Merge pull request #64 from biggraph/darabos-tweaks

.gitignore

@@ -11,4 +11,5 @@ __pycache__
 node_modules
 dist
 build
+joblib-cache
 *.egg-info

lynxkite-app/data/Image processing

@@ -7,22 +7,11 @@
       "data": {
         "title": "Open image",
         "params": {
-          "filename": "/Users/danieldarabos/Downloads/mimic-a-fraction.png"
+          "filename": "https://media.licdn.com/dms/image/v2/C4E03AQEq4tdJKQiNHQ/profile-displayphoto-shrink_200_200/profile-displayphoto-shrink_200_200/0/1657270040827?e=2147483647&v=beta&t=lDxix0_0-_K7NUFqgPdzxY5-P7f73bWpPS_XRre842c"
         },
         "display": null,
         "error": null,
         "meta": {
-          "name": "Open image",
-          "params": {
-            "filename": {
-              "name": "filename",
-              "default": null,
-              "type": {
-                "type": "<class 'str'>"
-              }
-            }
-          },
-          "inputs": {},
           "outputs": {
             "output": {
               "name": "output",
@@ -32,15 +21,29 @@
               "position": "right"
             }
           },
-          "type": "basic",
-          "sub_nodes": null
-        }
+          "name": "Open image",
+          "inputs": {},
+          "params": {
+            "filename": {
+              "name": "filename",
+              "type": {
+                "type": "<class 'str'>"
+              },
+              "default": null
+            }
+          },
+          "type": "basic"
+        },
+        "__execution_delay": 0.0,
+        "collapsed": null
       },
       "position": {
-        "x": 19.215964588549014,
-        "y": 205.21642829186527
+        "x": -316.51795927908694,
+        "y": 122.80901061526373
       },
-      "parentId": null
+      "width": 422.0,
+      "parentId": null,
+      "height": 222.0
     },
     {
       "id": "View image 1",
@@ -48,30 +51,31 @@
       "data": {
         "title": "View image",
         "params": {},
-        "display": 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",
+        "display": "data:image/jpeg;base64,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",
         "error": null,
         "meta": {
-          "name": "View image",
-          "params": {},
+          "type": "image",
+          "outputs": {},
           "inputs": {
             "image": {
               "name": "image",
               "type": {
-                "type": "<module 'PIL.Image' from '/opt/miniconda3/lib/python3.12/site-packages/PIL/Image.py'>"
+                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
               },
               "position": "left"
             }
           },
-          "outputs": {},
-          "type": "image",
-          "sub_nodes": null
+          "name": "View image",
+          "params": {}
         }
       },
       "position": {
         "x": 371.2152385614552,
         "y": -243.68185336918702
       },
-      "parentId": null
+      "parentId": null,
+      "width": 265.0,
+      "height": 288.0
     },
     {
       "id": "Flip verically 1",
@@ -82,35 +86,38 @@
         "display": null,
         "error": null,
         "meta": {
-          "name": "Flip verically",
-          "params": {},
-          "inputs": {
-            "image": {
-              "name": "image",
-              "type": {
-                "type": "<module 'PIL.Image' from '/opt/miniconda3/lib/python3.12/site-packages/PIL/Image.py'>"
-              },
-              "position": "left"
-            }
-          },
           "outputs": {
             "output": {
+              "position": "right",
               "name": "output",
               "type": {
                 "type": "None"
-              },
-              "position": "right"
+              }
             }
           },
           "type": "basic",
-          "sub_nodes": null
-        }
+          "params": {},
+          "name": "Flip verically",
+          "inputs": {
+            "image": {
+              "name": "image",
+              "position": "left",
+              "type": {
+                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
+              }
+            }
+          }
+        },
+        "collapsed": true,
+        "__execution_delay": null
       },
       "position": {
-        "x": 258.90660520478934,
-        "y": 582.9425419285425
+        "x": 228.3853393986406,
+        "y": 245.68255477059915
       },
-      "parentId": null
+      "width": 200.0,
+      "parentId": null,
+      "height": 200.0
     },
     {
       "id": "View image 2",
@@ -118,29 +125,30 @@
       "data": {
         "title": "View image",
         "params": {},
-        "display": 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+        "display": "data:image/jpeg;base64,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",
         "error": null,
         "meta": {
-          "name": "View image",
-          "params": {},
+          "outputs": {},
           "inputs": {
             "image": {
-              "name": "image",
+              "position": "left",
               "type": {
-                "type": "<module 'PIL.Image' from '/opt/miniconda3/lib/python3.12/site-packages/PIL/Image.py'>"
+                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
               },
-              "position": "left"
+              "name": "image"
             }
           },
-          "outputs": {},
-          "type": "image",
-          "sub_nodes": null
+          "name": "View image",
+          "params": {},
+          "type": "image"
         }
       },
       "position": {
         "x": 1027.1387925400982,
         "y": 251.36630333493974
       },
+      "width": 222.0,
+      "height": 291.0,
       "parentId": null
     },
     {
@@ -151,36 +159,39 @@
         "params": {},
         "display": null,
         "error": null,
+        "collapsed": true,
+        "__execution_delay": null,
         "meta": {
-          "name": "To grayscale",
+          "type": "basic",
           "params": {},
-          "inputs": {
-            "image": {
-              "name": "image",
-              "type": {
-                "type": "<module 'PIL.Image' from '/opt/miniconda3/lib/python3.12/site-packages/PIL/Image.py'>"
-              },
-              "position": "left"
-            }
-          },
           "outputs": {
             "output": {
-              "name": "output",
               "type": {
                 "type": "None"
               },
-              "position": "right"
+              "position": "right",
+              "name": "output"
             }
           },
-          "type": "basic",
-          "sub_nodes": null
+          "name": "To grayscale",
+          "inputs": {
+            "image": {
+              "position": "left",
+              "type": {
+                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
+              },
+              "name": "image"
+            }
+          }
         }
       },
       "position": {
         "x": 826.1911193192234,
         "y": 579.1542134884979
       },
-      "parentId": null
+      "parentId": null,
+      "width": 200.0,
+      "height": 200.0
     },
     {
       "id": "Blur 1",
@@ -193,11 +204,11 @@
         "display": null,
         "error": null,
         "meta": {
-          "name": "Blur",
+          "type": "basic",
           "params": {
             "radius": {
+              "default": 5.0,
               "name": "radius",
-              "default": null,
               "type": {
                 "type": "<class 'float'>"
               }
@@ -207,11 +218,12 @@
             "image": {
               "name": "image",
               "type": {
-                "type": "<module 'PIL.Image' from '/opt/miniconda3/lib/python3.12/site-packages/PIL/Image.py'>"
+                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
               },
               "position": "left"
             }
           },
+          "name": "Blur",
           "outputs": {
             "output": {
               "name": "output",
@@ -220,16 +232,16 @@
               },
               "position": "right"
             }
-          },
-          "type": "basic",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
         "x": 505.15961556359304,
         "y": 539.8477981917164
       },
-      "parentId": null
+      "parentId": null,
+      "height": 200.0,
+      "width": 200.0
     }
   ],
   "edges": [

