diff --git a/Classifier/__init__.py b/Classifier/__init__.py
new file mode 100644
index 0000000..8b13789
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@@ -0,0 +1 @@
+
diff --git a/Classifier/dict/index_v1.json b/Classifier/dict/index_v1.json
index ed24a5c..4622696 100644
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+++ b/Classifier/dict/index_v1.json
@@ -1 +1,6720 @@
-{"Pathway": {"Alkaloids": 0, "Amino acids and Peptides": 1, "Carbohydrates": 2, "Fatty acids": 3, "Polyketides": 4, "Shikimates and Phenylpropanoids": 5, "Terpenoids": 6}, "Superclass": {"Alkylresorsinols": 0, "Amino acid glycosides": 1, "Aminosugars and aminoglycosides": 2, "Anthranilic acid alkaloids": 3, "Apocarotenoids": 4, "Aromatic polyketides": 5, "Carotenoids (C40)": 6, "Carotenoids (C45)": 7, "Carotenoids (C50)": 8, "Chromanes": 9, "Coumarins": 10, "Cyclic polyketides": 11, "Diarylheptanoids": 12, "Diazotetronic acids and derivatives": 13, "Diphenyl ethers (DPEs)": 14, "Diterpenoids": 15, "Docosanoids": 16, "Eicosanoids": 17, "Fatty Acids and Conjugates": 18, "Fatty acyl glycosides": 19, "Fatty acyls": 20, "Fatty amides": 21, "Fatty esters": 22, "Flavonoids": 23, "Fluorenes": 24, "Glycerolipids": 25, "Glycerophospholipids": 26, "Guanidine alkaloids": 27, "Histidine alkaloids": 28, "Isoflavonoids": 29, "Lignans": 30, "Linear polyketides": 31, "Lysine alkaloids": 32, "Macrolides": 33, "Meroterpenoids": 34, "Miscellaneous alkaloids": 35, "Miscellaneous polyketides": 36, "Mitomycin derivatives": 37, "Monoterpenoids": 38, "Mycosporine derivatives": 39, "Naphthalenes": 40, "Nicotinic acid alkaloids": 41, "Nucleosides": 42, "Octadecanoids": 43, "Oligopeptides": 44, "Ornithine alkaloids": 45, "Peptide alkaloids": 46, "Phenanthrenoids": 47, "Phenolic acids (C6-C1)": 48, "Phenylethanoids (C6-C2)": 49, "Phenylpropanoids (C6-C3)": 50, "Phloroglucinols": 51, "Polycyclic aromatic polyketides": 52, "Polyethers": 53, "Polyols": 54, "Polyprenols": 55, "Proline alkaloids": 56, "Pseudoalkaloids": 57, "Pseudoalkaloids (transamidation)": 58, "Saccharides": 59, "Serine alkaloids": 60, "Sesquiterpenoids": 61, "Sesterterpenoids": 62, "Small peptides": 63, "Spingolipids": 64, "Steroids": 65, "Stilbenoids": 66, "Styrylpyrones": 67, "Terphenyls": 68, "Tetramate alkaloids": 69, "Triterpenoids": 70, "Tropolones": 71, "Tryptophan alkaloids": 72, "Tyrosine alkaloids": 73, "Xanthones": 74, "\u03b2-lactams": 75, "\u03b3-lactam-\u03b2-lactones": 76}, "Class": {"12-oxophytodienoic acid metabolites": 0, "2-arylbenzofurans": 1, "2-pyrone derivatives": 2, "3-Decalinoyltetramic acids": 3, "3-Spirotetramic acids": 4, "3-acyl tetramic acids": 5, "3-oligoenoyltetramic acids": 6, "4-pyrone derivatives": 7, "Abeoabietane diterpenoids": 8, "Abeolupane triterpenoids": 9, "Abeotaxane diterpenoids": 10, "Abietane diterpenoids": 11, "Acetate-derived alkaloids": 12, "Acetogenins": 13, "Acidic glycosphingolipids": 14, "Acorane sesquiterpenoids": 15, "Acridone alkaloids": 16, "Actinomycins": 17, "Acutumine alkaloids": 18, "Acyclic guanidine alkaloids": 19, "Acyclic monoterpenoids": 20, "Acyclic triterpenoids": 21, "Acyl phloroglucinols": 22, "Adianane triterpenoids": 23, "Aeruginosins": 24, "Aflatoxins": 25, "Africanane sesquiterpenoids": 26, "Agarofuran sesquiterpenoids": 27, "Ahp-containing cyclodepsipeptides": 28, "Alliacane sesquiterpenoids": 29, "Allohimachalane sesquiterpenoids": 30, "Amarylidaceae alkaloids": 31, "Amino cyclitols": 32, "Amino fatty acids": 33, "Aminoacids": 34, "Aminoglycosides": 35, "Aminosugars": 36, "Amphilectane diterpenoids": 37, "Anabaenopeptins": 38, "Androstane steroids": 39, "Angucyclines": 40, "Ansa macrolides": 41, "Ansa peptide alkaloids": 42, "Anthocyanidins": 43, "Anthracyclines": 44, "Anthranillic acid derivatives": 45, "Anthraquinones and anthrones": 46, "Antimycins": 47, "Aphidicolane diterpenoids": 48, "Aplysiatoxins": 49, "Apocarotenoids (C30, \u03a8-\u03a8)": 50, "Apocarotenoids (\u03a8-)": 51, "Apocarotenoids (\u03b2-)": 52, "Apocarotenoids(\u03b5-)": 53, "Aporphine alkaloids": 54, "Apotirucallane triterpenoids": 55, "Aristolane sesquiterpenoids": 56, "Aromadendrane sesquiterpenoids": 57, "Aromatic polyketides with side chains": 58, "Arteminisin": 59, "Arylnaphthalene and aryltetralin lignans": 60, "Asbestinane diterpenoids": 61, "Ascarosides": 62, "Ascomycins and Rapamycins": 63, "Asperane sesterterpenoids": 64, "Aspidosperma type": 65, "Aspidosperma-Iboga hybrid type (Vinca alkaloids)": 66, "Asteriscane sesquiterpenoids": 67, "Atisane diterpenoids": 68, "Aurones": 69, "Avermectins": 70, "Azaphilones": 71, "Azo and Azoxy alkaloids": 72, "Baccharane triterpenoids": 73, "Bactoprenols": 74, "Bafilomycins": 75, "Bagremycins": 76, "Bauerane triterpenoids": 77, "Benastatins and derivatives": 78, "Benzodiazepine alkaloids": 79, "Benzophenones": 80, "Benzoquinones": 81, "Bergamotane sesquiterpenoids": 82, "Betaestacin-type sesterterpenoids": 83, "Betalain alkaloids": 84, "Beyerane diterpenoids": 85, "Biaryl type diarylheptanoids": 86, "Bicyclic guanidine alkaloids": 87, "Bicyclogermacrane sesquiterpenoids": 88, "Bicyclohumulane sesquiterpenoids": 89, "Bisabolane sesquiterpenoids": 90, "Bisnaphthalenes": 91, "Bleomycins": 92, "Boromycins": 93, "Botryane sesquiterpenoids": 94, "Bourbonane sesquiterpenoids": 95, "Branched fatty acids": 96, "Brasilane sesquiterpenoids": 97, "Breviane diterpenoids": 98, "Briarane diterpenoids": 99, "Bryostatins": 100, "Bufadienolides": 101, "CDP-Glycerols": 102, "Cadinane sesquiterpenoids": 103, "Camphane monoterpenoids": 104, "Campherenane sesquiterpenoids": 105, "Cannabinoids": 106, "Capnellane sesquiterpenoids": 107, "Capsaicins and Capsaicinoids": 108, "Carabrane sesquiterpenoids": 109, "Carane monoterpenoids": 110, "Carbapenems": 111, "Carbazole alkaloids": 112, "Carbocyclic fatty acids": 113, "Carboline alkaloids": 114, "Cardenolides": 115, "Carotenoids (C40, \u03a7-\u03a7)": 116, "Carotenoids (C40, \u03a7-\u03a8)": 117, "Carotenoids (C40, \u03a8-\u03a8)": 118, "Carotenoids (C40, \u03b2-\u03a7)": 119, "Carotenoids (C40, \u03b2-\u03a8)": 120, "Carotenoids (C40, \u03b2-\u03b2)": 121, "Carotenoids (C40, \u03b2-\u03b3)": 122, "Carotenoids (C40, \u03b2-\u03b5)": 123, "Carotenoids (C40, \u03b2-\u03ba)": 124, "Carotenoids (C40, \u03b2-\u03c0)": 125, "Carotenoids (C40, \u03b3-\u03a8)": 126, "Carotenoids (C40, \u03b3-\u03b5)": 127, "Carotenoids (C40, \u03b5-\u03a8)": 128, "Carotenoids (C40, \u03b5-\u03b5)": 129, "Carotenoids (C40, \u03ba-\u03a7)": 130, "Carotenoids (C40, \u03ba-\u03ba)": 131, "Carotenoids (C40, \u03c0-\u03a7)": 132, "Carotenoids (C40, \u03c0-\u03a8)": 133, "Carotenoids (C40, \u03c0-\u03c0)": 134, "Carotenoids (C45, \u03a8-\u03a8)": 135, "Carotenoids (C45, \u03b2-\u03a8)": 136, "Carotenoids (C45, \u03b5-\u03a8)": 137, "Carotenoids (C50, \u03a8-\u03a8)": 138, "Carotenoids (C50, \u03b2-\u03a8)": 139, "Carotenoids (C50, \u03b2-\u03b2)": 140, "Carotenoids (C50, \u03b3-\u03b3)": 141, "Carotenoids (C50, \u03b5-\u03b5)": 142, "Caryolane sesquiterpenoids": 143, "Caryophyllane sesquiterpenoids": 144, "Casbane diterpenoids": 145, "Cassane diterpenoids": 146, "Catechols with side chains": 147, "Cedrane and Isocedrane sesquiterpenoids": 148, "Cembrane diterpenoids": 149, "Cephalosporins": 150, "Cephalotaxus alkaloids": 151, "Cephamycins": 152, "Ceramides": 153, "Cericerane sesterterpenoids": 154, "Chalcones": 155, "Chamigrane sesquiterpenoids": 156, "Cheilanthane sesterterpenoids": 157, "Chiloscyphane sesquiterpenoids": 158, "Cholane steroids": 159, "Cholestane steroids": 160, "Chromones": 161, "Cinnamic acid amides": 162, "Cinnamic acids and derivatives": 163, "Cinnamoyl phenols": 164, "Clavams": 165, "Clavulones": 166, "Cleistanthane diterpenoids": 167, "Clovane sesquiterpenoids": 168, "Colensane and Clerodane diterpenoids": 169, "Coloratane sesquiterpenoids": 170, "Copaane sesquiterpenoids": 171, "Copacamphane sesquiterpenoids": 172, "Corynanthe type": 173, "Coumarinolignans": 174, "Coumaronochromones": 175, "Coumestan": 176, "Cryptophycins": 177, "Cubebane sesquiterpenoids": 178, "Cucurbitane triterpenoids": 179, "Cuparane sesquiterpenoids": 180, "Cyano esters": 181, "Cyanogenic glycosides": 182, "Cyathane diterpenoids": 183, "Cyclic peptides": 184, "Cyclitols": 185, "Cycloabietane diterpenoids": 186, "Cycloamphilectane diterpenoids": 187, "Cycloapotirucallane triterpenoids": 188, "Cycloartane triterpenoids": 189, "Cyclobisabolane sesquiterpenoids": 190, "Cycloeudesmane sesquiterpenoids": 191, "Cyclofarnesane sesquiterpenoids": 192, "Cyclogermacrane sesquiterpenoids": 193, "Cyclolaurane sesquiterpenoids": 194, "Cyclonerane sesquiterpenoids": 195, "Cyclophytane diterpenoids": 196, "Cyclopiane diterpenoids": 197, "Cyclopiazonic acid-tpye tetramate alkaloids": 198, "Cytochalasan alkaloids": 199, "Cytosporins": 200, "DKXanthenes and derivatives": 201, "Dactylomelane diterpenoids": 202, "Dammarane and Protostane triterpenoids": 203, "Daphnane diterpenoids": 204, "Daucane sesquiterpenoids": 205, "Decalins with 2-pyrones": 206, "Decalins with side chains": 207, "Decipiane diterpenoids": 208, "Depsides": 209, "Depsidones": 210, "Depsipeptides": 211, "Devadarane diterpenoids": 212, "Diacylglycerols": 213, "Dialkylresorcinols": 214, "Diarylether type diarylheptanoids": 215, "Dibenzocyclooctadienes lignans": 216, "Dibenzylbutane lignans": 217, "Dibenzylbutyrolactone lignans": 218, "Dicarboxylic acids": 219, "Dihydroflavonols": 220, "Dimeric phloroglucinols": 221, "Dipeptides": 222, "Disaccharides": 223, "Docosa-1,2-dioxolanes": 224, "Dolabellane diterpenoids": 225, "Dolastane diterpenoids": 226, "Dolichols": 227, "Drimane sesquiterpenoids": 228, "Duclauxin and derivatives": 229, "Dunniane sesquiterpenoids": 230, "Ecdysteroids": 231, "Eicosa-1,2-dioxolanes": 232, "Elemane sesquiterpenoids": 233, "Elfamycins": 234, "Enediynes": 235, "Epothilones": 236, "Epoxy fatty acids": 237, "Epoxyeicosatrienoic acids": 238, "Eremane diterpenoids": 239, "Eremophilane sesquiterpenoids": 240, "Ergostane steroids": 241, "Ergot alkaloids": 242, "Ericamycins": 243, "Erythromycins": 244, "Erythroxylane diterpenoids": 245, "Estrane steroids": 246, "Eudesmane sesquiterpenoids": 247, "Eunicellane diterpenoids": 248, "Farnesane sesquiterpenoids": 249, "Fasamycins and derivatives": 250, "Fatty acid estolides": 251, "Fatty acyl CoAs": 252, "Fatty acyl carnitines": 253, "Fatty acyl glycosides of mono- and disaccharides": 254, "Fatty acyl homoserine lactones": 255, "Fatty alcohols": 256, "Fatty aldehydes": 257, "Fatty ethers": 258, "Fatty nitriles": 259, "Fenchane monoterpenoids": 260, "Fernane and Arborinane triterpenoids": 261, "Filicane triterpenoids": 262, "Flavan-3-ols": 263, "Flavandiols (Leucoanthocyanidins)": 264, "Flavanones": 265, "Flavans": 266, "Flavones": 267, "Flavonolignans": 268, "Flavonols": 269, "Flavonostilbenes": 270, "Friedelane triterpenoids": 271, "Fukinane sesquiterpenoids": 272, "Fungal DPEs": 273, "Fungal cyclic polyketides (Miscellaneous)": 274, "Furanoabietane diterpenoids": 275, "Furanoid lignans": 276, "Furans": 277, "Furocoumarins": 278, "Furofuranoid lignans": 279, "Furostane steroids": 280, "Fusicoccane diterpenoids": 281, "Fusidane triterpenoids": 282, "Gallotannins": 283, "Gammacerane triterpenoids": 284, "Germacrane sesquiterpenoids": 285, "Gersemiane diterpenoids": 286, "Gibberellins": 287, "Glucosinolates": 288, "Glutinane triterpenoids": 289, "Glycerophosphates": 290, "Glycerophosphocholines": 291, "Glycerophosphoethanolamines": 292, "Glycerophosphoglycerols": 293, "Glycerophosphoglycerophosphates": 294, "Glycerophosphoglycerophosphoglycerols": 295, "Glycerophosphoinositol phosphates": 296, "Glycerophosphoinositolglycans": 297, "Glycerophosphoinositols": 298, "Glycerophosphoserines": 299, "Glycosyldiacylglycerols": 300, "Glycosylglycerophospholipids": 301, "Glycosylmonoacylglycerols": 302, "Gorgonane sesquiterpenoids": 303, "Grayanotoxane diterpenoids": 304, "Griseofulvins": 305, "Guaiane sesquiterpenoids": 306, "Guanacastane diterpenoids": 307, "Gymnomitrane sesquiterpenoids": 