removed redundent model dropdown label

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

app.py

@@ -105,7 +105,7 @@ def create_application():
                     if task_dropdown.value in model.tasks
                 ],
                 interactive=True,
-                label="Matching Mammal models",
+                label="",
                 visible=False,
                 scale=10,
             )

gradio_app.ipynb

@@ -0,0 +1,740 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv pip list|grep  mammal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from mammal.examples.dti_bindingdb_kd.task import DtiBindingdbKdTask\n",
+    "from mammal.keys import CLS_PRED, SCORES\n",
+    "from mammal.model import Mammal\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# input\n",
+    "target_seq = \"NLMKRCTRGFRKLGKCTTLEEEKCKTLYPRGQCTCSDSKMNTHSCDCKSC\"\n",
+    "drug_seq = \"CC(=O)NCCC1=CNc2c1cc(OC)cc2\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Load Model\n",
+    "model = Mammal.from_pretrained(\"ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd\")\n",
+    "model.eval()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Load Tokenizer\n",
+    "tokenizer_op = ModularTokenizerOp.from_pretrained(\"ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# convert to MAMMAL style\n",
+    "sample_dict = {\"target_seq\": target_seq, \"drug_seq\": drug_seq}\n",
+    "sample_dict = DtiBindingdbKdTask.data_preprocessing(\n",
+    "    sample_dict=sample_dict,\n",
+    "    tokenizer_op=tokenizer_op,\n",
+    "    target_sequence_key=\"target_seq\",\n",
+    "    drug_sequence_key=\"drug_seq\",\n",
+    "    norm_y_mean=None,\n",
+    "    norm_y_std=None,\n",
+    "    device=model.device,\n",
+    ")\n",
+    "\n",
+    "sample_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "f\"<@TOKENIZER-TYPE=AA><MASK>\"\\\n",
+    "              f\"<@TOKENIZER-TYPE=AA@MAX-LEN={target_max_seq_length}><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{target_sequence}<SEQUENCE_NATURAL_END>\" \\\n",
+    "              f\"<@TOKENIZER-TYPE=SMILES@MAX-LEN={drug_max_seq_length}><MOLECULAR_ENTITY><MOLECULAR_ENTITY_SMALL_MOLECULE><SEQUENCE_NATURAL_START>{drug_sequence}<SEQUENCE_NATURAL_END>\" \\\n",
+    "              \"<EOS>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# forward pass - encoder_only mode which supports scalar predictions\n",
+    "batch_dict = model.forward_encoder_only([sample_dict])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Post-process the model's output\n",
+    "batch_dict = DtiBindingdbKdTask.process_model_output(\n",
+    "    batch_dict,\n",
+    "    scalars_preds_processed_key=\"model.out.dti_bindingdb_kd\",\n",
+    "    norm_y_mean=5.79384684128215,\n",
+    "    norm_y_std=1.33808027428196,\n",
+    ")\n",
+    "ans = {\n",
+    "    \"model.out.dti_bindingdb_kd\": float(batch_dict[\"model.out.dti_bindingdb_kd\"][0])\n",
+    "}\n",
+    "\n",
+    "# Print prediction\n",
+    "print(f\"{ans=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sample_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GRADIO app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import torch\n",
+    "from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp\n",
+    "from mammal.examples.dti_bindingdb_kd.task import DtiBindingdbKdTask\n",
+    "from mammal.keys import *\n",
+    "from mammal.model import Mammal\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path doesn't exist. Will try to download fron hf hub. pretrained_model_name_or_path='ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd'\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8afb628360ae435395dd34ba644945b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 9 files:   0%|          | 0/9 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load model from dir: pretrained_model_name_or_path='/Users/matann/.cache/huggingface/hub/models--ibm--biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd/snapshots/81aa7e8935b01de596fd1820708171a8663bd8d7'\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54b0bd1230d749ec95d7a5a95804b680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The OrderedVocab you are attempting to save contains holes for indices [314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500], your vocabulary could be corrupted !\n",