lynxkite-app/data/PyTorch demo

@@ -11,25 +11,28 @@
         "error": null,
         "meta": {
           "name": "Input: features",
-          "params": {},
-          "inputs": {},
+          "type": "basic",
           "outputs": {
             "x": {
+              "position": "top",
               "name": "x",
               "type": {
                 "type": "tensor"
-              },
-              "position": "top"
+              }
             }
           },
-          "type": "basic",
-          "sub_nodes": null
-        }
+          "params": {},
+          "inputs": {}
+        },
+        "collapsed": true,
+        "__execution_delay": null
       },
       "position": {
         "x": -108.60604658638658,
         "y": 63.96065124378427
       },
+      "width": 200.0,
+      "height": 200.0,
       "parentId": null
     },
     {
@@ -40,10 +43,13 @@
         "params": {},
         "display": null,
         "error": null,
+        "__execution_delay": null,
+        "collapsed": true,
         "meta": {
           "name": "Input: graph edges",
-          "params": {},
           "inputs": {},
+          "params": {},
+          "type": "basic",
           "outputs": {
             "edges": {
               "name": "edges",
@@ -52,16 +58,16 @@
               },
               "position": "top"
             }
-          },
-          "type": "basic",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
         "x": 180.7373888617958,
         "y": 58.54904654355781
       },
-      "parentId": null
+      "width": 200.0,
+      "parentId": null,
+      "height": 200.0
     },
     {
       "id": "Linear 1",
@@ -74,43 +80,44 @@
         "display": null,
         "error": null,
         "meta": {
-          "name": "Linear",
-          "params": {
-            "output_dim": {
-              "name": "output_dim",
-              "default": "same",
-              "type": {
-                "type": "<class 'str'>"
-              }
-            }
-          },
           "inputs": {
             "x": {
-              "name": "x",
               "type": {
                 "type": "tensor"
               },
-              "position": "bottom"
+              "position": "bottom",
+              "name": "x"
             }
           },
+          "type": "basic",
+          "name": "Linear",
           "outputs": {
             "x": {
-              "name": "x",
               "type": {
                 "type": "tensor"
               },
-              "position": "top"
+              "position": "top",
+              "name": "x"
             }
           },
-          "type": "basic",
-          "sub_nodes": null
+          "params": {
+            "output_dim": {
+              "name": "output_dim",
+              "type": {
+                "type": "<class 'str'>"
+              },
+              "default": "same"
+            }
+          }
         }
       },
       "position": {
-        "x": 56.938816909128036,
-        "y": -573.5634543506885
+        "x": 78.37881963123723,
+        "y": -528.3012263817914
       },
-      "parentId": null
+      "parentId": null,
+      "width": 204.0,
+      "height": 140.0
     },
     {
       "id": "Activation 1",
@@ -123,11 +130,30 @@
         "display": null,
         "error": null,
         "meta": {
+          "outputs": {
+            "x": {
+              "position": "top",
+              "type": {
+                "type": "tensor"
+              },
+              "name": "x"
+            }
+          },
           "name": "Activation",
+          "type": "basic",
+          "inputs": {
+            "x": {
+              "position": "bottom",
+              "name": "x",
+              "type": {
+                "type": "tensor"
+              }
+            }
+          },
           "params": {
             "type": {
+              "default": "OptionsFor_type.ReLU",
               "name": "type",
-              "default": 1,
               "type": {
                 "enum": [
                   "ReLU",
@@ -137,34 +163,16 @@
                 ]
               }
             }
-          },
-          "inputs": {
-            "x": {
-              "name": "x",
-              "type": {
-                "type": "tensor"
-              },
-              "position": "bottom"
-            }
-          },
-          "outputs": {
-            "x": {
-              "name": "x",
-              "type": {
-                "type": "tensor"
-              },
-              "position": "top"
-            }
-          },
-          "type": "basic",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
-        "x": 84.15324804216073,
-        "y": -729.2300004316357
+        "x": 98.44658319023353,
+        "y": -741.1411130550297
       },
-      "parentId": null
+      "height": 154.0,
+      "parentId": null,
+      "width": 173.0
     },
     {
       "id": "Dropout 1",
@@ -177,16 +185,7 @@
         "display": null,
         "error": null,
         "meta": {
-          "name": "Dropout",
-          "params": {
-            "p": {
-              "name": "p",
-              "default": 0.5,
-              "type": {
-                "type": "<class 'float'>"
-              }
-            }
-          },
+          "type": "basic",
           "inputs": {
             "x": {
               "name": "x",
@@ -196,59 +195,34 @@
               "position": "bottom"
             }
           },
-          "outputs": {
-            "x": {
-              "name": "x",
+          "name": "Dropout",
