 1.72169852 1.29517913]",
-                  "[1.4980013370513916, 1.4540209770202637, 1.7123804092407227, 1.3061636686325073]"
+                  "[0.04508126 0.76880038 0.80721325 0.62542385]",
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+                  "[1.0830243825912476, 1.7584562301635742, 1.8005754947662354, 1.6277496814727783]"
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-                  "[1.6632444858551025, 2.0650277137756348, 1.8643897771835327, 2.032055377960205]"
+                  "[0.6032477  0.83361369 0.18538666 0.19108021]",
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                 ],
                 [
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-                  "[1.95928192 1.84273899 1.7151463  1.38619852]",
-                  "[1.943433403968811, 1.905775785446167, 1.727120280265808, 1.4508745670318604]"
+                  "[0.15064228 0.03198934 0.25754827 0.51484001]",
+                  "[1.15064228 1.03198934 1.25754833 1.51484001]",
+                  "[1.1556042432785034, 0.9955940246582031, 1.2316606044769287, 1.5150485038757324]"
                 ],
                 [
-                  "[0.94516498 0.08422136 0.5608117  0.07652664]",
-                  "[1.94516492 1.08422136 1.56081176 1.07652664]",
-                  "[1.8603894710540771, 1.0990982055664062, 1.5473597049713135, 1.1221177577972412]"
+                  "[0.48959708 0.48549271 0.32688856 0.356677  ]",
+                  "[1.48959708 1.48549271 1.32688856 1.35667706]",
+                  "[1.4930214881896973, 1.467790961265564, 1.3132573366165161, 1.3589863777160645]"
                 ],
                 [
-                  "[0.32225502 0.16999388 0.05823922 0.9628762 ]",
-                  "[1.32225502 1.16999388 1.05823922 1.9628762 ]",
-                  "[1.3253933191299438, 1.1194703578948975, 1.0517100095748901, 1.903820514678955]"
+                  "[0.08107251 0.2602725  0.18861133 0.44833237]",
+                  "[1.08107257 1.2602725  1.18861127 1.44833231]",
+                  "[1.102121114730835, 1.2180893421173096, 1.160165548324585, 1.4495322704315186]"
                 ],
                 [
-                  "[0.91730917 0.22574073 0.09591609 0.33056474]",
-                  "[1.91730917 1.22574067 1.09591603 1.33056474]",
-                  "[1.8627445697784424, 1.2110977172851562, 1.085142731666565, 1.3327856063842773]"
+                  "[0.68094063 0.45189077 0.22661722 0.37354094]",
+                  "[1.68094063 1.45189071 1.22661722 1.37354088]",
+                  "[1.6725687980651855, 1.4393560886383057, 1.2169336080551147, 1.3746893405914307]"
                 ]
               ]
             },
@@ -644,6 +636,10 @@
                 "y"
               ],
               "data": [
+                [
+                  "[0.52046251 0.45887971 0.72169858 0.29517919]",
+                  "[1.52046251 1.45887971 1.72169852 1.29517913]"
+                ],
                 [
                   "[0.85706753 0.61447072 0.41741937 0.85147089]",
                   "[1.85706758 1.61447072 1.41741943 1.85147095]"
@@ -676,6 +672,10 @@
                   "[4.27091718e-01 4.89909172e-01 6.92297399e-01 2.57611275e-04]",
                   "[1.42709172 1.48990917 1.69229746 1.00025761]"
                 ],
+                [
+                  "[0.32225502 0.16999388 0.05823922 0.9628762 ]",
+                  "[1.32225502 1.16999388 1.05823922 1.9628762 ]"
+                ],
                 [
                   "[0.50783676 0.04156506 0.21984279 0.8454656 ]",
                   "[1.50783682 1.04156506 1.21984279 1.84546566]"
@@ -688,18 +688,10 @@
                   "[0.11693293 0.49860179 0.55020827 0.88832849]",
                   "[1.11693287 1.49860179 1.55020833 1.88832855]"
                 ],
-                [
-                  "[0.48959708 0.48549271 0.32688856 0.356677  ]",
-                  "[1.48959708 1.48549271 1.32688856 1.35667706]"
-                ],
                 [
                   "[0.50272274 0.54912758 0.17663097 0.79070699]",
                   "[1.50272274 1.54912758 1.17663097 1.79070699]"
                 ],
-                [
-                  "[0.04508126 0.76880038 0.80721325 0.62542385]",
-                  "[1.04508126 1.76880038 1.80721331 1.62542391]"
-                ],
                 [
                   "[0.19908059 0.17570406 0.51475513 0.1893943 ]",
                   "[1.19908059 1.175704   1.51475513 1.18939424]"
@@ -717,17 +709,13 @@
                   "[1.24388778 1.07268476 1.68350863 1.73431659]"