+      "The OrderedVocab you are attempting to save contains holes for indices [314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526], your vocabulary could be corrupted !\n",
+      "The OrderedVocab you are attempting to save contains holes for indices [314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 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3405, 3406, 3407, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521], your vocabulary could be corrupted !\n",
+      "The OrderedVocab you are attempting to save contains holes for indices [314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 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1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 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2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 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2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 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4899, 4900, 4901, 4902, 4903, 4904, 4905, 4906, 4907, 4908, 4909, 4910, 4911, 4912, 4913, 4914, 4915, 4916, 4917, 4918, 4919, 4920, 4921, 4922, 4923, 4924, 4925, 4926, 4927, 4928, 4929, 4930, 4931, 4932, 4933, 4934, 4935, 4936, 4937, 4938, 4939, 4940, 4941, 4942, 4943, 4944, 4945, 4946, 4947, 4948, 4949, 4950, 4951, 4952, 4953, 4954, 4955, 4956, 4957, 4958, 4959, 4960, 4961, 4962, 4963, 4964, 4965, 4966, 4967, 4968, 4969, 4970, 4971, 4972, 4973, 4974, 4975, 4976, 4977, 4978, 4979, 4980, 4981, 4982, 4983, 4984, 4985, 4986, 4987, 4988, 4989, 4990, 4991, 4992, 4993, 4994, 4995, 4996, 4997, 4998, 4999], your vocabulary could be corrupted !\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "ppi = \"Protein-Protein Interaction (PPI)\"\n",
+    "dti = \"Drug-Target Binding Affinity\"\n",
+    "\n",
+    "\n",
+    "model_paths={ppi:\"ibm/biomed.omics.bl.sm.ma-ted-458m\",dti: \"ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd\"}\n",
+    "# Load Models\n",
+    "models=dict()\n",
+    "tokenizer_op = dict()\n",
+    "for model_type in [ppi,dti]:\n",
+    "    models[model_type] = Mammal.from_pretrained(model_paths[model_type])\n",
+    "    models[model_type].eval()\n",
+    "    # Load Tokenizer\n",
+    "    tokenizer_op[model_type] = ModularTokenizerOp.from_pretrained(model_paths[model_type])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Load Model\n",
+    "# model_dti = Mammal.from_pretrained()\n",
+    "# model_dti.eval()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "#token for positive binding\n",
+    "positive_token_id=tokenizer_op[ppi].get_token_id(\"<1>\")\n",
+    "\n",
+    "# Default input proteins\n",
+    "protein_calmodulin = \"MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQDGFIDKEDLHDGDGKISFEEFLNLVNKEMTADVDGDGQVNYEEFVTMMTSK\"\n",
+    "protein_calcineurin = \"MSSKLLLAGLDIERVLAEKNFYKEWDTWIIEAMNVGDEEVDRIKEFKEDEIFEEAKTLGTAEMQEYKKQKLEEAIEGAFDIFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIRQMWDQNGDWDRIKELKFGEIKKLSAKDTRGTIFIKVFENLGTGVDSEYEDVSKYMLKHQ\"\n",
+    "\n",
+    "\n",
+    "# input\n",
+    "target_seq = \"NLMKRCTRGFRKLGKCTTLEEEKCKTLYPRGQCTCSDSKMNTHSCDCKSC\"\n",
+    "drug_seq = \"CC(=O)NCCC1=CNc2c1cc(OC)cc2\"\n",
+    "\n",
+    "def format_prompt_ppi(prot1,prot2):\n",
+    "    # Formatting prompt to match pre-training syntax\n",
+    "    return f\"<@TOKENIZER-TYPE=AA><BINDING_AFFINITY_CLASS><SENTINEL_ID_0><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot1}<SEQUENCE_NATURAL_END><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot2}<SEQUENCE_NATURAL_END><EOS>\"\n",
+    "def format_prompt_dti(prot,drug):\n",
+    "    sample_dict = {\"target_seq\": target_seq, \"drug_seq\": drug_seq}\n",
+    "    sample_dict = DtiBindingdbKdTask.data_preprocessing(\n",
+    "        sample_dict=sample_dict,\n",
+    "        tokenizer_op=tokenizer_op[dti],\n",
+    "        target_sequence_key=\"target_seq\",\n",
+    "        drug_sequence_key=\"drug_seq\",\n",
+    "        norm_y_mean=None,\n",
+    "        norm_y_std=None,\n",
+    "        device=models[dti].device,\n",
+    "    )\n",
+    "    return sample_dict\n",
+    "\n",
+    "def run_prompt(prompt):\n",
+    "    # Create and load sample\n",
+    "    sample_dict = dict()\n",
+    "    sample_dict[ENCODER_INPUTS_STR] = prompt\n",