308, "Halimane diterpenoids": 309, "Halogenated hydrocarbons": 310, "Hamigerane sesquiterpenoids": 311, "Hapalindole alkaloids": 312, "Hasubanan alkaloids": 313, "Hepoxilins": 314, "Herbertane sesquiterpenoids": 315, "Heterocyclic fatty acids": 316, "Himachalane sesquiterpenoids": 317, "Hirsutane sesquiterpenoids": 318, "Homoerythrina alkaloids": 319, "Homofarnesane sesquiterpenoids": 320, "Hopane and Moretane triterpenoids": 321, "Humbertiane sesquiterpenoids": 322, "Humulane sesquiterpenoids": 323, "Hydrocarbons": 324, "Hydroperoxy fatty acids": 325, "Hydroxy fatty acids": 326, "Hydroxy-hydroperoxyeicosapentaenoic acids": 327, "Hydroxy-hydroperoxyeicosatetraenoic acids": 328, "Hydroxy-hydroperoxyeicosatrienoic acids": 329, "Iboga type": 330, "Icetexane diterpenoids": 331, "Illudalane sesquiterpenoids": 332, "Illudane sesquiterpenoids": 333, "Imidazole alkaloids": 334, "Indole diketopiperazine alkaloids (L-Trp, L-Ala)": 335, "Indole diketopiperazine alkaloids (L-Trp, L-Pro)": 336, "Indole diketopiperazine alkaloids (L-Trp, L-Trp)": 337, "Indole-Diterpenoid alkaloids (Penitrems)": 338, "Indolizidine alkaloids": 339, "Ingenane diterpenoids": 340, "Iphionane sesquiterpenoids": 341, "Iridoids monoterpenoids": 342, "Irregular monoterpenoids": 343, "Isariotin alkaloids": 344, "Ishwarane sesquiterpenoids": 345, "Isoaurones": 346, "Isocomane sesquiterpenoids": 347, "Isocoumarins": 348, "Isodaucane sesquiterpenoids": 349, "Isoflavanones": 350, "Isoflavones": 351, "Isofurans": 352, "Isoindole alkaloids": 353, "Isolactarane sesquiterpenoids": 354, "Isoprostanes": 355, "Isoquinoline alkaloids": 356, "Ivaxillarane sesquiterpenoids": 357, "Jasmonic acids": 358, "Jatrophane diterpenoids": 359, "Jatropholane diterpenoids": 360, "Kaurane and Phyllocladane diterpenoids": 361, "Kavalactones and derivatives": 362, "Kempane diterpenoids": 363, "Labdane diterpenoids": 364, "Lactam bearing macrolide lactones": 365, "Lactarane sesquiterpenoids": 366, "Lactones": 367, "Ladder polyethers": 368, "Lanostane, Tirucallane and Euphane triterpenoids": 369, "Lathyrane diterpenoids": 370, "Laurane sesquiterpenoids": 371, "Leukotrienes": 372, "Levuglandins": 373, "Limonoids": 374, "Linear diarylheptanoids": 375, "Linear peptides": 376, "Linear polyenes": 377, "Linear sesterterpenoids": 378, "Linear tetronates": 379, "Lipopeptides": 380, "Lipoxins": 381, "Lippifoliane sesquiterpenoids": 382, "Lobane diterpenoids": 383, "Long-Chain Bicyclic Phosphotriester": 384, "Longibornane sesquiterpenoids": 385, "Longifolane sesquiterpenoids": 386, "Longipinane sesquiterpenoids": 387, "Luminacins and derivatives": 388, "Lupane triterpenoids": 389, "Macrocyclic tetramic acids": 390, "Macrolide lactams": 391, "Macrolide lactones": 392, "Macrotetrolides": 393, "Malabaricane triterpenoids": 394, "Mangicol-type sesterterpenoids": 395, "Marasmane sesquiterpenoids": 396, "Maresins": 397, "Marine-bacterial DPEs": 398, "Megastigmanes": 399, "Melithiazole and Myxothiazole derivatives": 400, "Menthane monoterpenoids": 401, "Merohemiterpenoids": 402, "Meromonoterpenoids": 403, "Merosesquiterpenoids": 404, "Meroterpenoids with 5- or 6-membered ring": 405, "Meroterpenoids with bridged ring": 406, "Methoxy fatty acids": 407, "Methyl xanthones": 408, "Microcolins and mirabimids": 409, "Microcystins": 410, "Microginins": 411, "Minor lignans": 412, "Miscellaneous alkaloids": 413, "Miscellaneous apocarotenoids": 414, "Miscellaneous meroterpenoids": 415, "Miscellaneous polyketides": 416, "Mitomycins": 417, "Monacolins and Monacolin derivatives": 418, "Monoacylglycerols": 419, "Monoalkylresorcinols": 420, "Monocarbocyclic sesterterpenoids": 421, "Monocyclic guanidine alkaloids": 422, "Monocyclic monoterpenoids": 423, "Monocyclic \u03b2-lactams": 424, "Monomeric stilbenes": 425, "Monosaccharides": 426, "Morphinan alkaloids": 427, "Mulinane diterpenoids": 428, "Multiflorane triterpenoids": 429, "Mycolic acids": 430, "Mycosporine and Mycosporine-like amino acids": 431, "Myrsinane diterpenoids": 432, "N-acyl amines": 433, "N-acyl ethanolamines (endocannabinoids)": 434, "Nagilactone diterpenoids": 435, "Naphthalenes and derivatives": 436, "Naphthalenones": 437, "Naphthoquinones": 438, "Nardosinane sesquiterpenoids": 439, "Neoflavonoids": 440, "Neohopane triterpenoids": 441, "Neolignans": 442, "Neurofurans": 443, "Neuroprostanes": 444, "Neutral glycosphingolipids": 445, "Nitro fatty acids": 446, "Nonadrides": 447, "Norcembrane diterpenoids": 448, "Noreremophilane sesquiterpenoids": 449, "Noreudesmane sesquiterpenoids": 450, "Norkaurane diterpenoids": 451, "Norlabdane diterpenoids": 452, "Norpimarane and Norisopimarane diterpenoids": 453, "Norsesterterpenoids": 454, "Oblogolides": 455, "Obtusane diterpenoids": 456, "Oleanane triterpenoids": 457, "Oligomeric phloroglucinols (phlorotannins)": 458, "Oligomeric stibenes": 459, "Oligomycins": 460, "Onocerane triterpenoids": 461, "Open-chain polyketides": 462, "Open-chained neoflavonoids": 463, "Ophiobolane sesterterpenoids": 464, "Oplopane sesquiterpenoids": 465, "Oppositane sesquiterpenoids": 466, "Orthosomycins": 467, "Other Docosanoids": 468, "Other Eicosanoids": 469, "Other Octadecanoids": 470, "Other indole diketopiperazine alkaloids": 471, "Other polyketide meroterpenoids": 472, "Oxa-Bridged Macrolides": 473, "Oxasqualenoids": 474, "Oxazole alkaloids": 475, "Oxidized glycerophospholipids": 476, "Oxo fatty acids": 477, "Oxygenated hydrocarbons": 478, "Pachydictyane diterpenoids": 479, "Pachysanane triterpenoids": 480, "Pacifigorgiane sesquiterpenoids": 481, "Panasinsane sesquiterpenoids": 482, "Paraconic acids and derivatives": 483, "Paraliane diterpenoids": 484, "Parguerane diterpenoids": 485, "Patchoulane sesquiterpenoids": 486, "Paulomycins and derivatives": 487, "Penicillins": 488, "Pentacyclic guanidine alkaloids": 489, "Pentalenane sesquiterpenoids": 490, "Pepluane diterpenoids": 491, "Peptaibols": 492, "Perforane sesquiterpenoids": 493, "Phenalens": 494, "Phenanthrenes": 495, "Phenazine alkaloids": 496, "Phenethylisoquinoline alkaloids": 497, "Phenoxazine alkaloids": 498, "Phenylalanine-derived alkaloids": 499, "Phenylethanoids": 500, "Phenylethylamines": 501, "Phloroglucinol-terpene hybrids": 502, "Phoslactomycins or Phosphazomycins": 503, "Phosphosphingolipids": 504, "Phthalide derivatives": 505, "Phytane diterpenoids": 506, "Phytofurans": 507, "Phytoprostanes": 508, "Picrotoxane sesquiterpenoids": 509, "Pimarane and Isopimarane diterpenoids": 510, "Pimprinine alkaloids": 511, "Pinane monoterpenoids": 512, "Pinguisane sesquiterpenoids": 513, "Piperidine alkaloids": 514, "Plant xanthones": 515, "Platensimycin and Platencins": 516, "Podocarpane diterpenoids": 517, "Polyamines": 518, "Polyene macrolides": 519, "Polyesters": 520, "Polyether ionophores": 521, "Polypodane triterpenoids": 522, "Polyprenol derivatives": 523, "Polyprenylated cyclic polyketides (Hop meroterpenoids)": 524, "Polysaccharides": 525, "Pradimicins": 526, "Pregnane steroids": 527, "Premyrsinane diterpenoids": 528, "Prenyl quinone meroterpenoids": 529, "Prenylated,geranylated phloroglucinols": 530, "Prenylbisabolane diterpenoids": 531, "Prenyleudesmane diterpenoids": 532, "Presilphiperfolane and Probotryane sesquiterpenoids": 533, "Prezizaane sesquiterpenoids": 534, "Primary amides": 535, "Proanthocyanins": 536, "Prodigiosins": 537, "Prostaglandins": 538, "Protoberberine alkaloids": 539, "Protoilludane sesquiterpenoids": 540, "Protopine alkaloids": 541, "Pseudoguaiane sesquiterpenoids": 542, "Pseudopterane diterpenoids": 543, "Pterocarpan": 544, "Pulvinones": 545, "Purine alkaloids": 546, "Purine nucleos(t)ides": 547, "Pyranocoumarins": 548, "Pyrazine and Piperazine alkaloids": 549, "Pyridine alkaloids": 550, "Pyrimidine nucleos(t)ides": 551, "Pyrrocidine tetramate alkaloids": 552, "Pyrrole alkaloids": 553, "Pyrrolidine alkaloids": 554, "Pyrrolizidine alkaloids": 555, "Pyrroloindole alkaloids": 556, "Pyrroloquinoline alkaloids": 557, "Quadrane sesquiterpenoids": 558, "Quassinoids": 559, "Quinazoline alkaloids": 560, "Quinoline alkaloids": 561, "Quinolizidine alkaloids": 562, "Resin glycosides": 563, "Resolvin Ds": 564, "Resolvin Es": 565, "Rhamnofolane diterpenoids": 566, "Rhamnolipids": 567, "Rhizoxins": 568, "RiPPs-Amatoxins and Phallotoxins": 569, "RiPPs-Bottromycins": 570, "RiPPs-Cyanobactins": 571, "RiPPs-Lanthipeptides": 572, "RiPPs-Lasso peptides": 573, "RiPPs-Microcins": 574, "RiPPs-Thiopeptides": 575, "Rotenoids": 576, "Rotundane sesquiterpenoids": 577, "Sacculatane diterpenoids": 578, "Salinosporamides": 579, "Santalane sesquiterpenoids": 580, "Saponaceolide triterpenoids": 581, "Sativane sesquiterpenoids": 582, "Saxitoxins": 583, "Scalarane sesterterpenoids": 584, "Secoabietane diterpenoids": 585, "Secochamigrane sesquiterpenoids": 586, "Secoeudesmane sesquiterpenoids": 587, "Secogermacrane sesquiterpenoids": 588, "Secoiridoid monoterpenoids": 589, "Secokaurane diterpenoids": 590, "Secolabdane diterpenoids": 591, "Segetane diterpenoids": 592, "Selaginellins": 593, "Serratane triterpenoids": 594, "Serrulatane and Biflorane diterpenoids": 595, "Shikimic acids and derivatives": 596, "Shionane triterpenoids": 597, "Silphinane sesquiterpenoids": 598, "Silphiperfolane sesquiterpenoids": 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\ No newline at end of file
+{
+  "Pathway": {
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+    "Amino acids and Peptides": 1,
+    "Carbohydrates": 2,
+    "Fatty acids": 3,
+    "Polyketides": 4,
+    "Shikimates and Phenylpropanoids": 5,
+    "Terpenoids": 6
+  },
+  "Superclass": {
+    "Alkylresorsinols": 0,
+    "Amino acid glycosides": 1,
+    "Aminosugars and aminoglycosides": 2,
+    "Anthranilic acid alkaloids": 3,
+    "Apocarotenoids": 4,
+    "Aromatic polyketides": 5,
+    "Carotenoids (C40)": 6,
+    "Carotenoids (C45)": 7,
+    "Carotenoids (C50)": 8,
+    "Chromanes": 9,
+    "Coumarins": 10,
+    "Cyclic polyketides": 11,
+    "Diarylheptanoids": 12,
+    "Diazotetronic acids and derivatives": 13,
+    "Diphenyl ethers (DPEs)": 14,
+    "Diterpenoids": 15,
+    "Docosanoids": 16,
+    "Eicosanoids": 17,
+    "Fatty Acids and Conjugates": 18,
+    "Fatty acyl glycosides": 19,
+    "Fatty acyls": 20,
+    "Fatty amides": 21,
+    "Fatty esters": 22,
+    "Flavonoids": 23,
+    "Fluorenes": 24,
+    "Glycerolipids": 25,
+    "Glycerophospholipids": 26,
+    "Guanidine alkaloids": 27,
+    "Histidine alkaloids": 28,
+    "Isoflavonoids": 29,
+    "Lignans": 30,
+    "Linear polyketides": 31,
+    "Lysine alkaloids": 32,
+    "Macrolides": 33,
+    "Meroterpenoids": 34,
+    "Miscellaneous alkaloids": 35,
+    "Miscellaneous polyketides": 36,
+    "Mitomycin derivatives": 37,
+    "Monoterpenoids": 38,
+    "Mycosporine derivatives": 39,
+    "Naphthalenes": 40,
+    "Nicotinic acid alkaloids": 41,
+    "Nucleosides": 42,
+    "Octadecanoids": 43,
+    "Oligopeptides": 44,
+    "Ornithine alkaloids": 45,
+    "Peptide alkaloids": 46,
+    "Phenanthrenoids": 47,
+    "Phenolic acids (C6-C1)": 48,
+    "Phenylethanoids (C6-C2)": 49,
+    "Phenylpropanoids (C6-C3)": 50,
+    "Phloroglucinols": 51,
+    "Polycyclic aromatic polyketides": 52,
+    "Polyethers": 53,
+    "Polyols": 54,
+    "Polyprenols": 55,
+    "Proline alkaloids": 56,
+    "Pseudoalkaloids": 57,
+    "Pseudoalkaloids (transamidation)": 58,
+    "Saccharides": 59,
+    "Serine alkaloids": 60,
+    "Sesquiterpenoids": 61,
+    "Sesterterpenoids": 62,
+    "Small peptides": 63,
+    "Spingolipids": 64,
+    "Steroids": 65,
+    "Stilbenoids": 66,
+    "Styrylpyrones": 67,
+    "Terphenyls": 68,
+    "Tetramate alkaloids": 69,
+    "Triterpenoids": 70,
+    "Tropolones": 71,
+    "Tryptophan alkaloids": 72,
+    "Tyrosine alkaloids": 73,
+    "Xanthones": 74,
+    "\u03b2-lactams": 75,
+    "\u03b3-lactam-\u03b2-lactones": 76
+  },
+  "Class": {
+    "12-oxophytodienoic acid metabolites": 0,
+    "2-arylbenzofurans": 1,
+    "2-pyrone derivatives": 2,
+    "3-Decalinoyltetramic acids": 3,
+    "3-Spirotetramic acids": 4,