+          "params": {
+            "p": {
+              "default": 0.5,
               "type": {
-                "type": "tensor"
+                "type": "<class 'float'>"
               },
-              "position": "top"
+              "name": "p"
             }
           },
-          "type": "basic",
-          "sub_nodes": null
-        }
-      },
-      "position": {
-        "x": 56.938816909128036,
-        "y": -889.4846386414522
-      },
-      "parentId": null
-    },
-    {
-      "id": "Repeat 1",
-      "type": "area",
-      "data": {
-        "title": "Repeat",
-        "params": {
-          "times": "3"
-        },
-        "display": null,
-        "error": null,
-        "meta": {
-          "name": "Repeat",
-          "params": {
-            "times": {
-              "name": "times",
-              "default": 1,
+          "outputs": {
+            "x": {
+              "position": "top",
+              "name": "x",
               "type": {
-                "type": "<class 'int'>"
+                "type": "tensor"
               }
             }
-          },
-          "inputs": {},
-          "outputs": {},
-          "type": "area",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
-        "x": -48.6249442834993,
-        "y": -970.0583599108166
+        "x": 73.61437458187964,
+        "y": -929.9824215609918
       },
+      "width": 207.0,
       "parentId": null,
-      "width": 400,
-      "height": 600
+      "height": 159.0
     },
     {
       "id": "Graph conv 1",
@@ -261,11 +235,21 @@
         "display": null,
         "error": null,
         "meta": {
+          "outputs": {
+            "x": {
+              "type": {
+                "type": "tensor"
+              },
+              "name": "x",
+              "position": "top"
+            }
+          },
+          "type": "basic",
           "name": "Graph conv",
           "params": {
             "type": {
               "name": "type",
-              "default": 1,
+              "default": "OptionsFor_type.GCNConv",
               "type": {
                 "enum": [
                   "GCNConv",
@@ -278,10 +262,10 @@
           },
           "inputs": {
             "x": {
-              "name": "x",
               "type": {
                 "type": "tensor"
               },
+              "name": "x",
               "position": "bottom"
             },
             "edges": {
@@ -291,61 +275,17 @@
               },
               "position": "bottom"
             }
-          },
-          "outputs": {
-            "x": {
-              "name": "x",
-              "type": {
-                "type": "tensor"
-              },
-              "position": "top"
-            }
-          },
-          "type": "basic",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
         "x": 64.08886242755246,
         "y": -269.43023573181557
       },
+      "height": 200.0,
+      "width": 200.0,
       "parentId": null
     },
-    {
-      "id": "Repeat 2",
-      "type": "area",
-      "data": {
-        "title": "Repeat",
-        "params": {
-          "times": "5"
-        },
-        "display": null,
-        "error": null,
-        "meta": {
-          "name": "Repeat",
-          "params": {
-            "times": {
-              "name": "times",
-              "default": 1,
-              "type": {
-                "type": "<class 'int'>"
-              }
-            }
-          },
-          "inputs": {},
-          "outputs": {},
-          "type": "area",
-          "sub_nodes": null
-        }
-      },
-      "position": {
-        "x": -46.21033706832179,
-        "y": -326.2712248181098
-      },
-      "parentId": null,
-      "width": 400,
-      "height": 200
-    },
     {
       "id": "Supervised loss 1",
       "type": "basic",
@@ -354,19 +294,22 @@
         "params": {},
         "display": null,
         "error": null,
+        "collapsed": true,
+        "__execution_delay": null,
         "meta": {
-          "name": "Supervised loss",
+          "type": "basic",
           "params": {},
+          "name": "Supervised loss",
           "inputs": {
-            "x": {
-              "name": "x",
+            "y": {
+              "name": "y",
               "type": {
                 "type": "tensor"
               },
               "position": "bottom"
             },
-            "y": {
-              "name": "y",
+            "x": {
+              "name": "x",
               "type": {
                 "type": "tensor"
               },
@@ -375,22 +318,22 @@
           },
           "outputs": {
             "loss": {
-              "name": "loss",
+              "position": "top",
               "type": {
                 "type": "tensor"
               },
-              "position": "top"
+              "name": "loss"
             }
-          },
-          "type": "basic",
-          "sub_nodes": null
+          }
         }
       },
       "position": {
         "x": 110.53693593362718,
         "y": -1123.9976567905628
       },
-      "parentId": null
+      "parentId": null,
+      "height": 80.0,
+      "width": 204.0
     },
     {
       "id": "Input: label 1",
@@ -400,27 +343,30 @@
         "params": {},
         "display": null,
         "error": null,
+        "collapsed": true,
+        "__execution_delay": null,
         "meta": {
-          "name": "Input: label",
-          "params": {},
           "inputs": {},
           "outputs": {
             "y": {
