import numpy as np img_vec = np.array([[0.96055233, 2.258286e-05, 0.03799276, 0.00010049599, 0.0005647978, 0.0007671073], [8.2665576e-07, 0.9998913, 4.5214394e-05, 4.160851e-05, 2.4786823e-06, 1.8634972e-05], [7.672811e-05, 0.11272975, 0.0007890531, 0.885402, 0.0008227498, 0.00017966662], [0.00071230385, 0.5516768, 8.690722e-05, 0.44231993, 0.0030954983, 0.0021085672], [0.0016641148, 0.77376026, 0.0031669345, 0.0009105488, 0.21182135, 0.008676735], [6.535834e-06, 8.022754e-06, 1.4224903e-06, 1.215236e-06, 1.1371254e-06, 0.99998176], [6.131489e-06, 0.9909105, 0.00059569377, 5.8810587e-05, 0.008347422, 8.145085e-05], [0.0036011222, 0.019580165, 8.709692e-06, 0.97638834, 0.00014254787, 0.00027917026], [0.001183745, 0.0006474623, 0.9962907, 1.0715525e-05, 0.00025993327, 0.0016074692], [1.0901107e-06, 0.9997192, 0.00015559935, 6.622048e-05, 4.5755154e-05, 1.2046643e-05], [0.0086707845, 0.00019842519, 7.946216e-06, 0.990886, 7.297908e-05, 0.00016386487], [6.780453e-05, 0.9600331, 0.00020796066, 0.00074749015, 0.0033559885, 0.03558765], [9.2790346e-05, 6.0882794e-06, 1.5991038e-05, 1.153297e-05, 0.9985512, 0.0013223367], [0.00064366346, 0.0011190572, 0.9874978, 4.3241245e-05, 0.00935937, 0.0013369184], [5.2049214e-05, 1.2801657e-06, 2.9498646e-05, 1.7153299e-06, 2.559748e-05, 0.99988985], [0.0011970395, 9.731338e-06, 2.1237669e-05, 2.5094421e-05, 0.9977976, 0.000949231], [3.6162317e-06, 0.99877614, 2.023129e-05, 4.7237274e-05, 0.0011406511, 1.2184895e-05], [1.0230211e-05, 1.4835642e-06, 1.3025513e-05, 2.512241e-06, 0.003777029, 0.9961958], [0.0008493654, 1.1132153e-05, 0.00043108794, 1.4500493e-05, 0.00025561644, 0.9984382], [0.0019356995, 0.00017562874, 2.8180919e-05, 0.9975648, 4.8310343e-05, 0.00024738634], [0.0011779362, 0.00065545057, 0.99803513, 1.6797849e-06, 0.00010095722, 2.8904156e-05], [9.089741e-05, 0.978512, 0.005697065, 0.0005912572, 0.0011946606, 0.0139141055], [0.0005574316, 1.5321544e-05, 0.998833, 1.0735541e-06, 1.3806449e-05, 0.00057943986], [3.3686506e-06, 0.9970419, 0.0023412593, 5.4328397e-05, 0.0005123902, 4.668524e-05], [0.00033546498, 7.646274e-06, 0.00027485247, 3.023207e-06, 0.00021423706, 0.9991648], [1.8367424e-05, 4.2896054e-06, 0.000106551015, 4.9463824e-06, 0.0002757381, 0.9995901], [0.9992767, 1.0295089e-05, 0.0005470521, 6.5369866e-05, 2.390274e-05, 7.664138e-05], [0.9813146, 2.0880283e-05, 0.00026747925, 5.134113e-05, 0.00017709624, 0.018168535], [0.000114715054, 3.5882879e-06, 6.4218184e-05, 9.025374e-07, 2.9348028e-05, 0.99978715], [8.362681e-05, 3.8040469e-06, 3.1611694e-06, 5.5942483e-06, 2.07715e-05, 0.99988306], [2.381162e-05, 5.388778e-06, 0.9998869, 3.7252593e-07, 5.261669e-06, 7.830333e-05], [2.0016892e-06, 5.1701e-05, 3.349846e-06, 3.764534e-06, 3.08445e-07, 0.99993885], [2.2664715e-05, 0.99886394, 0.00025650975, 0.0006828798, 