+    "3-acyl tetramic acids": 5,
+    "3-oligoenoyltetramic acids": 6,
+    "4-pyrone derivatives": 7,
+    "Abeoabietane diterpenoids": 8,
+    "Abeolupane triterpenoids": 9,
+    "Abeotaxane diterpenoids": 10,
+    "Abietane diterpenoids": 11,
+    "Acetate-derived alkaloids": 12,
+    "Acetogenins": 13,
+    "Acidic glycosphingolipids": 14,
+    "Acorane sesquiterpenoids": 15,
+    "Acridone alkaloids": 16,
+    "Actinomycins": 17,
+    "Acutumine alkaloids": 18,
+    "Acyclic guanidine alkaloids": 19,
+    "Acyclic monoterpenoids": 20,
+    "Acyclic triterpenoids": 21,
+    "Acyl phloroglucinols": 22,
+    "Adianane triterpenoids": 23,
+    "Aeruginosins": 24,
+    "Aflatoxins": 25,
+    "Africanane sesquiterpenoids": 26,
+    "Agarofuran sesquiterpenoids": 27,
+    "Ahp-containing cyclodepsipeptides": 28,
+    "Alliacane sesquiterpenoids": 29,
+    "Allohimachalane sesquiterpenoids": 30,
+    "Amarylidaceae alkaloids": 31,
+    "Amino cyclitols": 32,
+    "Amino fatty acids": 33,
+    "Aminoacids": 34,
+    "Aminoglycosides": 35,
+    "Aminosugars": 36,
+    "Amphilectane diterpenoids": 37,
+    "Anabaenopeptins": 38,
+    "Androstane steroids": 39,
+    "Angucyclines": 40,
+    "Ansa macrolides": 41,
+    "Ansa peptide alkaloids": 42,
+    "Anthocyanidins": 43,
+    "Anthracyclines": 44,
+    "Anthranillic acid derivatives": 45,
+    "Anthraquinones and anthrones": 46,
+    "Antimycins": 47,
+    "Aphidicolane diterpenoids": 48,
+    "Aplysiatoxins": 49,
+    "Apocarotenoids (C30, \u03a8-\u03a8)": 50,
+    "Apocarotenoids (\u03a8-)": 51,
+    "Apocarotenoids (\u03b2-)": 52,
+    "Apocarotenoids(\u03b5-)": 53,
+    "Aporphine alkaloids": 54,
+    "Apotirucallane triterpenoids": 55,
+    "Aristolane sesquiterpenoids": 56,
+    "Aromadendrane sesquiterpenoids": 57,
+    "Aromatic polyketides with side chains": 58,
+    "Arteminisin": 59,
+    "Arylnaphthalene and aryltetralin lignans": 60,
+    "Asbestinane diterpenoids": 61,
+    "Ascarosides": 62,
+    "Ascomycins and Rapamycins": 63,
+    "Asperane sesterterpenoids": 64,
+    "Aspidosperma type": 65,
+    "Aspidosperma-Iboga hybrid type (Vinca alkaloids)": 66,
+    "Asteriscane sesquiterpenoids": 67,
+    "Atisane diterpenoids": 68,
+    "Aurones": 69,
+    "Avermectins": 70,
+    "Azaphilones": 71,
+    "Azo and Azoxy alkaloids": 72,
+    "Baccharane triterpenoids": 73,
+    "Bactoprenols": 74,
+    "Bafilomycins": 75,
+    "Bagremycins": 76,
+    "Bauerane triterpenoids": 77,
+    "Benastatins and derivatives": 78,
+    "Benzodiazepine alkaloids": 79,
+    "Benzophenones": 80,
+    "Benzoquinones": 81,
+    "Bergamotane sesquiterpenoids": 82,
+    "Betaestacin-type sesterterpenoids": 83,
+    "Betalain alkaloids": 84,
+    "Beyerane diterpenoids": 85,
+    "Biaryl type diarylheptanoids": 86,
+    "Bicyclic guanidine alkaloids": 87,
+    "Bicyclogermacrane sesquiterpenoids": 88,
+    "Bicyclohumulane sesquiterpenoids": 89,
+    "Bisabolane sesquiterpenoids": 90,
+    "Bisnaphthalenes": 91,
+    "Bleomycins": 92,
+    "Boromycins": 93,
+    "Botryane sesquiterpenoids": 94,
+    "Bourbonane sesquiterpenoids": 95,
+    "Branched fatty acids": 96,
+    "Brasilane sesquiterpenoids": 97,
+    "Breviane diterpenoids": 98,
+    "Briarane diterpenoids": 99,
+    "Bryostatins": 100,
+    "Bufadienolides": 101,
+    "CDP-Glycerols": 102,
+    "Cadinane sesquiterpenoids": 103,
+    "Camphane monoterpenoids": 104,
+    "Campherenane sesquiterpenoids": 105,
+    "Cannabinoids": 106,
+    "Capnellane sesquiterpenoids": 107,
+    "Capsaicins and Capsaicinoids": 108,
+    "Carabrane sesquiterpenoids": 109,
+    "Carane monoterpenoids": 110,
+    "Carbapenems": 111,
+    "Carbazole alkaloids": 112,
+    "Carbocyclic fatty acids": 113,
+    "Carboline alkaloids": 114,
+    "Cardenolides": 115,
+    "Carotenoids (C40, \u03a7-\u03a7)": 116,
+    "Carotenoids (C40, \u03a7-\u03a8)": 117,
+    "Carotenoids (C40, \u03a8-\u03a8)": 118,
+    "Carotenoids (C40, \u03b2-\u03a7)": 119,
+    "Carotenoids (C40, \u03b2-\u03a8)": 120,
+    "Carotenoids (C40, \u03b2-\u03b2)": 121,
+    "Carotenoids (C40, \u03b2-\u03b3)": 122,
+    "Carotenoids (C40, \u03b2-\u03b5)": 123,
+    "Carotenoids (C40, \u03b2-\u03ba)": 124,
+    "Carotenoids (C40, \u03b2-\u03c0)": 125,
+    "Carotenoids (C40, \u03b3-\u03a8)": 126,
+    "Carotenoids (C40, \u03b3-\u03b5)": 127,
+    "Carotenoids (C40, \u03b5-\u03a8)": 128,
+    "Carotenoids (C40, \u03b5-\u03b5)": 129,
+    "Carotenoids (C40, \u03ba-\u03a7)": 130,
+    "Carotenoids (C40, \u03ba-\u03ba)": 131,
+    "Carotenoids (C40, \u03c0-\u03a7)": 132,
+    "Carotenoids (C40, \u03c0-\u03a8)": 133,
+    "Carotenoids (C40, \u03c0-\u03c0)": 134,
+    "Carotenoids (C45, \u03a8-\u03a8)": 135,
+    "Carotenoids (C45, \u03b2-\u03a8)": 136,
+    "Carotenoids (C45, \u03b5-\u03a8)": 137,
+    "Carotenoids (C50, \u03a8-\u03a8)": 138,
+    "Carotenoids (C50, \u03b2-\u03a8)": 139,
+    "Carotenoids (C50, \u03b2-\u03b2)": 140,
+    "Carotenoids (C50, \u03b3-\u03b3)": 141,
+    "Carotenoids (C50, \u03b5-\u03b5)": 142,
+    "Caryolane sesquiterpenoids": 143,
+    "Caryophyllane sesquiterpenoids": 144,
+    "Casbane diterpenoids": 145,
+    "Cassane diterpenoids": 146,
+    "Catechols with side chains": 147,
+    "Cedrane and Isocedrane sesquiterpenoids": 148,
+    "Cembrane diterpenoids": 149,
+    "Cephalosporins": 150,
+    "Cephalotaxus alkaloids": 151,
+    "Cephamycins": 152,
+    "Ceramides": 153,
+    "Cericerane sesterterpenoids": 154,
+    "Chalcones": 155,
+    "Chamigrane sesquiterpenoids": 156,
+    "Cheilanthane sesterterpenoids": 157,
+    "Chiloscyphane sesquiterpenoids": 158,
+    "Cholane steroids": 159,
+    "Cholestane steroids": 160,
+    "Chromones": 161,
+    "Cinnamic acid amides": 162,
+    "Cinnamic acids and derivatives": 163,
+    "Cinnamoyl phenols": 164,
+    "Clavams": 165,
+    "Clavulones": 166,
+    "Cleistanthane diterpenoids": 167,
+    "Clovane sesquiterpenoids": 168,
+    "Colensane and Clerodane diterpenoids": 169,
+    "Coloratane sesquiterpenoids": 170,
+    "Copaane sesquiterpenoids": 171,
+    "Copacamphane sesquiterpenoids": 172,
+    "Corynanthe type": 173,
+    "Coumarinolignans": 174,
+    "Coumaronochromones": 175,
+    "Coumestan": 176,
+    "Cryptophycins": 177,
+    "Cubebane sesquiterpenoids": 178,
+    "Cucurbitane triterpenoids": 179,
+    "Cuparane sesquiterpenoids": 180,
+    "Cyano esters": 181,
+    "Cyanogenic glycosides": 182,
+    "Cyathane diterpenoids": 183,
+    "Cyclic peptides": 184,
+    "Cyclitols": 185,
+    "Cycloabietane diterpenoids": 186,
+    "Cycloamphilectane diterpenoids": 187,
+    "Cycloapotirucallane triterpenoids": 188,
+    "Cycloartane triterpenoids": 189,
+    "Cyclobisabolane sesquiterpenoids": 190,
+    "Cycloeudesmane sesquiterpenoids": 191,
+    "Cyclofarnesane sesquiterpenoids": 192,
+    "Cyclogermacrane sesquiterpenoids": 193,
+    "Cyclolaurane sesquiterpenoids": 194,
+    "Cyclonerane sesquiterpenoids": 195,
+    "Cyclophytane diterpenoids": 196,
+    "Cyclopiane diterpenoids": 197,
+    "Cyclopiazonic acid-tpye tetramate alkaloids": 198,
+    "Cytochalasan alkaloids": 199,
+    "Cytosporins": 200,
+    "DKXanthenes and derivatives": 201,
+    "Dactylomelane diterpenoids": 202,
+    "Dammarane and Protostane triterpenoids": 203,
+    "Daphnane diterpenoids": 204,
+    "Daucane sesquiterpenoids": 205,
+    "Decalins with 2-pyrones": 206,
+    "Decalins with side chains": 207,
+    "Decipiane diterpenoids": 208,
+    "Depsides": 209,
+    "Depsidones": 210,
+    "Depsipeptides": 211,
+    "Devadarane diterpenoids": 212,
+    "Diacylglycerols": 213,
+    "Dialkylresorcinols": 214,
+    "Diarylether type diarylheptanoids": 215,
+    "Dibenzocyclooctadienes lignans": 216,
+    "Dibenzylbutane lignans": 217,
+    "Dibenzylbutyrolactone lignans": 218,
+    "Dicarboxylic acids": 219,
+    "Dihydroflavonols": 220,
+    "Dimeric phloroglucinols": 221,
+    "Dipeptides": 222,
+    "Disaccharides": 223,
+    "Docosa-1,2-dioxolanes": 224,
+    "Dolabellane diterpenoids": 225,
+    "Dolastane diterpenoids": 226,
+    "Dolichols": 227,
+    "Drimane sesquiterpenoids": 228,
+    "Duclauxin and derivatives": 229,
+    "Dunniane sesquiterpenoids": 230,
+    "Ecdysteroids": 231,
+    "Eicosa-1,2-dioxolanes": 232,
+    "Elemane sesquiterpenoids": 233,
+    "Elfamycins": 234,
+    "Enediynes": 235,
+    "Epothilones": 236,
+    "Epoxy fatty acids": 237,
+    "Epoxyeicosatrienoic acids": 238,
+    "Eremane diterpenoids": 239,
+    "Eremophilane sesquiterpenoids": 240,
+    "Ergostane steroids": 241,
+    "Ergot alkaloids": 242,
+    "Ericamycins": 243,
+    "Erythromycins": 244,
+    "Erythroxylane diterpenoids": 245,
+    "Estrane steroids": 246,
+    "Eudesmane sesquiterpenoids": 247,
+    "Eunicellane diterpenoids": 248,
+    "Farnesane sesquiterpenoids": 249,
+    "Fasamycins and derivatives": 250,
+    "Fatty acid estolides": 251,
+    "Fatty acyl CoAs": 252,
+    "Fatty acyl carnitines": 253,
+    "Fatty acyl glycosides of mono- and disaccharides": 254,
+    "Fatty acyl homoserine lactones": 255,
+    "Fatty alcohols": 256,
+    "Fatty aldehydes": 257,
+    "Fatty ethers": 258,
+    "Fatty nitriles": 259,
+    "Fenchane monoterpenoids": 260,
+    "Fernane and Arborinane triterpenoids": 261,
+    "Filicane triterpenoids": 262,
+    "Flavan-3-ols": 263,
+    "Flavandiols (Leucoanthocyanidins)": 264,
+    "Flavanones": 265,
+    "Flavans": 266,
+    "Flavones": 267,
+    "Flavonolignans": 268,
+    "Flavonols": 269,
+    "Flavonostilbenes": 270,
+    "Friedelane triterpenoids": 271,
+    "Fukinane sesquiterpenoids": 272,
+    "Fungal DPEs": 273,
+    "Fungal cyclic polyketides (Miscellaneous)": 274,
+    "Furanoabietane diterpenoids": 275,
+    "Furanoid lignans": 276,
+    "Furans": 277,
+    "Furocoumarins": 278,
+    "Furofuranoid lignans": 279,
+    "Furostane steroids": 280,
+    "Fusicoccane diterpenoids": 281,
+    "Fusidane triterpenoids": 282,
+    "Gallotannins": 283,
+    "Gammacerane triterpenoids": 284,
+    "Germacrane sesquiterpenoids": 285,
+    "Gersemiane diterpenoids": 286,
+    "Gibberellins": 287,
+    "Glucosinolates": 288,
+    "Glutinane triterpenoids": 289,
+    "Glycerophosphates": 290,
+    "Glycerophosphocholines": 291,
+    "Glycerophosphoethanolamines": 292,
+    "Glycerophosphoglycerols": 293,
+    "Glycerophosphoglycerophosphates": 294,
+    "Glycerophosphoglycerophosphoglycerols": 295,
+    "Glycerophosphoinositol phosphates": 296,
+    "Glycerophosphoinositolglycans": 297,
+    "Glycerophosphoinositols": 298,
+    "Glycerophosphoserines": 299,
+    "Glycosyldiacylglycerols": 300,
+    "Glycosylglycerophospholipids": 301,
+    "Glycosylmonoacylglycerols": 302,
+    "Gorgonane sesquiterpenoids": 303,
+    "Grayanotoxane diterpenoids": 304,
+    "Griseofulvins": 305,
+    "Guaiane sesquiterpenoids": 306,
+    "Guanacastane diterpenoids": 307,
+    "Gymnomitrane sesquiterpenoids": 308,
+    "Halimane diterpenoids": 309,
+    "Halogenated hydrocarbons": 310,
+    "Hamigerane sesquiterpenoids": 311,
+    "Hapalindole alkaloids": 312,
+    "Hasubanan alkaloids": 313,
+    "Hepoxilins": 314,
+    "Herbertane sesquiterpenoids": 315,
+    "Heterocyclic fatty acids": 316,
+    "Himachalane sesquiterpenoids": 317,
+    "Hirsutane sesquiterpenoids": 318,
+    "Homoerythrina alkaloids": 319,
+    "Homofarnesane sesquiterpenoids": 320,
+    "Hopane and Moretane triterpenoids": 321,
+    "Humbertiane sesquiterpenoids": 322,
+    "Humulane sesquiterpenoids": 323,
+    "Hydrocarbons": 324,
+    "Hydroperoxy fatty acids": 325,
+    "Hydroxy fatty acids": 326,
+    "Hydroxy-hydroperoxyeicosapentaenoic acids": 327,
+    "Hydroxy-hydroperoxyeicosatetraenoic acids": 328,
+    "Hydroxy-hydroperoxyeicosatrienoic acids": 329,
+    "Iboga type": 330,
+    "Icetexane diterpenoids": 331,
+    "Illudalane sesquiterpenoids": 332,
+    "Illudane sesquiterpenoids": 333,
+    "Imidazole alkaloids": 334,
+    "Indole diketopiperazine alkaloids (L-Trp, L-Ala)": 335,