                 ],
                 [
-                  "[0.56922203 0.98222166 0.76851749 0.28615737]",
-                  "[1.56922197 1.9822216  1.76851749 1.28615737]"
+                  "[0.62569475 0.9881897  0.83639616 0.9828859 ]",
+                  "[1.62569475 1.9881897  1.83639622 1.98288584]"
                 ],
                 [
                   "[0.88776821 0.51636773 0.30333066 0.32230979]",
                   "[1.88776827 1.51636767 1.30333066 1.32230973]"
                 ],
-                [
-                  "[0.90817457 0.89270043 0.38583666 0.66566533]",
-                  "[1.90817451 1.89270043 1.3858366  1.66566539]"
-                ],
                 [
                   "[0.48507756 0.80808765 0.77162558 0.47834778]",
                   "[1.48507762 1.80808759 1.77162552 1.47834778]"
@@ -752,6 +740,14 @@
                   "[0.23942459 0.90487361 0.69337189 0.65089428]",
                   "[1.23942459 1.90487361 1.69337189 1.65089428]"
                 ],
+                [
+                  "[0.94516498 0.08422136 0.5608117  0.07652664]",
+                  "[1.94516492 1.08422136 1.56081176 1.07652664]"
+                ],
+                [
+                  "[0.26661873 0.45946234 0.13510543 0.81294441]",
+                  "[1.26661873 1.4594624  1.13510537 1.81294441]"
+                ],
                 [
                   "[0.30754459 0.77694583 0.09278506 0.38326019]",
                   "[1.30754459 1.77694583 1.09278512 1.38326025]"
@@ -760,6 +756,10 @@
                   "[0.27845025 0.32472342 0.82203609 0.77107543]",
                   "[1.27845025 1.32472348 1.82203603 1.77107549]"
                 ],
+                [
+                  "[0.4827103  0.10563457 0.98858833 0.82286644]",
+                  "[1.48271036 1.10563457 1.98858833 1.82286644]"
+                ],
                 [
                   "[0.98033333 0.97656083 0.38939917 0.81491041]",
                   "[1.98033333 1.97656083 1.38939917 1.81491041]"
@@ -816,10 +816,6 @@
                   "[0.77427191 0.21829212 0.12769502 0.74303615]",
                   "[1.77427197 1.21829212 1.12769508 1.74303615]"
                 ],
-                [
-                  "[0.08107251 0.2602725  0.18861133 0.44833237]",
-                  "[1.08107257 1.2602725  1.18861127 1.44833231]"
-                ],
                 [
                   "[0.59812403 0.78395379 0.0291847  0.81814629]",
                   "[1.59812403 1.78395379 1.0291847  1.81814623]"
@@ -840,6 +836,10 @@
                   "[0.85566247 0.83362883 0.48424995 0.25265992]",
                   "[1.85566247 1.83362889 1.48424995 1.25265992]"
                 ],
+                [
+                  "[0.95928186 0.84273899 0.71514636 0.38619852]",
+                  "[1.95928192 1.84273899 1.7151463  1.38619852]"
+                ],
                 [
                   "[0.32565445 0.90939188 0.07488042 0.13730896]",
                   "[1.32565451 1.90939188 1.07488036 1.13730896]"
@@ -852,6 +852,10 @@
                   "[0.79905868 0.89367443 0.75429088 0.3190186 ]",
                   "[1.79905868 1.89367437 1.75429082 1.3190186 ]"
                 ],
+                [
+                  "[0.54914117 0.03810108 0.87531954 0.73044223]",
+                  "[1.54914117 1.03810108 1.87531948 1.73044229]"
+                ],
                 [
                   "[0.67418337 0.79634351 0.23229051 0.71345252]",
                   "[1.67418337 1.79634356 1.23229051 1.71345258]"
@@ -896,6 +900,10 @@
                   "[0.47963417 0.81818312 0.48720706 0.49339259]",
                   "[1.47963417 1.81818318 1.48720706 1.49339259]"
                 ],
+                [
+                  "[0.9630242  0.76359051 0.24853623 0.76881069]",
+                  "[1.96302414 1.76359057 1.24853623 1.76881075]"
+                ],
                 [
                   "[0.60609657 0.96257663 0.19292736 0.95702219]",
                   "[1.60609651 1.96257663 1.19292736 1.95702219]"
@@ -916,21 +924,13 @@
                   "[0.59492421 0.90274489 0.38069052 0.46101224]",
                   "[1.59492421 1.90274489 1.38069057 1.46101224]"
                 ],
-                [
-                  "[0.15064228 0.03198934 0.25754827 0.51484001]",
-                  "[1.15064228 1.03198934 1.25754833 1.51484001]"
-                ],
                 [
                   "[0.12024075 0.21342516 0.56858408 0.58644271]",
                   "[1.12024069 1.21342516 1.56858408 1.58644271]"
                 ],
                 [