-              "name": "y",
               "type": {
                 "type": "tensor"
               },
-              "position": "top"
+              "position": "top",
+              "name": "y"
             }
           },
+          "name": "Input: label",
           "type": "basic",
-          "sub_nodes": null
+          "params": {}
         }
       },
       "position": {
         "x": 666.110498676668,
         "y": -898.6721561114967
       },
+      "width": 200.0,
+      "height": 73.0,
       "parentId": null
     },
     {
@@ -429,17 +375,23 @@
       "data": {
         "title": "Optimizer",
         "params": {
-          "type": "AdamW",
-          "lr": 0.001
+          "lr": 0.001,
+          "type": "AdamW"
         },
         "display": null,
         "error": null,
         "meta": {
           "name": "Optimizer",
+          "outputs": {},
           "params": {
+            "lr": {
+              "default": 0.001,
+              "name": "lr",
+              "type": {
+                "type": "<class 'float'>"
+              }
+            },
             "type": {
-              "name": "type",
-              "default": 1,
               "type": {
                 "enum": [
                   "AdamW",
@@ -450,35 +402,136 @@
                   "Paged AdamW",
                   "Galore AdamW"
                 ]
-              }
-            },
-            "lr": {
-              "name": "lr",
-              "default": 0.001,
-              "type": {
-                "type": "<class 'float'>"
-              }
+              },
+              "name": "type",
+              "default": "OptionsFor_type.AdamW"
             }
           },
           "inputs": {
             "loss": {
+              "type": {
+                "type": "tensor"
+              },
               "name": "loss",
+              "position": "bottom"
+            }
+          },
+          "type": "basic"
+        }
+      },
+      "position": {
+        "x": 115.25730958703494,
+        "y": -1431.5320892753364
+      },
+      "width": 200.0,
+      "height": 233.0,
+      "parentId": null
+    },
+    {
+      "id": "Repeat 3",
+      "type": "basic",
+      "data": {
+        "title": "Repeat",
+        "params": {
+          "times": 1.0
+        },
+        "display": null,
+        "error": null,
+        "meta": {
+          "type": "basic",
+          "position": {
+            "y": 340.0,
+            "x": 371.0
+          },
+          "outputs": {
+            "output": {
+              "name": "output",
               "type": {
                 "type": "tensor"
               },
               "position": "bottom"
             }
           },
-          "outputs": {},
+          "name": "Repeat",
+          "params": {
+            "times": {
+              "name": "times",
+              "default": 1.0,
+              "type": {
+                "type": "<class 'int'>"
+              }
+            }
+          },
+          "inputs": {
+            "input": {
+              "type": {
+                "type": "tensor"
+              },
+              "position": "top",
+              "name": "input"
+            }
+          }
+        }
+      },
+      "position": {
+        "x": -245.1288628776232,
+        "y": -276.90661040974317
+      },
+      "width": 200.0,
+      "height": 200.0
+    },
+    {
+      "id": "Repeat 1",
+      "type": "basic",
+      "data": {
+        "title": "Repeat",
+        "params": {
+          "times": 1.0
+        },
+        "display": null,
+        "error": null,
+        "meta": {
+          "outputs": {
+            "output": {
+              "position": "bottom",
+              "type": {
+                "type": "tensor"
+              },
+              "name": "output"
+            }
+          },
+          "params": {
+            "times": {
+              "default": 1.0,
+              "type": {
+                "type": "<class 'int'>"
+              },
+              "name": "times"
+            }
+          },
+          "position": {
+            "x": 387.0,
+            "y": 337.0
+          },
           "type": "basic",
-          "sub_nodes": null
+          "name": "Repeat",
+          "inputs": {
+            "input": {
+              "name": "input",
+              "position": "top",
+              "type": {
+                "type": "tensor"
+              }
+            }
+          }
         }
       },
       "position": {
-        "x": 165.28398260528976,
-        "y": -1338.6254108128633
+        "x": -258.0088683218416,
+        "y": -737.3822225246788
       },
-      "parentId": null
+      "width": 200.0,
+      "height": 200.0
     }
   ],
   "edges": [
@@ -537,6 +590,34 @@
       "target": "Optimizer 1",
       "sourceHandle": "loss",
       "targetHandle": "loss"
+    },
+    {
+      "id": "Graph conv 1 Repeat 3",
+      "source": "Graph conv 1",
+      "target": "Repeat 3",
+      "sourceHandle": "x",
+      "targetHandle": "input"
+    },
+    {
+      "id": "Repeat 3 Graph conv 1",
+      "source": "Repeat 3",
+      "target": "Graph conv 1",
+      "sourceHandle": "output",
+      "targetHandle": "x"
+    },
+    {
+      "id": "Dropout 1 Repeat 1",
+      "source": "Dropout 1",
+      "target": "Repeat 1",
+      "sourceHandle": "x",
+      "targetHandle": "input"
+    },
+    {
+      "id": "Repeat 1 Linear 1",
+      "source": "Repeat 1",
+      "target": "Linear 1",
+      "sourceHandle": "output",
+      "targetHandle": "x"
     }
   ]
 }