+    "Indole diketopiperazine alkaloids (L-Trp, L-Pro)": 336,
+    "Indole diketopiperazine alkaloids (L-Trp, L-Trp)": 337,
+    "Indole-Diterpenoid alkaloids (Penitrems)": 338,
+    "Indolizidine alkaloids": 339,
+    "Ingenane diterpenoids": 340,
+    "Iphionane sesquiterpenoids": 341,
+    "Iridoids monoterpenoids": 342,
+    "Irregular monoterpenoids": 343,
+    "Isariotin alkaloids": 344,
+    "Ishwarane sesquiterpenoids": 345,
+    "Isoaurones": 346,
+    "Isocomane sesquiterpenoids": 347,
+    "Isocoumarins": 348,
+    "Isodaucane sesquiterpenoids": 349,
+    "Isoflavanones": 350,
+    "Isoflavones": 351,
+    "Isofurans": 352,
+    "Isoindole alkaloids": 353,
+    "Isolactarane sesquiterpenoids": 354,
+    "Isoprostanes": 355,
+    "Isoquinoline alkaloids": 356,
+    "Ivaxillarane sesquiterpenoids": 357,
+    "Jasmonic acids": 358,
+    "Jatrophane diterpenoids": 359,
+    "Jatropholane diterpenoids": 360,
+    "Kaurane and Phyllocladane diterpenoids": 361,
+    "Kavalactones and derivatives": 362,
+    "Kempane diterpenoids": 363,
+    "Labdane diterpenoids": 364,
+    "Lactam bearing macrolide lactones": 365,
+    "Lactarane sesquiterpenoids": 366,
+    "Lactones": 367,
+    "Ladder polyethers": 368,
+    "Lanostane, Tirucallane and Euphane triterpenoids": 369,
+    "Lathyrane diterpenoids": 370,
+    "Laurane sesquiterpenoids": 371,
+    "Leukotrienes": 372,
+    "Levuglandins": 373,
+    "Limonoids": 374,
+    "Linear diarylheptanoids": 375,
+    "Linear peptides": 376,
+    "Linear polyenes": 377,
+    "Linear sesterterpenoids": 378,
+    "Linear tetronates": 379,
+    "Lipopeptides": 380,
+    "Lipoxins": 381,
+    "Lippifoliane sesquiterpenoids": 382,
+    "Lobane diterpenoids": 383,
+    "Long-Chain Bicyclic Phosphotriester": 384,
+    "Longibornane sesquiterpenoids": 385,
+    "Longifolane sesquiterpenoids": 386,
+    "Longipinane sesquiterpenoids": 387,
+    "Luminacins and derivatives": 388,
+    "Lupane triterpenoids": 389,
+    "Macrocyclic tetramic acids": 390,
+    "Macrolide lactams": 391,
+    "Macrolide lactones": 392,
+    "Macrotetrolides": 393,
+    "Malabaricane triterpenoids": 394,
+    "Mangicol-type sesterterpenoids": 395,
+    "Marasmane sesquiterpenoids": 396,
+    "Maresins": 397,
+    "Marine-bacterial DPEs": 398,
+    "Megastigmanes": 399,
+    "Melithiazole and Myxothiazole derivatives": 400,
+    "Menthane monoterpenoids": 401,
+    "Merohemiterpenoids": 402,
+    "Meromonoterpenoids": 403,
+    "Merosesquiterpenoids": 404,
+    "Meroterpenoids with 5- or 6-membered ring": 405,
+    "Meroterpenoids with bridged ring": 406,
+    "Methoxy fatty acids": 407,
+    "Methyl xanthones": 408,
+    "Microcolins and mirabimids": 409,
+    "Microcystins": 410,
+    "Microginins": 411,
+    "Minor lignans": 412,
+    "Miscellaneous alkaloids": 413,
+    "Miscellaneous apocarotenoids": 414,
+    "Miscellaneous meroterpenoids": 415,
+    "Miscellaneous polyketides": 416,
+    "Mitomycins": 417,
+    "Monacolins and Monacolin derivatives": 418,
+    "Monoacylglycerols": 419,
+    "Monoalkylresorcinols": 420,
+    "Monocarbocyclic sesterterpenoids": 421,
+    "Monocyclic guanidine alkaloids": 422,
+    "Monocyclic monoterpenoids": 423,
+    "Monocyclic \u03b2-lactams": 424,
+    "Monomeric stilbenes": 425,
+    "Monosaccharides": 426,
+    "Morphinan alkaloids": 427,
+    "Mulinane diterpenoids": 428,
+    "Multiflorane triterpenoids": 429,
+    "Mycolic acids": 430,
+    "Mycosporine and Mycosporine-like amino acids": 431,
+    "Myrsinane diterpenoids": 432,
+    "N-acyl amines": 433,
+    "N-acyl ethanolamines (endocannabinoids)": 434,
+    "Nagilactone diterpenoids": 435,
+    "Naphthalenes and derivatives": 436,
+    "Naphthalenones": 437,
+    "Naphthoquinones": 438,
+    "Nardosinane sesquiterpenoids": 439,
+    "Neoflavonoids": 440,
+    "Neohopane triterpenoids": 441,
+    "Neolignans": 442,
+    "Neurofurans": 443,
+    "Neuroprostanes": 444,
+    "Neutral glycosphingolipids": 445,
+    "Nitro fatty acids": 446,
+    "Nonadrides": 447,
+    "Norcembrane diterpenoids": 448,
+    "Noreremophilane sesquiterpenoids": 449,
+    "Noreudesmane sesquiterpenoids": 450,
+    "Norkaurane diterpenoids": 451,
+    "Norlabdane diterpenoids": 452,
+    "Norpimarane and Norisopimarane diterpenoids": 453,
+    "Norsesterterpenoids": 454,
+    "Oblogolides": 455,
+    "Obtusane diterpenoids": 456,
+    "Oleanane triterpenoids": 457,
+    "Oligomeric phloroglucinols (phlorotannins)": 458,
+    "Oligomeric stibenes": 459,
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+    "Onocerane triterpenoids": 461,
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+    "Open-chained neoflavonoids": 463,
+    "Ophiobolane sesterterpenoids": 464,
+    "Oplopane sesquiterpenoids": 465,
+    "Oppositane sesquiterpenoids": 466,
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+    "Other Eicosanoids": 469,
+    "Other Octadecanoids": 470,
+    "Other indole diketopiperazine alkaloids": 471,
+    "Other polyketide meroterpenoids": 472,
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+    "Oxasqualenoids": 474,
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+    "Oxo fatty acids": 477,
+    "Oxygenated hydrocarbons": 478,
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+    "Pachysanane triterpenoids": 480,
+    "Pacifigorgiane sesquiterpenoids": 481,
+    "Panasinsane sesquiterpenoids": 482,
+    "Paraconic acids and derivatives": 483,
+    "Paraliane diterpenoids": 484,
+    "Parguerane diterpenoids": 485,
+    "Patchoulane sesquiterpenoids": 486,
+    "Paulomycins and derivatives": 487,
+    "Penicillins": 488,
+    "Pentacyclic guanidine alkaloids": 489,
+    "Pentalenane sesquiterpenoids": 490,
+    "Pepluane diterpenoids": 491,
+    "Peptaibols": 492,
+    "Perforane sesquiterpenoids": 493,
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+    "Phenanthrenes": 495,
+    "Phenazine alkaloids": 496,
+    "Phenethylisoquinoline alkaloids": 497,
+    "Phenoxazine alkaloids": 498,
+    "Phenylalanine-derived alkaloids": 499,
+    "Phenylethanoids": 500,
+    "Phenylethylamines": 501,
+    "Phloroglucinol-terpene hybrids": 502,
+    "Phoslactomycins or Phosphazomycins": 503,
+    "Phosphosphingolipids": 504,
+    "Phthalide derivatives": 505,
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+    "Phytoprostanes": 508,
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+    "Pinguisane sesquiterpenoids": 513,
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+    "Platensimycin and Platencins": 516,
+    "Podocarpane diterpenoids": 517,
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+    "Polypodane triterpenoids": 522,
+    "Polyprenol derivatives": 523,
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+    "Purine nucleos(t)ides": 547,
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+    "Pyrazine and Piperazine alkaloids": 549,
+    "Pyridine alkaloids": 550,
+    "Pyrimidine nucleos(t)ides": 551,
+    "Pyrrocidine tetramate alkaloids": 552,
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+    "RiPPs-Bottromycins": 570,
+    "RiPPs-Cyanobactins": 571,
+    "RiPPs-Lanthipeptides": 572,
+    "RiPPs-Lasso peptides": 573,
+    "RiPPs-Microcins": 574,
+    "RiPPs-Thiopeptides": 575,
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+    "Saponaceolide triterpenoids": 581,
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+    "Secoeudesmane sesquiterpenoids": 587,
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+    "Secolabdane diterpenoids": 591,
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+    "Selaginellins": 593,
+    "Serratane triterpenoids": 594,
+    "Serrulatane and Biflorane diterpenoids": 595,
+    "Shikimic acids and derivatives": 596,
+    "Shionane triterpenoids": 597,
+    "Silphinane sesquiterpenoids": 598,
+    "Silphiperfolane sesquiterpenoids": 599,
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+    "Simple diketopiperazine alkaloids": 604,
+    "Simple indole alkaloids": 605,
+    "Simple oxindole alkaloids": 606,
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+    "Simple tetramate alkaloids": 608,
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+    "Sophorolipids": 610,
+    "Sorbicilinoids": 611,
+    "Sphaerane diterpenoids": 612,
+    "Sphaeroane diterpenoids": 613,
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+    "Sphingoid bases": 615,
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+    "Spirodioxynaphthalenes": 617,
+    "Spirostane steroids": 618,
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+    "Streptothricins and derivatives": 630,
+    "Strobilurins and derivatives": 631,
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+    "Taraxerane triterpenoids": 634,
+    "Taxane diterpenoids": 635,
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+    "Terpenoid tetrahydroisoquinoline alkaloids": 637,
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+    "Thujane monoterpenoids": 648,
+    "Thujopsane sesquiterpenoids": 649,
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+    "Totarane diterpenoids": 651,
+    "Trachylobane diterpenoids": 652,
+    "Tremulane sesquiterpenoids": 653,
+    "Triacylglycerols": 654,
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+    "Trinervitane diterpenoids": 658,
+    "Tripeptides": 659,
+    "Tropane alkaloids": 660,
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+    "Tropolones and derivatives (Shikimate)": 662,
+    "Tylosins": 663,
+    "Unsaturated fatty acids": 664,
+    "Ursane and Taraxastane triterpenoids": 665,
+    "Usnic acid and derivatives": 666,
+    "Valerane sesquiterpenoids": 667,
+    "Valerenane sesquiterpenoids": 668,
+    "Valparane diterpenoids": 669,
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+    "Verrucosane diterpenoids": 671,
+    "Verticillane diterpenoids": 672,
+    "Villanovane diterpenoids": 673,
+    "Viscidane diterpenoids": 674,
+    "Vitamin D2 and derivatives": 675,
+    "Vitamin D3 and derivatives": 676,
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+    "Wax monoesters": 678,
+    "Xeniaphyllane diterpenoids": 679,
+    "Xenicane diterpenoids": 680,
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+    "Zearalenones": 682,
+    "Zizaane sesquiterpenoids": 683,
+    "m-Terphenyls": 684,
+    "p-Terphenyls": 685,
+    "pteridine alkaloids": 686
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+    },
+    "3": {
+      "Pathway": [
+        0
+      ]
+    },
+    "28": {
+      "Pathway": [
+        0
+      ]
+    },
+    "27": {
+      "Pathway": [
+        0
+      ]
+    },
+    "57": {
+      "Pathway": [
+        0,
+        6
+      ]
+    },
+    "46": {
+      "Pathway": [
+        0,
+        1
+      ]
+    },
+    "69": {
+      "Pathway": [
+        0,
+        1
+      ]
+    },
+    "44": {
+      "Pathway": [
+        1,
+        4
+      ]
+    },
+    "63": {
+      "Pathway": [
+        1
+      ]
+    },
+    "75": {
+      "Pathway": [
+        1
+      ]
+    },
+    "76": {
+      "Pathway": [
+        1
+      ]
+    },
+    "1": {
+      "Pathway": [
+        1
+      ]
+    },
+    "59": {
+      "Pathway": [
+        2
+      ]
+    },
+    "54": {
+      "Pathway": [
+        2
+      ]
+    },
+    "2": {
+      "Pathway": [
+        2
+      ]
+    },
+    "42": {
+      "Pathway": [
+        2
+      ]
+    },
+    "35": {
+      "Pathway": [
+        0
+      ]
+    },
+    "36": {
+      "Pathway": [
+        4
+      ]
+    },
+    "37": {
+      "Pathway": [
+        0
+      ]
+    },
+    "58": {
+      "Pathway": [
+        0
+      ]
+    },
+    "60": {
+      "Pathway": [
+        0
+      ]
+    },
+    "47": {
+      "Pathway": [
+        5
+      ]
+    }
+  }
+}
diff --git a/Classifier/prediction_voting.py b/Classifier/prediction_voting.py
index 7fa2166..89a1dcb 100644
--- a/Classifier/prediction_voting.py
+++ b/Classifier/prediction_voting.py
@@ -1,101 +1,74 @@
-
-import itertools 
 import numpy as np
 