6.1300234e-05, 0.00011275579], [0.00468414, 0.00089653797, 0.8993314, 0.0002791324, 0.047310602, 0.047498215], [0.00071968854, 0.00012177682, 0.24034318, 0.00021860476, 0.0041009136, 0.75449586], [0.0011865953, 9.121098e-05, 0.9970276, 5.668214e-06, 0.00016106537, 0.001527927], [2.3530924e-06, 2.1742053e-06, 5.649086e-07, 4.312148e-07, 3.4217187e-06, 0.99999106], [0.00014497749, 0.0025425302, 4.2945932e-05, 0.00046326726, 6.245927e-05, 0.9967438], [0.00011293146, 0.00020251136, 0.99934644, 1.5175293e-05, 1.2938454e-05, 0.0003099968], [2.1264614e-05, 8.0309466e-05, 4.3275886e-05, 5.127402e-06, 0.00022328363, 0.99962676], [0.20738572, 0.0003576652, 0.01344411, 0.009969251, 0.74644786, 0.022395454], [0.00020409816, 3.12241e-05, 6.8546e-05, 2.2159917e-05, 0.0001425006, 0.9995315], [0.00014553087, 0.00028567325, 0.00020951268, 1.4521221e-05, 2.8495773e-05, 0.9993162], [0.0008911211, 8.1792154e-05, 0.99874395, 3.3982797e-06, 0.00012417257, 0.000155491], [0.00038905922, 9.907559e-06, 0.00018059136, 3.0968743e-06, 0.0011700838, 0.99824727], [1.7221013e-05, 2.6349528e-06, 5.5370288e-06, 2.477567e-06, 8.265479e-05, 0.9998895], [9.9847566e-05, 6.3684242e-06, 0.9982102, 2.084257e-06, 0.00013659797, 0.0015449121], [1.0410963e-05, 6.426374e-05, 1.8598449e-06, 6.239615e-06, 7.999239e-05, 0.9998373], [0.0869675, 0.06584302, 0.6744239, 0.10300095, 0.01296012, 0.056804616], [0.0117717115, 0.00048132922, 0.97692513, 0.00019082078, 0.0019260441, 0.008704997], [0.00024866726, 3.399789e-06, 1.9388704e-05, 5.750063e-06, 7.2076978e-06, 0.9997155], [2.4895007e-07, 0.99995446, 2.479923e-05, 1.8267598e-06, 2.6911289e-06, 1.5947582e-05], [0.9994961, 4.9302357e-06, 7.520917e-06, 1.349918e-05, 3.1691372e-05, 0.00044620666], [0.0110280635, 5.7762674e-05, 0.94336766, 8.401646e-05, 3.855721e-05, 0.04542392], [6.528507e-05, 8.321988e-06, 0.0003266337, 2.215188e-05, 3.3083543e-05, 0.99954444], [9.423565e-05, 4.435012e-05, 3.656979e-05, 8.248457e-05, 0.0001624587, 0.99957997], [0.0010561227, 2.2123982e-05, 0.0007157712, 7.461979e-06, 3.649775e-05, 0.998162], [0.00010612672, 1.2822995e-05, 0.0002027852, 0.00014030428, 1.5221644e-05, 0.9995227], [2.5843387e-05, 5.8845282e-05, 3.468311e-05, 3.2176097e-06, 0.00012839773, 0.999749], [6.324824e-05, 0.9942643, 0.00085196213, 0.00046436148, 0.00011373451, 0.004242372], [0.00014700319, 8.9749e-06, 0.9986589, 1.4744371e-05, 0.00020180215, 0.00096853205], [0.00014962514, 2.0305952e-05, 0.00014865371, 1.0956987e-05, 5.2481402e-05, 0.99961793], [0.9980076, 2.7990957e-06, 6.032486e-05, 0.00044359695, 0.00019174292, 0.0012939703], [0.007691547, 0.00093500345, 0.18037315, 0.00011863655, 0.020382222, 0.79049945], [0.21641138, 1.0207995e-05, 0.7222835, 3.0492043e-05, 4.4889697e-05, 0.061219506], [6.379888e-07, 1.6201967e-06, 3.1150805e-06, 3.8368708e-07, 6.1940234e-05, 