lynxkite-app/data/example-pizza.md

@@ -0,0 +1,136 @@
+hello
+
+### 1. **Overview**
+
+This document outlines the pricing structure and available options for our pizza delivery service. The goal is to provide clear guidance on the pricing tiers, additional offerings, and optional extras to ensure consistency across all locations and platforms (phone, online, in-app). All pricing is based on current market trends, food costs, and competitive analysis.
+
+---
+
+### 2. **Pizza Options**
+
+#### 2.1 **Size & Base Pricing**
+
+| Size             | Diameter   | Price (Cheese Pizza) |
+|------------------|------------|----------------------|
+| Small            | 10 inches  | $8.99                |
+| Medium           | 12 inches  | $11.99               |
+| Large            | 14 inches  | $14.99               |
+| Extra Large      | 16 inches  | $17.99               |
+
+**Note**: Cheese pizza pricing includes sauce and cheese. Toppings are additional (see section 2.3).
+
+#### 2.2 **Crust Options**
+
+| Crust Type       | Description                              | Price Adjustment |
+|------------------|------------------------------------------|------------------|
+| Classic Hand-Tossed | Soft, airy texture                     | No Change        |
+| Thin & Crispy    | Light and crunchy                        | No Change        |
+| Stuffed Crust    | Filled with mozzarella                   | +$2.00 (M-XL)    |
+| Gluten-Free      | 10" only; made with rice flour           | +$2.50 (Small Only) |
+
+---
+
+### 3. **Toppings**
+
+#### 3.1 **Standard Toppings**
+**Price per topping:**
+
+- Small: $1.00
+- Medium: $1.50
+- Large: $2.00
+- Extra Large: $2.50
+
+| Topping          | Category       |
+|------------------|----------------|
+| Pepperoni        | Meat           |
+| Sausage          | Meat           |
+| Mushrooms        | Vegetable      |
+| Onions           | Vegetable      |
+| Bell Peppers     | Vegetable      |
+| Olives           | Vegetable      |
+| Extra Cheese     | Dairy          |
+
+#### 3.2 **Premium Toppings**
+**Price per topping:**
+
+- Small: $1.75
+- Medium: $2.25
+- Large: $2.75
+- Extra Large: $3.25
+
+| Topping          | Category       |
+|------------------|----------------|
+| Grilled Chicken  | Meat           |
+| Bacon            | Meat           |
+| Sun-Dried Tomatoes| Vegetable      |
+| Artichoke Hearts | Vegetable      |
+| Feta Cheese      | Dairy          |
+| Vegan Cheese     | Dairy Alternative |
+
+---
+
+### 4. **Specialty Pizzas**
+
+Specialty pizzas include a combination of premium toppings and are available in all sizes. Prices below are for Medium size, with additional costs for upgrading to larger sizes.
+
+| Pizza Name           | Description                                        | Price (Medium)  |
+|----------------------|----------------------------------------------------|-----------------|
+| Meat Lover’s          | Pepperoni, sausage, bacon, ham                     | $16.99          |
+| Veggie Delight        | Mushrooms, bell peppers, onions, olives            | $14.99          |
+| BBQ Chicken           | BBQ sauce, grilled chicken, red onions, cilantro   | $17.99          |
+| Margherita            | Fresh mozzarella, tomatoes, basil                  | $15.99          |
+| Hawaiian              | Ham, pineapple                                     | $14.99          |
+
+---
+
+### 5. **Additional Menu Items**
+
+#### 5.1 **Side Orders**
+
+| Item               | Description                          | Price         |