-def vote_classification(n_path, 
-                        n_class, 
-                        n_super, 
-                        pred_class, 
-                        pred_super, 
-                        path_from_class, 
-                        path_from_superclass, isglycoside, ontology_dictionary):
-    class_result = []
-    superclass_result = []
-    pathway_result = []
-
-    index = ontology_dictionary
-
-    index_class = list(index['Class'].keys())
-    index_superclass = list(index['Superclass'].keys())
-    index_pathway = list(index['Pathway'].keys())
-
-    path_for_vote = n_path+path_from_class+path_from_superclass
-    path = list(set([ k for k in path_for_vote if path_for_vote.count(k) ==3]))
-
-    if path == []:
-        path = list(set([ k for k in path_for_vote if path_for_vote.count(k) ==2]))
-        if len(path)>1:
-            path = list(set([ k for k in path_for_vote if path_for_vote.count(k) ==2])) 
-    if path == []:
-        for w in n_path:
-            pathway_result.append(index_pathway[w])
-        return pathway_result,superclass_result,class_result,isglycoside
-
-
-    else: #path != []
-        if set(n_path) & set(path) != set():
-            if set(path) & set(path_from_superclass) != set():
-                n_super = [ l for l in n_super if set(path)& set(index['Super_hierarchy'][str(l)]['Pathway']) != set()]
-                if n_super == []:
-                    n_class = [ m for m in n_class if set(path) & set(index['Class_hierarchy'][str(m)]['Pathway']) != set() ]
-                    n_super = [index['Class_hierarchy'][str(n)]['Superclass'] for n in n_class]
-                    n_super = list(set(itertools.chain.from_iterable(n_super)))
-
-                elif len(n_super) > 1: #super != []
-                    n_class = [ u for u in n_class if set(path) & set(index['Class_hierarchy'][str(u)]['Pathway']) != set() ]
-                    if n_class != []:
-                        n_super = [index['Class_hierarchy'][str(v)]['Superclass'] for v in n_class]
-                        n_path = [index['Class_hierarchy'][str(v)]['Pathway'] for v in n_class]
-                        n_path = list(set(itertools.chain.from_iterable(n_path)))
-                        n_super = list(set(itertools.chain.from_iterable(n_super)))
-
-                    elif len(path)==1:
-                        n_super = [np.argmax(pred_super)]
-                        n_class = [ m for m in [np.argmax(pred_class)] if set(n_super) & set(index['Class_hierarchy'][str(m)]['Superclass']) != set() ]
-
-
-                else:
-                    n_class = [ o for o in n_class if set(n_super) & set(index['Class_hierarchy'][str(o)]['Superclass'])!=set() ]
-                    if n_class == []:
-                        n_class = [ m for m in [np.argmax(pred_class)] if set(n_super) & set(index['Class_hierarchy'][str(m)]['Superclass']) != set() ]
-            else:
-                n_class = [ p for p in n_class if  set(path) & set(index['Class_hierarchy'][str(p)]['Pathway']) !=set() ]
-                n_super = [index['Class_hierarchy'][str(q)]['Superclass'] for q in n_class]
-
-                n_super = list(set(itertools.chain.from_iterable(n_super)))
+
+# TODO this can be vastly simplified again
+
+
+def vote_classification(n_path, n_class, n_super,
+                        pred_class, pred_super,
+                        path_from_class, path_from_superclass,
+                        index):
+    """
+    Classify the obtained classes
+
+    @param n_path: predicted pathways above noise
+    @param n_class: predicted classes above noise
+    @param n_super: predicted superclasses above noise
+    @param pred_class: predicted classes (all)
+    @param pred_super: predicted superclasses (all)
+    @param path_from_class: pathways extracted from classes
+    @param path_from_superclass: pathways extracted from superclasses
+    @param index: the "ontology"
+    @return: pathway_result, superclass_result, class_result => the classified results ids
+    """
+
+    pathways_for_vote = list(n_path) + list(path_from_class) + list(path_from_superclass)
+
+    # Select pathways with at least 3 counts
+
+    path = {k for k in pathways_for_vote if pathways_for_vote.count(k) == 3}
+
+    if not path:  # if path is empty, we select pathways with only 2 counts
+        path = {k for k in pathways_for_vote if pathways_for_vote.count(k) == 2}
+
+    if not path:  # if path is still empty
+        # Add all the pathways we already know about, the rest is empty
+        return n_path, [], []
+
+    if path & set(n_path):  # we have at least some of n_path in our path
+        if path & set(path_from_superclass):  # we have at least some of the path from superclass in path
+            n_super = [i for i in n_super if path & set(index['Super_hierarchy'][str(i)]['Pathway'])]
+            n_class = [i for i in n_class if path & set(index['Class_hierarchy'][str(i)]['Pathway'])]
+
+            if not n_super:  # n_super is empty
+                n_super = list({index['Class_hierarchy'][str(n)]['Superclass'] for n in n_class})
+
+            elif len(n_super) > 1:  # super != []
+                if n_class:  # n_class is not empty
+                    n_super = list({j for i in n_class for j in index['Class_hierarchy'][str(i)]['Superclass']})
+                    n_path = list({j for i in n_class for j in index['Class_hierarchy'][str(i)]['Pathway']})
+                elif len(path) == 1:
+                    n_super = [np.argmax(pred_super)]
+                    best_candidate_class_index = np.argmax(pred_class)
+                    if set(n_super) & set(index['Class_hierarchy'][str(best_candidate_class_index)]['Superclass']):
+                        n_class = [best_candidate_class_index]
+
+            else:  # we have only one n_super
+                # now our classes are only the classes from our best superclass
+                n_class = [i for i in n_class if set(n_super) & set(index['Class_hierarchy'][str(i)]['Superclass'])]
+                if not n_class:  # n_class is empty, we take the predicted class with the highest score
+                    best_candidate_class_index = np.argmax(pred_class)
+                    if set(n_super) & set(index['Class_hierarchy'][str(best_candidate_class_index)]['Superclass']):
+                        n_class = [best_candidate_class_index]
 