+    "\n",
+    "    # Tokenize\n",
+    "    sample_dict=tokenizer_op[ppi](\n",
+    "        sample_dict=sample_dict,\n",
+    "        key_in=ENCODER_INPUTS_STR,\n",
+    "        key_out_tokens_ids=ENCODER_INPUTS_TOKENS,\n",
+    "        key_out_attention_mask=ENCODER_INPUTS_ATTENTION_MASK,\n",
+    "    )\n",
+    "    sample_dict[ENCODER_INPUTS_TOKENS] = torch.tensor(sample_dict[ENCODER_INPUTS_TOKENS])\n",
+    "    sample_dict[ENCODER_INPUTS_ATTENTION_MASK] = torch.tensor(sample_dict[ENCODER_INPUTS_ATTENTION_MASK])\n",
+    "\n",
+    "\n",
+    "    # Generate Prediction\n",
+    "    batch_dict = models[ppi].generate(\n",
+    "        [sample_dict],\n",
+    "        output_scores=True,\n",
+    "        return_dict_in_generate=True,\n",
+    "        max_new_tokens=5,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "    # Get output\n",
+    "    generated_output = tokenizer_op[ppi]._tokenizer.decode(batch_dict[CLS_PRED][0])\n",
+    "    score = batch_dict['model.out.scores'][0][1][positive_token_id].item()\n",
+    "    \n",
+    "    return generated_output,score\n",
+    "\n",
+    "def create_and_run_prompt(prot1, prot2):\n",
+    "    prompt = format_prompt_ppi(prot1, prot2)\n",
+    "    res=prompt, *run_prompt(prompt=prompt)\n",
+    "    return res\n",
+    "\n",
+    "def create_and_run_prompt_dtb(prot, drug):\n",
+    "    sample_dict = format_prompt_dti(prot, drug)\n",
+    "    # Post-process the model's output\n",
+    "    # batch_dict = model_dti.forward_encoder_only([sample_dict])\n",
+    "    batch_dict = models[dti].forward_encoder_only([sample_dict])\n",
+    "    batch_dict = DtiBindingdbKdTask.process_model_output(\n",
+    "        batch_dict,\n",
+    "        scalars_preds_processed_key=\"model.out.dti_bindingdb_kd\",\n",
+    "        norm_y_mean=5.79384684128215,\n",
+    "        norm_y_std=1.33808027428196,\n",
+    "    )\n",
+    "    ans = [\n",
+    "        \"model.out.dti_bindingdb_kd\", float(batch_dict[\"model.out.dti_bindingdb_kd\"][0])\n",
+    "    ]\n",
+    "    res=sample_dict[\"data.query.encoder_input\"], *ans\n",
+    "    return res\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ppi_demo():\n",
+    "    markup_text = f\"\"\"\n",
+    "# Mammal based Protein-Protein Interaction (PPI) demonstration\n",
+    "\n",
+    "Given two protein sequences, estimate if the proteins interact or not.\n",
+    "\n",
+    "### Using the model from \n",
+    "\n",
+    " ```{model_paths[ppi]} ```\n",
+    "\"\"\"\n",
+    "    with gr.Group() as ppi_demo:\n",
+    "        gr.Markdown(markup_text)\n",
+    "        with gr.Row():\n",
+    "            prot1 = gr.Textbox(\n",
+    "                label=\"Protein 1 sequence\",\n",
+    "                # info=\"standard\",\n",
+    "                interactive=True,\n",
+    "                lines=3,\n",
+    "                value=protein_calmodulin,\n",
+    "            )\n",
+    "            prot2 = gr.Textbox(\n",
+    "                label=\"Protein 2 sequence\",\n",
+    "                # info=\"standard\",\n",
+    "                interactive=True,\n",
+    "                lines=3,\n",
+    "                value=protein_calcineurin,\n",
+    "            )\n",
+    "        with gr.Row():\n",
+    "            run_mammal = gr.Button(\"Run Mammal prompt for Protein-Protein Interaction\",variant='primary')\n",
+    "        with gr.Row():\n",
+    "            prompt_box = gr.Textbox(label=\"Mammal prompt\",lines=5)\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            decoded = gr.Textbox(label=\"Mammal output\")\n",
+    "            run_mammal.click(\n",
+    "                fn=create_and_run_prompt,\n",
+    "                inputs=[prot1,prot2],\n",
+    "                outputs=[prompt_box,decoded,gr.Number(label='PPI score')]\n",
+    "            )\n",
+    "        with gr.Row():\n",
+    "            gr.Markdown(\"```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interacting and ```<0>``` for non-interacting\")\n",
+    "        ppi_demo.visible=False\n",
+    "        return ppi_demo\n",
+    "    \n",
+    "    \n",
+    "def create_tdb_demo():\n",
+    "    markup_text = f\"\"\"\n",
+    "# Mammal based Target-Drug binding affinity demonstration\n",
+    "\n",