+|--------------------|--------------------------------------|---------------|
+| Garlic Breadsticks | Served with marinara dipping sauce   | $5.99         |
+| Chicken Wings      | Buffalo, BBQ, or plain (10 pieces)   | $9.99         |
+| Mozzarella Sticks  | Served with marinara (8 pieces)      | $6.99         |
+| Caesar Salad       | Romaine, croutons, Caesar dressing   | $7.99         |
+
+#### 5.2 **Desserts**
+
+| Item               | Description                          | Price         |
+|--------------------|--------------------------------------|---------------|
+| Chocolate Brownies | Chewy and rich (6 pieces)            | $4.99         |
+| Cinnamon Sticks    | Dusted with cinnamon sugar           | $5.99         |
+
+---
+
+### 6. **Drinks**
+
+| Size               | Price         |
+|--------------------|---------------|
+| 20 oz Bottle       | $1.99         |
+| 2-Liter Bottle     | $3.50         |
+
+Available options: Coke, Diet Coke, Sprite, Root Beer, Lemonade.
+
+---
+
+### 7. **Delivery Fees & Minimum Order**
+
+- **Delivery Fee**: $2.99
+- **Minimum Order**: $12.00
+
+*Note: Delivery fees and minimum order thresholds apply to all delivery orders within a 5-mile radius. Additional charges may apply for orders outside this zone.*
+
+---
+
+### 8. **Promotions & Discounts**
+
+- **Monday Madness**: Buy one large pizza, get a second pizza for 50% off.
+- **Student Discount**: 10% off with valid student ID (pickup only).
+- **Family Deal**: 2 large pizzas, 1 side, and 2-liter soda for $29.99.
+
+---
+
+### 9. **Conclusion**
+
+This pricing and menu structure is designed to offer a wide range of choices for our customers while maintaining competitive pricing and ensuring profitability. Please ensure all team members are familiar with the details in this document and implement it accordingly.

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

@@ -24,7 +24,8 @@ export default function NodeParameter({ name, value, meta, onChange }: NodeParam
         <textarea className="textarea textarea-bordered w-full max-w-xs"
           rows={6}
           value={value}
-          onChange={(evt) => onChange(evt.currentTarget.value)}
+          onChange={(evt) => onChange(evt.currentTarget.value, { delay: 2 })}
+          onBlur={(evt) => onChange(evt.currentTarget.value, { delay: 0 })}
         />
       </> : meta?.type?.enum ? <>
         <ParamName name={name} />

lynxkite-graph-analytics/pyproject.toml

@@ -6,9 +6,11 @@ readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
     "grand-cypher>=0.12.0",
+    "joblib>=1.4.2",
     "lynxkite-core",
     "matplotlib>=3.10.0",
     "networkx>=3.4.2",
+    "osmnx>=2.0.1",
     "pandas>=2.2.3",
     "polars[gpu]>=1.14.0",
 ]

lynxkite-graph-analytics/src/lynxkite_plugins/graph_analytics/lynxkite_ops.py

@@ -6,13 +6,16 @@ from collections import deque
 import dataclasses
 import functools
 import grandcypher
+import joblib
 import matplotlib
 import networkx as nx
 import pandas as pd
 import polars as pl
 import traceback
 import typing
+import zipfile
 
+mem = joblib.Memory("../joblib-cache")
 ENV = "LynxKite Graph Analytics"
 op = ops.op_registration(ENV)
 
@@ -52,6 +55,8 @@ class Bundle:
         d = dict(graph.nodes(data=True))
         nodes = pd.DataFrame(d.values(), index=d.keys())
         nodes["id"] = nodes.index
+        if "index" in nodes.columns:
+            nodes.drop(columns=["index"], inplace=True)
         return cls(
             dfs={"edges": edges, "nodes": nodes},
             relations=[
@@ -187,6 +192,32 @@ def import_csv(
     )
 