         else:
-            n_super = [ l for l in n_super if set(path) & set(index['Super_hierarchy'][str(l)]['Pathway']) != set()]
-            if n_super == []:
-                n_class = [ m for m in n_class if set(path) & set(index['Class_hierarchy'][str(m)]['Pathway']) != set()]
-                n_super = [index['Class_hierarchy'][str(n)]['Superclass'] for n in n_class]
-                n_path = [index['Class_hierarchy'][str(v)]['Pathway'] for v in n_class]
-                n_path = list(set(itertools.chain.from_iterable(n_path)))
-                n_super = list(set(itertools.chain.from_iterable(n_super)))
-
-
-            elif len(n_super) > 1: #super != []
-                n_class = [ u for u in n_class if set(path) & set(index['Class_hierarchy'][str(u)]['Pathway']) != set()]
-                n_super = [index['Class_hierarchy'][str(v)]['Superclass'] for v in n_class]
-                n_path = [index['Class_hierarchy'][str(v)]['Pathway'] for v in n_class]
-                n_path = list(set(itertools.chain.from_iterable(n_path)))
-                n_super = list(set(itertools.chain.from_iterable(n_super)))
-
-
-            else:
-                n_class = [ o for o in n_class if set(path) & set(index['Class_hierarchy'][str(o)]['Pathway']) != set() ]
-                n_super = [index['Class_hierarchy'][str(v)]['Superclass'] for v in n_class]
-                n_path = [index['Class_hierarchy'][str(v)]['Pathway'] for v in n_class]
-                n_path = list(set(itertools.chain.from_iterable(n_path)))
-                n_super = list(set(itertools.chain.from_iterable(n_super)))
-
-    for r in path:
-        pathway_result.append(index_pathway[r])
-    for s in n_super:
-        superclass_result.append(index_superclass[s])
-    for t in n_class:
-        class_result.append(index_class[t])
-    
-    return pathway_result,superclass_result,class_result,isglycoside #three class results and glycoside checker result (True/False)
-    
+            n_class = [p for p in n_class if path & set(index['Class_hierarchy'][str(p)]['Pathway'])]
+            n_super = list({index['Class_hierarchy'][str(q)]['Superclass'] for q in n_class})
+
+    else:
+        # Select all the classes that are part of our selected pathways
+        n_class = [m for m in n_class if set(path) & set(index['Class_hierarchy'][str(m)]['Pathway'])]
+        n_super = list({index['Class_hierarchy'][str(n)]['Superclass'] for n in n_class})
+        n_path = list({index['Class_hierarchy'][str(v)]['Pathway'] for v in n_class})
 