-                  "[0.49691743 0.61873293 0.90698647 0.94486356]",
-                  "[1.49691749 1.61873293 1.90698647 1.94486356]"
-                ],
-                [
-                  "[0.6032477  0.83361369 0.18538666 0.19108021]",
-                  "[1.60324764 1.83361363 1.18538666 1.19108021]"
+                  "[0.91730917 0.22574073 0.09591609 0.33056474]",
+                  "[1.91730917 1.22574067 1.09591603 1.33056474]"
                 ],
                 [
                   "[0.63235509 0.70352674 0.96188956 0.46240485]",
@@ -980,10 +980,6 @@
                   "[0.28942841 0.05601001 0.33039129 0.27781558]",
                   "[1.28942847 1.05601001 1.33039129 1.27781558]"
                 ],
-                [
-                  "[0.68094063 0.45189077 0.22661722 0.37354094]",
-                  "[1.68094063 1.45189071 1.22661722 1.37354088]"
-                ],
                 [
                   "[0.43681622 0.74680805 0.83598751 0.12414402]",
                   "[1.43681622 1.74680805 1.83598757 1.12414408]"
@@ -992,10 +988,6 @@
                   "[0.47870928 0.17129105 0.27300501 0.20634609]",
                   "[1.47870922 1.17129111 1.27300501 1.20634604]"
                 ],
-                [
-                  "[0.72795159 0.79317838 0.27832931 0.96576637]",
-                  "[1.72795153 1.79317832 1.27832937 1.96576643]"
-                ],
                 [
                   "[0.87608397 0.93200487 0.80169648 0.37758952]",
                   "[1.87608397 1.93200493 1.80169654 1.37758946]"
@@ -1008,11 +1000,55 @@
             }
           },
           "other": {
-            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(in_features=4, out_features=4, bias=True): Input__embedding_1_x -> Linear_1_x\n  (1) - <function leaky_relu at 0x731d1381bba0>: Linear_1_x -> Activation_2_x\n  (2) - Identity(): Activation_2_x -> Activation_2_x\n), model_inputs=['Input__embedding_1_x'], model_outputs=['Activation_2_x'], loss_inputs=['Input__label_1_y', 'Activation_2_x'], loss=Sequential(\n  (0) - <function mse_loss at 0x731d138216c0>: Activation_2_x, Input__label_1_y -> MSE_loss_1_loss\n  (1) - Identity(): MSE_loss_1_loss -> loss\n), optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n), source_workspace=None, trained=True)"
+            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(in_features=4, out_features=4, bias=True): Input__embedding_1_x -> Linear_1_x\n  (1) - <function leaky_relu at 0x719e0ce23a60>: Linear_1_x -> Activation_2_x\n  (2) - Identity(): Activation_2_x -> Activation_2_x\n), model_inputs=['Input__embedding_1_x'], model_outputs=['Activation_2_x'], loss_inputs=['Input__label_1_y', 'Activation_2_x'], loss=Sequential(\n  (0) - <function mse_loss at 0x719e0ce2d580>: Activation_2_x, Input__label_1_y -> MSE_loss_1_loss\n  (1) - Identity(): MSE_loss_1_loss -> loss\n), optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n), source_workspace=None, trained=True)"
           },
           "relations": []
         },
         "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "predicted",
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              }
+            },
+            "other": {
+              "model": {
+                "model": {
+                  "inputs": [
+                    "Input__embedding_1_x"
+                  ],
+                  "loss_inputs": [
+                    "Input__label_1_y",
+                    "Activation_2_x"
+                  ],
+                  "outputs": [
+                    "Activation_2_x"
+                  ],
+                  "trained": true
+                },
+                "type": "model"
+              }
+            },
+            "relations": []
+          }
+        ],
         "meta": {
           "inputs": {
             "bundle": {
@@ -1056,48 +1092,34 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": {
-          "dataframes": {
-            "df": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_test": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_train": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            }
-          },
-          "other": {
-            "model": {
-              "model": {
-                "inputs": [
-                  "Input__embedding_1_x"
-                ],
-                "loss_inputs": [
-                  "Input__label_1_y",
-                  "Activation_2_x"
-                ],
-                "outputs": [
-                  "Activation_2_x"
-                ],
-                "trained": false
-              },