 
+@op("Import GraphML")
+@mem.cache
+def import_graphml(*, filename: str):
+    """Imports a GraphML file."""
+    if filename.endswith(".zip"):
+        with zipfile.ZipFile(filename, "r") as z:
+            for fn in z.namelist():
+                if fn.endswith(".graphml"):
+                    with z.open(fn) as f:
+                        G = nx.read_graphml(f)
+                        break
+            else:
+                raise ValueError("No GraphML file found in the ZIP archive.")
+    else:
+        G = nx.read_graphml(filename)
+    return G
+
+
+@op("Graph from OSM")
+@mem.cache
+def import_osm(*, location: str):
+    import osmnx as ox
+
+    return ox.graph.graph_from_place(location, network_type="drive")
+
+
 @op("Create scale-free graph")
 def create_scale_free_graph(*, nodes: int = 10):
     """Creates a scale-free graph with the given number of nodes."""
@@ -213,6 +244,11 @@ def discard_loop_edges(graph: nx.Graph):
     return graph
 
 
+@op("Discard parallel edges")
+def discard_parallel_edges(graph: nx.Graph):
+    return nx.DiGraph(graph)
+
+
 @op("SQL")
 def sql(bundle: Bundle, *, query: ops.LongStr, save_as: str = "result"):
     """Run a SQL query on the DataFrames in the bundle. Save the results as a new DataFrame."""
@@ -286,7 +322,9 @@ def _map_color(value):
         colors = cmap.colors[: len(categories)]
         return [
             "#{:02x}{:02x}{:02x}".format(int(r * 255), int(g * 255), int(b * 255))
-            for r, g, b in [colors[categories.get_loc(v)] for v in value]
+            for r, g, b in [
+                colors[min(len(colors) - 1, categories.get_loc(v))] for v in value
+            ]
         ]
 
 
@@ -295,10 +333,35 @@ def visualize_graph(graph: Bundle, *, color_nodes_by: ops.NodeAttribute = None):
     nodes = graph.dfs["nodes"].copy()
     if color_nodes_by:
         nodes["color"] = _map_color(nodes[color_nodes_by])
+    for cols in ["x y", "long lat"]:
+        x, y = cols.split()
+        if (
+            x in nodes.columns
+            and nodes[x].dtype == "float64"
+            and y in nodes.columns
+            and nodes[y].dtype == "float64"
+        ):
+            cx, cy = nodes[x].mean(), nodes[y].mean()
+            dx, dy = nodes[x].std(), nodes[y].std()
+            # Scale up to avoid float precision issues and because eCharts omits short edges.
+            scale_x = 100 / max(dx, dy)
+            scale_y = scale_x
+            if y == "lat":
+                scale_y *= -1
+            pos = {
+                node_id: ((row[x] - cx) * scale_x, (row[y] - cy) * scale_y)
+                for node_id, row in nodes.iterrows()
+            }
+            curveness = 0  # Street maps are better with straight streets.
+            break
+    else:
+        pos = nx.spring_layout(
+            graph.to_nx(), iterations=max(1, int(10000 / len(nodes)))
+        )
+        curveness = 0.3
     nodes = nodes.to_records()
     edges = graph.dfs["edges"].drop_duplicates(["source", "target"])
     edges = edges.to_records()
-    pos = nx.spring_layout(graph.to_nx(), iterations=max(1, int(10000 / len(nodes))))
     v = {
         "animationDuration": 500,
         "animationEasingUpdate": "quinticInOut",
@@ -308,7 +371,7 @@ def visualize_graph(graph: Bundle, *, color_nodes_by: ops.NodeAttribute = None):
                 "roam": True,
                 "lineStyle": {
                     "color": "gray",
-                    "curveness": 0.3,
+                    "curveness": curveness,
                 },
                 "emphasis": {
                     "focus": "adjacency",

lynxkite-graph-analytics/src/lynxkite_plugins/graph_analytics/pytorch_model_ops.py

@@ -65,3 +65,11 @@ reg(
         P.basic("lr", 0.001),
     ],
 )
+
+ops.register_passive_op(
+    ENV,
+    "Repeat",
+    inputs=[ops.Input(name="input", position="top", type="tensor")],
+    outputs=[ops.Output(name="output", position="bottom", type="tensor")],
+    params=[ops.Parameter.basic("times", 1, int)],
+)

lynxkite-graph-analytics/uv.lock

@@ -10,26 +10,60 @@ resolution-markers = [
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 ]
 