+    return path, n_super, n_class  # We have to check if we want path or n_path here!
diff --git a/app.py b/app.py
index a629d54..2ebcb7b 100644
--- a/app.py
+++ b/app.py
@@ -4,22 +4,15 @@
 import dash_bootstrap_components as dbc
 import dash_html_components as html
 import dash_table
-import plotly.express as px
 from dash.dependencies import Input, Output
-import os
-from zipfile import ZipFile
 import urllib.parse
 from flask import Flask, request
 import json
-import pandas as pd
 import requests
-import numpy as np
 import urllib.parse
 
-import sys
-sys.path.insert(0, "Classifier")
-import fingerprint_handler
-import prediction_voting
+from classification import classify_structure
+from models import ClassifyEntity
 
 server = Flask(__name__)
 app = dash.Dash(__name__, server=server, external_stylesheets=[dbc.themes.BOOTSTRAP])
@@ -27,10 +20,6 @@
 
 server = app.server
 
-
-from models import ClassifyEntity
-
-
 ontology_dictionary = json.loads(open("Classifier/dict/index_v1.json").read())
 
 NAVBAR = dbc.Navbar(
@@ -42,11 +31,14 @@
         dbc.Nav(
             [
                 dbc.NavItem(dbc.NavLink("NP Classifier", href="#")),
-                dbc.NavItem(dbc.NavLink("Report Feedback", href="https://docs.google.com/forms/d/e/1FAIpQLSf1-sw-P0SQGokyeaOpEmLda0UPJW93qkrI8rfp7D46fHVi6g/viewform?usp=sf_link")),
-                dbc.NavItem(dbc.NavLink("Preprint Publication", href="https://chemrxiv.org/articles/preprint/NPClassifier_A_Deep_Neural_Network-Based_Structural_Classification_Tool_for_Natural_Products/12885494/1")),
-                dbc.NavItem(dbc.NavLink("API", href="https://ccms-ucsd.github.io/GNPSDocumentation/api/#structure-np-classifier")),
+                dbc.NavItem(dbc.NavLink("Report Feedback",
+                                        href="https://docs.google.com/forms/d/e/1FAIpQLSf1-sw-P0SQGokyeaOpEmLda0UPJW93qkrI8rfp7D46fHVi6g/viewform?usp=sf_link")),
+                dbc.NavItem(dbc.NavLink("Preprint Publication",
+                                        href="https://chemrxiv.org/articles/preprint/NPClassifier_A_Deep_Neural_Network-Based_Structural_Classification_Tool_for_Natural_Products/12885494/1")),
+                dbc.NavItem(dbc.NavLink("API",
+                                        href="https://ccms-ucsd.github.io/GNPSDocumentation/api/#structure-np-classifier")),
             ],
-        navbar=True)
+            navbar=True)
     ],
     color="light",
     dark=False,
@@ -83,7 +75,7 @@
 
 BODY = dbc.Container(
     [
-        dbc.Row([dbc.Col(dbc.Card(DASHBOARD)),], style={"marginTop": 30}),
+        dbc.Row([dbc.Col(dbc.Card(DASHBOARD)), ], style={"marginTop": 30}),
     ],
     className="mt-12",
 )
@@ -101,44 +93,34 @@ def display_page(pathname):
     else:
         return "CC1C(O)CC2C1C(OC1OC(COC(C)=O)C(O)C(O)C1O)OC=C2C(O)=O"
 
-# This function will rerun at any 
+
+# This function will rerun at any
 @app.callback(
     [Output('classification_table', 'children'), Output('structure', 'children')],
     [Input('smiles_string', 'value')],
 )
 def handle_smiles(smiles_string):
     classification_dict = _process_full_classification(smiles_string)
-    #isglycoside, class_results, superclass_results, pathway_results, path_from_class, path_from_superclass, n_path, fp1, fp2 = classify_structure(smiles_string)
 
     output_list = []
 
     for result in classification_dict["pathway_results"]:
-        output_dict = {}
-        output_dict["type"] = "pathway"
-        output_dict["entry"] = result
+        output_dict = {"type": "pathway", "entry": result}
         output_list.append(output_dict)
 
-
     for result in classification_dict["superclass_results"]:
-        output_dict = {}
-        output_dict["type"] = "superclass"
-        output_dict["entry"] = result
+        output_dict = {"type": "superclass", "entry": result}
         output_list.append(output_dict)
 