-              "type": "model"
-            }
-          },
-          "relations": []
-        },
+        "display": null,
         "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              }
+            },
+            "other": {},
+            "relations": []
+          }
+        ],
         "meta": {
           "inputs": {
             "bundle": {
@@ -1157,48 +1179,51 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": {
-          "dataframes": {
-            "df": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_test": {
-              "columns": [
-                "x",
-                "y"
-              ]
+        "display": null,
+        "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              }
             },
-            "df_train": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            }
-          },
-          "other": {
-            "model": {
+            "other": {
               "model": {
-                "inputs": [
-                  "Input__embedding_1_x"
-                ],
-                "loss_inputs": [
-                  "Input__label_1_y",
-                  "Activation_2_x"
-                ],
-                "outputs": [
-                  "Activation_2_x"
-                ],
-                "trained": true
-              },
-              "type": "model"
-            }
-          },
-          "relations": []
-        },
-        "error": null,
+                "model": {
+                  "inputs": [
+                    "Input__embedding_1_x"
+                  ],
+                  "loss_inputs": [
+                    "Input__label_1_y",
+                    "Activation_2_x"
+                  ],
+                  "outputs": [
+                    "Activation_2_x"
+                  ],
+                  "trained": false
+                },
+                "type": "model"
+              }
+            },
+            "relations": []
+          }
+        ],
         "meta": {
           "inputs": {
             "bundle": {
@@ -1242,14 +1267,10 @@
               }
             }
           },
-          "position": {
-            "x": 666.0,
-            "y": 492.0
-          },
           "type": "basic"
         },
         "params": {
-          "epochs": "1000",
+          "epochs": "1001",
           "input_mapping": "{\"map\":{\"Input__embedding_1_x\":{\"column\":\"x\",\"df\":\"df_train\"},\"Input__label_1_y\":{\"column\":\"y\",\"df\":\"df_train\"}}}",
           "model_name": "model"
         },
@@ -1270,49 +1291,52 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": {
-          "dataframes": {
-            "df": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            },
-            "df_test": {
-              "columns": [
-                "predicted",
-                "x",
-                "y"
-              ]
+        "display": null,
+        "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "predicted",
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              }
             },
-            "df_train": {
-              "columns": [
-                "x",
-                "y"
-              ]
-            }
-          },
-          "other": {
-            "model": {
+            "other": {
               "model": {
-                "inputs": [
-                  "Input__embedding_1_x"
-                ],
-                "loss_inputs": [
-                  "Input__label_1_y",
-                  "Activation_2_x"
-                ],
-                "outputs": [
-                  "Activation_2_x"
-                ],
-                "trained": true
-              },
-              "type": "model"
-            }
-          },
-          "relations": []
-        },
-        "error": null,
+                "model": {
+                  "inputs": [
+                    "Input__embedding_1_x"
+                  ],
+                  "loss_inputs": [
+                    "Input__label_1_y",
+                    "Activation_2_x"
+                  ],
+                  "outputs": [
+                    "Activation_2_x"
+                  ],
+                  "trained": true
+                },
+                "type": "model"
+              }
+            },
+            "relations": []
+          }
+        ],
         "meta": {
           "inputs": {
             "bundle": {
@@ -1356,10 +1380,6 @@
               }
             }
           },
-          "position": {
-            "x": 934.0,
-            "y": 167.0
-          },
           "type": "basic"
         },
         "params": {