+    "Given a protein sequence and a drug (in SMILES), estimate the binding affinity.\n",
+    "\n",
+    "### Using the model from \n",
+    "\n",
+    " ```{model_paths[dti]} ```\n",
+    "\"\"\"\n",
+    "    with gr.Group() as tdb_demo:\n",
+    "        gr.Markdown(markup_text)\n",
+    "        with gr.Row():\n",
+    "            prot = gr.Textbox(\n",
+    "                label=\"Protein sequence\",\n",
+    "                # info=\"standard\",\n",
+    "                interactive=True,\n",
+    "                lines=3,\n",
+    "                value=target_seq\n",
+    "            )\n",
+    "            drug = gr.Textbox(\n",
+    "                label=\"drug sequence (SMILES)\",\n",
+    "                # info=\"standard\",\n",
+    "                interactive=True,\n",
+    "                lines=3,\n",
+    "                value=drug_seq,\n",
+    "            )\n",
+    "        with gr.Row():\n",
+    "            run_mammal = gr.Button(\"Run Mammal prompt for Protein-Protein Interaction\",variant='primary')\n",
+    "        with gr.Row():\n",
+    "            prompt_box = gr.Textbox(label=\"Mammal prompt\",lines=5)\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            decoded = gr.Textbox(label=\"Mammal output\")\n",
+    "            run_mammal.click(\n",
+    "                fn=create_and_run_prompt_dtb,\n",
+    "                inputs=[prot,drug],\n",
+    "                outputs=[prompt_box,decoded,gr.Number(label='DTI score')]\n",
+    "            )\n",
+    "        with gr.Row():\n",
+    "            gr.Markdown(\"```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interacting and ```<0>``` for non-interacting\")\n",
+    "        tdb_demo.visible=False\n",
+    "        return tdb_demo\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def create_application():\n",
+    "    \n",
+    "\n",
+    "    with gr.Blocks() as demo:\n",
+    "        main_dropdown = gr.Dropdown(choices=[\"select demo\",ppi,dti])\n",
+    "        main_dropdown.interactive=True\n",
+    "        ppi_demo = create_ppi_demo()\n",
+    "        dtb_demo = create_tdb_demo()\n",
+    "        def set_ppi_vis(main_text):\n",
+    "            return gr.Group(visible=main_text==ppi),gr.Group(visible=main_text==dti)\n",
+    "        main_dropdown.change(set_ppi_vis, inputs=main_dropdown, outputs=[ppi_demo,dtb_demo])\n",
+    "        \n",
+    "            \n",
+    "        # # part II\n",
+    "            \n",
+    "        # gr.Markdown(markup_text)\n",
+    "        # with gr.Row():\n",
+    "        #     target = gr.Textbox(\n",
+    "        #         label=\"target sequence\",\n",
+    "        #         # info=\"standard\",\n",
+    "        #         interactive=True,\n",
+    "        #         lines=1,\n",
+    "        #         value=\"NLMKRCTRGFRKLGKCTTLEEEKCKTLYPRGQCTCSDSKMNTHSCDCKSC\",\n",
+    "        #     )\n",
+    "        #     drug = gr.Textbox(\n",
+    "        #         label=\"drug sequence\",\n",
+    "        #         # info=\"standard\",\n",
+    "        #         interactive=True,\n",
+    "        #         lines=1,\n",
+    "        #         value=\"CC(=O)NCCC1=CNc2c1cc(OC)cc2\",\n",
+    "        #     )\n",
+    "        # with gr.Row():\n",
+    "        #     dt_run_mammal = gr.Button(\"Run Mammal prompt for drug-target binding affinity\",variant='primary')\n",
+    "        # with gr.Row():\n",
+    "        #     dt_prompt_box = gr.Textbox(label=\"Mammal prompt\",lines=5)\n",
+    "        \n",
+    "        # with gr.Row():\n",
+    "        #     dt_decoded = gr.Textbox(label=\"Mammal output\")\n",
+    "        #     dt_run_mammal.click(\n",
+    "        #         fn=create_and_run_prompt,\n",
+    "        #         inputs=[target,drug],\n",
+    "        #         outputs=[dt_prompt_box,dt_decoded,gr.Number(label='PPI score')]\n",
+    "        #     )\n",
+    "        # with gr.Row():\n",
+    "        #     gr.Markdown(\"```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interacting and ```<0>``` for non-interacting\")\n",
+    "            \n",
+    "    return demo\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7865\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7865/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "demo = create_application()\n",
+    "\n",