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-version = "2025.1.31"
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+version = "3.4.1"
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+    { name = "packaging" },
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+    { name = "pyproj" },
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 dependencies = [
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     { name = "nvidia-nvcomp-cu12" },
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 [[package]]
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 version = "24.12.1"
-source = { registry = "https://pypi.nvidia.com/" }
+source = { registry = "https://pypi.org/simple" }
 wheels = [
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 [[package]]
@@ -387,27 +410,29 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
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+    { name = "joblib" },
     { name = "lynxkite-core" },
     { name = "matplotlib" },
     { name = "networkx" },
-    { name = "nx-cugraph-cu12" },
+    { name = "osmnx" },
     { name = "pandas" },
     { name = "polars", extra = ["gpu"] },
 ]
 
 [package.optional-dependencies]
 gpu = [
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+    { name = "nx-cugraph-cu12" },
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 [package.metadata]
 requires-dist = [
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-    { name = "httpx", marker = "extra == 'gpu'" },
+    { name = "joblib", specifier = ">=1.4.2" },
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     { name = "matplotlib", specifier = ">=3.10.0" },
     { name = "networkx", specifier = ">=3.4.2" },
-    { name = "nx-cugraph-cu12", specifier = ">=24.12.0" },
+    { name = "nx-cugraph-cu12", marker = "extra == 'gpu'", specifier = ">=24.12.0" },
+    { name = "osmnx", specifier = ">=2.0.1" },
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     { name = "polars", extras = ["gpu"], specifier = ">=1.14.0" },
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 [[package]]
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 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 },
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
 ]
 
 [[package]]
@@ -900,3 +1025,12 @@ sdist = { url = "https://files.pythonhosted.org/packages/e1/34/943888654477a574a
 wheels = [
     { url = "https://files.pythonhosted.org/packages/a6/ab/7e5f53c3b9d14972843a647d8d7a853969a58aecc7559cb3267302c94774/tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd", size = 346586 },
 ]
+
+[[package]]
+name = "urllib3"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/63/e53da845320b757bf29ef6a9062f5c669fe997973f966045cb019c3f4b66/urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d", size = 307268 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c8/19/4ec628951a74043532ca2cf5d97b7b14863931476d117c471e8e2b1eb39f/urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df", size = 128369 },
+]

lynxkite-pillow-example/pyproject.toml

@@ -5,6 +5,7 @@ description = "An example LynxKite plugin that wraps some Pillow image processin
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
+    "fsspec>=2025.2.0",
     "lynxkite-core",
     "pillow>=11.1.0",
 ]

lynxkite-pillow-example/src/lynxkite_plugins/pillow_example/__init__.py

@@ -4,6 +4,7 @@ from lynxkite.core import ops
 from lynxkite.core.executors import one_by_one
 from PIL import Image, ImageFilter
 import base64
+import fsspec
 import io
 
 ENV = "Pillow"
@@ -13,12 +14,15 @@ one_by_one.register(ENV, cache=False)
 
 @op("Open image")
 def open_image(*, filename: str):
-    return Image.open(filename)
+    with fsspec.open(filename, "rb") as f:
+        data = io.BytesIO(f.read())
+    return Image.open(data)
 
 
 @op("Save image")
 def save_image(image: Image, *, filename: str):
-    image.save(filename)
+    with fsspec.open(filename, "wb") as f:
+        image.save(f)
 
 
 @op("Crop")
@@ -59,7 +63,7 @@ def to_grayscale(image: Image):
 @op("View image", view="image")
 def view_image(image: Image):
     buffered = io.BytesIO()
-    image.save(buffered, format="JPEG")
+    image.save(buffered, format="webp")
     b64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
     data_url = "data:image/jpeg;base64," + b64
     return data_url

lynxkite-pillow-example/uv.lock

@@ -1,22 +1,33 @@
 version = 1
 requires-python = ">=3.11"
 
+[[package]]
+name = "fsspec"
+version = "2025.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/79/68612ed99700e6413de42895aa725463e821a6b3be75c87fcce1b4af4c70/fsspec-2025.2.0.tar.gz", hash = "sha256:1c24b16eaa0a1798afa0337aa0db9b256718ab2a89c425371f5628d22c3b6afd", size = 292283 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e2/94/758680531a00d06e471ef649e4ec2ed6bf185356a7f9fbfbb7368a40bd49/fsspec-2025.2.0-py3-none-any.whl", hash = "sha256:9de2ad9ce1f85e1931858535bc882543171d197001a0a5eb2ddc04f1781ab95b", size = 184484 },
+]
+
 [[package]]
 name = "lynxkite-core"
 version = "0.1.0"
 source = { virtual = "../lynxkite-core" }
 
 [[package]]
-name = "lynxkite-pillow"
+name = "lynxkite-pillow-example"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "fsspec" },
     { name = "lynxkite-core" },
     { name = "pillow" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "fsspec", specifier = ">=2025.2.0" },
     { name = "lynxkite-core", virtual = "../lynxkite-core" },
     { name = "pillow", specifier = ">=11.1.0" },
 ]