lynxkite-app/src/lynxkite_app/crdt.py

@@ -90,6 +90,7 @@ last_ws_input = None
 def clean_input(ws_pyd):
     for node in ws_pyd.nodes:
         node.data.display = None
+        node.data.input_metadata = None
         node.data.error = None
         node.data.status = workspace.NodeStatus.done
         node.position.x = 0
@@ -168,9 +169,12 @@ def try_to_load_workspace(ws: pycrdt.Map, name: str):
     """
     if os.path.exists(name):
         ws_pyd = workspace.load(name)
-        # We treat the display field as a black box, since it is a large
-        # dictionary that is meant to change as a whole.
-        crdt_update(ws, ws_pyd.model_dump(), non_collaborative_fields={"display"})
+        crdt_update(
+            ws,
+            ws_pyd.model_dump(),
+            # We treat some fields as black boxes. They are not edited on the frontend.
+            non_collaborative_fields={"display", "input_metadata"},
+        )
 
 
 last_known_versions = {}

lynxkite-app/tests/test_main.py

@@ -37,6 +37,7 @@ def test_save_and_load():
                     "type": "basic",
                     "data": {
                         "display": None,
+                        "input_metadata": None,
                         "error": "Unknown operation.",
                         "title": "Test node",
                         "params": {"param1": "value"},

lynxkite-app/web/playwright.config.ts

@@ -24,7 +24,7 @@ export default defineConfig({
   ],
   webServer: {
     command: "cd ../../examples && lynxkite",
-    url: "http://127.0.0.1:8000",
+    port: 8000,
     reuseExistingServer: false,
   },
 });

lynxkite-app/web/src/apiTypes.ts

@@ -5,6 +5,8 @@
 /* Do not modify it by hand - just update the pydantic models and then re-run the script
 */
 
+export type NodeStatus = "planned" | "active" | "done";
+
 export interface DirectoryEntry {
   name: string;
   type: string;
@@ -40,8 +42,9 @@ export interface WorkspaceNodeData {
     [k: string]: unknown;
   };
   display?: unknown;
+  input_metadata?: unknown;
   error?: string | null;
-  in_progress?: boolean;
+  status?: NodeStatus;
   [k: string]: unknown;
 }
 export interface Position {