+    "demo.launch(show_error=True, share=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# demo.blocks[7].show_label = True\n",
+    "demo.children\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = i+1\n",
+    "print(i)\n",
+    "demo.blocks[i].label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demo.blocks[7].value = \"changed\"\n",
+    "demo.blocks[7].visible = True  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr.close_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demo.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demo.visible=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demo.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dropdown_change(val):\n",
+    "    if val==\"other\":\n",
+    "        visibility=False\n",
+    "    else:\n",
+    "        visibility=True\n",
+    "    return gr.Radio([\"y\",\"N\",val], interactive=True, visible=visibility)\n",
+    "\n",
+    "with gr.Blocks() as d2:\n",
+    "    n1 = gr.Radio([\"y\",\"N\"])\n",
+    "    n3=gr.Radio([\"nothing\",\"here\"])\n",
+    "    @gr.render(inputs=[n1])\n",
+    "    def do_something(text, outputs=[n3]):\n",
+    "        print(text)\n",
+    "        # gr.Radio([text,\"not\"])\n",
+    "        # n3=gr.Button(\"bbb\")\n",
+    "    dr1 = gr.Dropdown(choices=[\"one\",\"two\",\"other\"], )\n",
+    "    dr1.interactive=True\n",
+    "    \n",
+    "    b1=gr.Button(\"asf\",interactive=True,)\n",
+    "    b1.click(dropdown_change, inputs=[n1], outputs=[n3] )\n",
+    "    \n",
+    "    dr1.change(fn=dropdown_change, inputs=[dr1], outputs=[n1])\n",
+    "    \n",
+    "d2.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n1=gr.Radio(gr.update(n1, kwargs={\"interactive\": False}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr.update(n1, kwargs={\"interactive\": False})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n1.visible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mammal",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

mammal_demo/ibm_theme.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+from typing import Iterable
+import gradio as gr
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+from gradio.themes.utils.colors import Color
+
+
+IBM_blue = Color(
+    name="blue",
+    c50="#edf5ff",
+    c100="#d0e2ff",
+    c200="#bfdbfe",
+    c300="#a6c8ff",
+    c400="#78a9ff",
+    c500="#4589ff",
+    c600="#0f62fe",
+    c700="#0043ce",
+    c800="#002d9c",
+    c900="#001d6c",
+    c950="#001141",
+)
+class IBMTheme(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = IBM_blue,
+        # secondary_hue: colors.Color | str = colors.blue,
+        # neutral_hue: colors.Color | str = colors.blue,
+        # spacing_size: sizes.Size | str = sizes.spacing_md,
+        # radius_size: sizes.Size | str = sizes.radius_md,
+        # text_size: sizes.Size | str = sizes.text_lg,
+        # font: fonts.Font
+        # | str
+        # | Iterable[fonts.Font | str] = (
+        #     fonts.GoogleFont("Quicksand"),
+        #     "ui-sans-serif",
+        #     "sans-serif",
+        # ),
+        font_mono: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            # secondary_hue=secondary_hue,
+            # neutral_hue=neutral_hue,
+            # spacing_size=spacing_size,
+            # radius_size=radius_size,
+            # text_size=text_size,
+            # font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            body_background_fill="#8d8d8d @ 12%",
+            body_text_color_dark="white",
+            body_text_color="#161616",
+            body_background_fill_dark="",
+            # button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            # button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
+            button_primary_text_color="white",
+            button_primary_text_color_dark="white",
+            # button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            # slider_color="*secondary_300",
+            # slider_color_dark="*secondary_600",
+            # block_title_text_weight="600",
+            block_border_width="3px",
+            block_border_color=IBM_blue.c500,
+            block_info_text_color="#990000",
+            # button_primary_shadow="*shadow_drop_lg",
+            # button_large_padding="32px",
+        )