-
     for result in classification_dict["class_results"]:
-        output_dict = {}
-        output_dict["type"] = "class"
-        output_dict["entry"] = result
+        output_dict = {"type": "class", "entry": result}
         output_list.append(output_dict)
 
     if classification_dict["isglycoside"]:
-        output_dict = {}
-        output_dict["type"] = "is glycoside"
-        output_dict["entry"] = "glycoside"
+        output_dict = {"type": "is glycoside", "entry": "glycoside"}
         output_list.append(output_dict)
 
-    #Creating Table
+    # Creating Table
     white_list_columns = ["type", "entry"]
     table_fig = dash_table.DataTable(
         columns=[
@@ -153,16 +135,18 @@ def handle_smiles(smiles_string):
         selected_columns=[],
         selected_rows=[],
         page_action="native",
-        page_current= 0,
-        page_size= 10,
+        page_current=0,
+        page_size=10,
     )
 
     # Creating Structure Image
-    img_obj = html.Img(id='image', src="https://gnps-structure.ucsd.edu/structureimg?smiles={}".format(urllib.parse.quote(smiles_string)))
+    img_obj = html.Img(id='image', src="https://gnps-structure.ucsd.edu/structureimg?smiles={}".format(
+        urllib.parse.quote(smiles_string)))
 
     return [table_fig, img_obj]
 
-# This function will rerun at any 
+
+# This function will rerun at any
 @app.callback(
     [Output('usage_summary', 'children')],
     [Input('url', 'pathname')],
@@ -172,84 +156,6 @@ def usage_summary(pathname):
     return ["Total Unique SMILES Classified - {}".format(number_entries)]
 
 
-def classify_structure(smiles):
-    isglycoside = fingerprint_handler._isglycoside(smiles)
-
-    fp = fingerprint_handler.calculate_fingerprint(smiles, 2)
-
-    fp1 = fp[0].tolist()[0]
-    fp2 = fp[1].tolist()[0]
-
-    query_dict = {}
-    query_dict["input_3"] = fp1
-    query_dict["input_4"] = fp2
-
-    # Handling SUPERCLASS
-    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/SUPERCLASS:predict"
-    payload = json.dumps({"instances": [ query_dict ]})
-
-    headers = {"content-type": "application/json"}
-    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
-
-    pred_super = np.asarray(json.loads(json_response.text)['predictions'])[0]
-    n_super = list(np.where(pred_super>=0.3)[0])
-
-    path_from_superclass = []
-    for j in n_super:
-        path_from_superclass += ontology_dictionary['Super_hierarchy'][str(j)]['Pathway']
-    path_from_superclass = list(set(path_from_superclass))
-
-    query_dict = {}
-    query_dict["input_3"] = fp1
-    query_dict["input_4"] = fp2
-
-    # Handling CLASS
-    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/CLASS:predict"
-    payload = json.dumps({"instances": [ query_dict ]})
-
-    headers = {"content-type": "application/json"}
-    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
-
-    pred_class = np.asarray(json.loads(json_response.text)['predictions'])[0]
-    n_class = list(np.where(pred_class>=0.1)[0])
-
-    path_from_class = []
-    for j in n_class:
-        path_from_class += ontology_dictionary['Class_hierarchy'][str(j)]['Pathway']
-    path_from_class = list(set(path_from_class))
-
-    query_dict = {}
-    query_dict["input_1"] = fp1
-    query_dict["input_2"] = fp2
-
-    # Handling PATHWAY
-    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/PATHWAY:predict"
-    payload = json.dumps({"instances": [ query_dict ]})
-
-    headers = {"content-type": "application/json"}
-    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
-
-    pred_path = np.asarray(json.loads(json_response.text)['predictions'])[0]
-    n_path = list(np.where(pred_path>=0.5)[0])
-
-    class_result = []
-    superclass_result = []
-    pathway_result = []
-
-    # Voting on Answer
-    pathway_result, superclass_result, class_result, isglycoside = prediction_voting.vote_classification(n_path, 
-                                                                                                        n_class, 
-                                                                                                        n_super, 
-                                                                                                        pred_class,
-                                                                                                        pred_super, 
-                                                                                                        path_from_class, 
-                                                                                                        path_from_superclass, 
-                                                                                                        isglycoside, 
-                                                                                                        ontology_dictionary)
-    
-    return isglycoside, class_result, superclass_result, pathway_result, path_from_class, path_from_superclass, n_path, fp1, fp2
-
-
 def _process_full_classification(smiles_string):
     try:
         db_record = ClassifyEntity.get(ClassifyEntity.smiles == smiles_string)
@@ -257,27 +163,29 @@ def _process_full_classification(smiles_string):
     except:
         pass
 
-    isglycoside, class_results, superclass_results, pathway_results, path_from_class, path_from_superclass, n_path, fp1, fp2 = classify_structure(smiles_string)
+    class_result, superclass_result, pathway_result, fp1, fp2, isglycoside = classify_structure(smiles_string, ontology_dictionary)
+
+    # Next version of the API response could just output *_result directly
 
-    respond_dict = {}
-    respond_dict["class_results"] = class_results
-    respond_dict["superclass_results"] = superclass_results
-    respond_dict["pathway_results"] = pathway_results
-    respond_dict["isglycoside"] = isglycoside
-    
-    respond_dict["fp1"] = fp1
-    respond_dict["fp2"] = fp2
+    respond_dict = {"class_results": list(class_result.values()),
+                    "superclass_results": list(superclass_result.values()),
+                    "pathway_results": list(pathway_result.values()),
+                    "class_results_ids": [int(i) for i in class_result.keys()],
+                    "superclass_results_ids": [int(i) for i in superclass_result.keys()],
+                    "pathway_results_ids": [int(i) for i in pathway_result.keys()],
+                    "isglycoside": isglycoside,
+                    "fp1": fp1, "fp2": fp2}
 
     # Lets save the result here, we should also check if its changed, and if so, we overwrite
     try:
         # Save it out
         ClassifyEntity.create(
-                smiles=smiles_string,
-                classification_json=json.dumps(respond_dict)
-            )
+            smiles=smiles_string,
+            classification_json=json.dumps(respond_dict)
+        )
     except:
         pass
-    
+
     return respond_dict
 
 
@@ -288,6 +196,7 @@ def classify():
 
     return json.dumps(respond_dict)
 
+
 # This gets you the model metadata
 @server.route("/model/metadata")
 def metadata():
@@ -295,7 +204,8 @@ def metadata():
     all_metadata = {}
     pathway_metadata = json.loads(requests.get("http://npclassifier-tf-server:8501/v1/models/PATHWAY/metadata").text)
     class_metadata = json.loads(requests.get("http://npclassifier-tf-server:8501/v1/models/CLASS/metadata").text)
-    superclass_metadata = json.loads(requests.get("http://npclassifier-tf-server:8501/v1/models/SUPERCLASS/metadata").text)
+    superclass_metadata = json.loads(
+        requests.get("http://npclassifier-tf-server:8501/v1/models/SUPERCLASS/metadata").text)
 
     all_metadata["pathway"] = pathway_metadata
     all_metadata["class"] = class_metadata
@@ -303,5 +213,6 @@ def metadata():
 
     return json.dumps(all_metadata)
 
+
 if __name__ == "__main__":
     app.run_server(debug=True, port=5000, host="0.0.0.0")
diff --git a/classification.py b/classification.py
new file mode 100644
index 0000000..59bd8b4
--- /dev/null
+++ b/classification.py
@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+
+import json
+
+import requests
+import numpy as np
+from Classifier import fingerprint_handler
+from Classifier import prediction_voting
+
+
+def classify_structure(smiles, ontology_dictionary):
+    isglycoside = fingerprint_handler._isglycoside(smiles)
+
+    fp = fingerprint_handler.calculate_fingerprint(smiles, 2)
+
+    fp1 = fp[0].tolist()[0]
+    fp2 = fp[1].tolist()[0]
+
+    query_dict = {"input_3": fp1, "input_4": fp2}
+
+    # Handling SUPERCLASS
+    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/SUPERCLASS:predict"
+    payload = json.dumps({"instances": [query_dict]})
+
+    headers = {"content-type": "application/json"}
+    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
+
+    pred_super = np.asarray(json.loads(json_response.text)['predictions'])[0]
+    n_super = set(np.where(pred_super >= 0.3)[0])
+
+    path_from_superclass = {j for i in n_super for j in ontology_dictionary['Super_hierarchy'][str(i)]['Pathway']}
+
+    query_dict = {"input_3": fp1, "input_4": fp2}
+
+    # Handling CLASS
+    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/CLASS:predict"
+    payload = json.dumps({"instances": [query_dict]})
+
+    headers = {"content-type": "application/json"}
+    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
+
+    pred_class = np.asarray(json.loads(json_response.text)['predictions'])[0]
+    n_class = set(np.where(pred_class >= 0.1)[0])
+
+    path_from_class = {j for i in n_class for j in ontology_dictionary['Class_hierarchy'][str(i)]['Pathway']}
+
+    query_dict = {"input_1": fp1, "input_2": fp2}
+
+    # Handling PATHWAY
+    fp_pred_url = "http://npclassifier-tf-server:8501/v1/models/PATHWAY:predict"
+    payload = json.dumps({"instances": [query_dict]})
+
+    headers = {"content-type": "application/json"}
+    json_response = requests.post(fp_pred_url, data=payload, headers=headers)
+
+    pred_path = np.asarray(json.loads(json_response.text)['predictions'])[0]
+    n_path = set(np.where(pred_path >= 0.5)[0])
+
+    # Voting on Answer
+    #
+    # pred_path, pred_super, pred_class are the nparrays of the totality of the predicted
+    # n_path, n_class, n_super are sets of the predicted pathways, classes and superclasses that are above noise
+    # path_from_class, path_from_superclass are sets of the pathways extracted from the superclasses/classes
+    n_pathway_result, n_superclass_result, n_class_result = prediction_voting.vote_classification(n_path,
+                                                                                                  n_class,
+                                                                                                  n_super,
+                                                                                                  pred_class,
+                                                                                                  pred_super,
+                                                                                                  path_from_class,
+                                                                                                  path_from_superclass,
+                                                                                                  ontology_dictionary)
+
+    # Get all the existing things of the ontology
+    index_class = list(ontology_dictionary['Class'].keys())
+    index_superclass = list(ontology_dictionary['Superclass'].keys())
+    index_pathway = list(ontology_dictionary['Pathway'].keys())
+
+    class_result = {i: index_class[i] for i in n_class_result}
+    superclass_result = {i: index_superclass[i] for i in n_superclass_result}
+    pathway_result = {i: index_pathway[i] for i in n_pathway_result}
+
+    return class_result, superclass_result, pathway_result, fp1, fp2, isglycoside