lynxkite-app/web/src/workspace/nodes/NodeGroupParameter.tsx

@@ -24,6 +24,7 @@ interface GroupsType {
 interface NodeGroupParameterProps {
   meta: { selector: SelectorType; groups: GroupsType };
   value: any;
+  data: any;
   setParam: (name: string, value: any, options?: { delay: number }) => void;
   deleteParam: (name: string, options?: { delay: number }) => void;
 }
@@ -31,6 +32,7 @@ interface NodeGroupParameterProps {
 export default function NodeGroupParameter({
   meta,
   value,
+  data,
   setParam,
   deleteParam,
 }: NodeGroupParameterProps) {
@@ -60,6 +62,7 @@ export default function NodeGroupParameter({
       name={selector.name}
       key={selector.name}
       value={selectedValue}
+      data={data}
       meta={selector}
       onChange={handleSelectorChange}
     />

lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx

@@ -52,11 +52,14 @@ function getModelBindings(
     }
   }
   const bindings = new Set<string>();
-  const other = data?.display?.other ?? data?.display?.value?.other ?? {};
-  for (const e of Object.values(other) as any[]) {
-    if (e.type === "model") {
-      for (const b of bindingsOfModel(e.model)) {
-        bindings.add(b);
+  const inputs = data?.input_metadata?.value ?? data?.input_metadata ?? [];
+  for (const input of inputs) {
+    const other = input.other ?? {};
+    for (const e of Object.values(other) as any[]) {
+      if (e.type === "model") {
+        for (const b of bindingsOfModel(e.model)) {
+          bindings.add(b);
+        }
       }
     }
   }
@@ -78,15 +81,20 @@ function parseJsonOrEmpty(json: string): object {
 function ModelMapping({ value, onChange, data, variant }: any) {
   const v: any = parseJsonOrEmpty(value);
   v.map ??= {};
-  const dfs =
-    data?.display?.dataframes ?? data?.display?.value?.dataframes ?? {};
+  const dfs: { [df: string]: string[] } = {};
+  const inputs = data?.input_metadata?.value ?? data?.input_metadata ?? [];
+  for (const input of inputs) {
+    const dataframes = input.dataframes as {
+      [df: string]: { columns: string[] };
+    };
+    for (const [df, { columns }] of Object.entries(dataframes)) {
+      dfs[df] = columns;
+    }
+  }
   const bindings = getModelBindings(data, variant);
   return (
     <table className="model-mapping-param">
       <tbody>
-        <tr>
-          <td>mm</td>
-        </tr>
         {bindings.length > 0 ? (
           bindings.map((binding: string) => (
             <tr key={binding}>
@@ -105,7 +113,7 @@ function ModelMapping({ value, onChange, data, variant }: any) {
                       onChange(JSON.stringify({ map }));
                     } else {
                       const columnSpec = {
-                        column: dfs[df].columns[0],
+                        column: dfs[df][0],
                         ...(v.map?.[binding] || {}),
                         df,
                       };
@@ -149,7 +157,7 @@ function ModelMapping({ value, onChange, data, variant }: any) {
                       onChange(JSON.stringify({ map }));
                     }}
                   >
-                    {dfs[v.map?.[binding]?.df]?.columns.map((col: string) => (
+                    {dfs[v.map?.[binding]?.df]?.map((col: string) => (
                       <option key={col} value={col}>
                         {col}
                       </option>
@@ -221,13 +229,13 @@ export default function NodeParameter({
       ) : meta?.type?.type === BOOLEAN ? (
         <div className="form-control">
           <label className="label cursor-pointer">
+            {name.replace(/_/g, " ")}
             <input
               className="checkbox"
               type="checkbox"
               checked={value}
               onChange={(evt) => onChange(evt.currentTarget.checked)}
             />
-            {name.replace(/_/g, " ")}
           </label>
         </div>
       ) : meta?.type?.type === MODEL_TRAINING_INPUT_MAPPING ? (

lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx

@@ -49,6 +49,7 @@ function NodeWithParams(props: any) {
             <NodeGroupParameter
               key={name}
               value={value}
+              data={props.data}
               meta={metaParams?.[name]}
               setParam={(name: string, value: any, opts?: UpdateOptions) =>
                 setParam(name, value, opts || {})

lynxkite-core/src/lynxkite/core/ops.py

@@ -106,18 +106,13 @@ class Result:
     The `output` attribute is what will be used as input for other operations.
     The `display` attribute is used to send data to display on the UI. The value has to be
     JSON-serializable.
+    `input_metadata` is a list of JSON objects describing each input.
     """
 
     output: typing.Any = None
     display: ReadOnlyJSON | None = None
     error: str | None = None
-
-    def default_display(self) -> ReadOnlyJSON | None:
-        """Automatically extracts basic data from the output."""
-        if hasattr(self.output, "default_display"):
-            return self.output.default_display()
-        else:
-            return None
+    input_metadata: ReadOnlyJSON | None = None
 
 
 MULTI_INPUT = Input(name="multi", type="*")
@@ -147,7 +142,7 @@ def _param_to_type(name, value, type):
                 return None if value == "" else _param_to_type(name, value, type)
             case (type, types.NoneType):
                 return None if value == "" else _param_to_type(name, value, type)
-    if issubclass(type, pydantic.BaseModel):
+    if isinstance(type, typeof) and issubclass(type, pydantic.BaseModel):
         try:
             return type.model_validate_json(value)
         except pydantic.ValidationError:

lynxkite-core/src/lynxkite/core/workspace.py

@@ -32,6 +32,7 @@ class WorkspaceNodeData(BaseConfig):
     title: str
     params: dict
     display: Optional[object] = None
+    input_metadata: Optional[object] = None
     error: Optional[str] = None
     status: NodeStatus = NodeStatus.done
     # Also contains a "meta" field when going out.
@@ -59,13 +60,13 @@ class WorkspaceNode(BaseConfig):
     def publish_result(self, result: ops.Result):
         """Sends the result to the frontend. Call this in an executor when the result is available."""
         self.data.display = result.display
-        if self.data.display is None:
-            self.data.display = result.default_display()
+        self.data.input_metadata = result.input_metadata
         self.data.error = result.error
         self.data.status = NodeStatus.done
         if hasattr(self, "_crdt"):
             with self._crdt.doc.transaction():
                 self._crdt["data"]["display"] = self.data.display
+                self._crdt["data"]["input_metadata"] = self.data.input_metadata
                 self._crdt["data"]["error"] = self.data.error
                 self._crdt["data"]["status"] = NodeStatus.done
 

lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py

@@ -119,7 +119,7 @@ class Bundle:
             "other": {k: str(v) for k, v in self.other.items()},
         }
 
-    def default_display(self):
+    def metadata(self):
         """JSON-serializable information about the bundle, metadata only."""
         return {
             "dataframes": {
@@ -130,8 +130,7 @@ class Bundle:
             },
             "relations": [dataclasses.asdict(relation) for relation in self.relations],
             "other": {
-                k: getattr(v, "default_display", lambda: {})()
-                for k, v in self.other.items()
+                k: getattr(v, "metadata", lambda: {})() for k, v in self.other.items()
             },
         }
 
@@ -231,15 +230,18 @@ def _execute_node(node, ws, catalog, outputs):
         if os.environ.get("LYNXKITE_LOG_OP_ERRORS"):
             traceback.print_exc()
         result = ops.Result(error=str(e))
-        # On error, just output the first input. This helps reduce the errors on the frontend,
-        # and it lets boxes easily access things from their inputs on the UI, even in error state.
-        if inputs:
-            result.output = inputs[0]
+    result.input_metadata = [_get_metadata(i) for i in inputs]
     if result.output is not None:
         outputs[node.id] = result.output
     node.publish_result(result)
 
 
+def _get_metadata(x):
+    if hasattr(x, "metadata"):
+        return x.metadata()
+    return {}
+
+
 def df_for_frontend(df: pd.DataFrame, limit: int) -> pd.DataFrame:
     """Returns a DataFrame with values that are safe to send to the frontend."""
     df = df[:limit]

lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py

@@ -184,7 +184,7 @@ class ModelConfig:
         c.model = copy.deepcopy(self.model)
         return c
 
-    def default_display(self):
+    def metadata(self):
         return {
             "type": "model",
             "model": {