0.9999323], [3.700095e-07, 5.901578e-06, 2.6105167e-07, 1.0030352e-06, 1.633056e-05, 0.99997616], [5.0621533e-05, 1.9467757e-06, 0.00022180694, 2.1919059e-06, 7.0791066e-05, 0.99965274], [2.1096278e-06, 4.6794298e-07, 4.203205e-06, 2.0262368e-07, 3.1075838e-06, 0.99998987], [0.00025825168, 0.98656857, 6.320665e-05, 0.0012662264, 0.0023025833, 0.00954124], [0.00044160953, 1.6045897e-05, 4.3325945e-05, 1.8907632e-06, 5.028731e-05, 0.9994468], [0.00019488716, 0.00019058606, 8.41964e-05, 6.888353e-05, 0.0001687472, 0.99929273], [4.5704277e-05, 6.786345e-05, 0.9982115, 2.6210228e-06, 0.0006911959, 0.0009810921], [7.6331664e-05, 2.011664e-06, 2.8380348e-06, 7.573107e-07, 0.00012957981, 0.9997886], [0.0004087675, 4.0216288e-05, 9.504767e-05, 4.42647e-06, 0.00020630336, 0.9992453], [0.00023382794, 4.4813132e-05, 7.9612044e-05, 6.986424e-05, 0.00073117134, 0.99884075], [0.062337544, 3.3703975e-05, 0.9317716, 2.508398e-05, 0.0012973467, 0.0045347577], [2.1503301e-05, 0.00016361932, 1.08347315e-07, 0.9998073, 2.8828802e-06, 4.5499937e-06], [0.00020062785, 0.00021684812, 4.28962e-07, 0.99948347, 6.681421e-05, 3.179363e-05], [9.77566e-05, 0.0063463016, 2.1972079e-05, 0.9933281, 1.2384863e-05, 0.00019351323], [0.00022732468, 0.028287608, 5.508699e-06, 0.97141, 7.669675e-06, 6.191368e-05], [0.00010191731, 0.0019176323, 6.4018345e-06, 0.9976427, 7.411093e-05, 0.00025718144], [9.5899224e-05, 0.00012591598, 2.5450026e-07, 0.999546, 4.338007e-07, 0.00023143641], [0.00084430055, 0.0003077784, 7.928243e-05, 0.99862146, 2.9028893e-06, 0.00014420082], [3.070976e-05, 1.4407342e-05, 4.922463e-06, 0.9999467, 5.0399626e-07, 2.8007819e-06], [2.182484e-06, 2.3648156e-06, 3.133209e-08, 0.999951, 8.8792e-06, 3.557294e-05], [2.3768967e-05, 0.00420844, 6.2199438e-06, 0.9949956, 0.00056535157, 0.00020066672], [9.024704e-05, 0.00071282196, 2.027537e-05, 0.9991554, 1.3690523e-05, 7.5391968e-06], [2.1495691e-06, 0.0004972608, 3.113801e-08, 0.9994978, 4.523372e-07, 2.2732054e-06], [0.0003065313, 0.00059253303, 1.6592052e-05, 0.9990299, 5.0043127e-06, 4.949302e-05], [1.6929504e-05, 0.00011503535, 1.5254145e-06, 0.9998354, 2.6390417e-05, 4.722362e-06], [1.353101e-05, 7.160427e-06, 8.9427135e-07, 1.0162887e-05, 1.2177648e-05, 0.99995613], [0.9647493, 8.234284e-06, 0.0012121877, 0.00016015388, 4.2248827e-05, 0.033827987], [9.6978925e-05, 7.325615e-06, 0.99978405, 4.1737053e-07, 6.712808e-05, 4.4136286e-05], [0.00039633378, 3.7775088e-05, 0.9984524, 2.0990623e-05, 0.0004990453, 0.00059333554], [3.810157e-05, 3.0353576e-06, 1.0080317e-05, 9.042036e-07, 6.2223257e-06, 0.9999417], [0.4111154, 0.0011122819, 0.4517259, 0.00014106455, 0.13472192, 0.0011834597], [5.320623e-06, 4.627046e-07, 0.99999225, 1.3624614e-07, 1.2822727e-06, 6.382401e-07], [0.9999937, 6.208606e-08, 1.581343e-07, 4.891238e-06, 6.107547e-07, 5.9826743e-07], [2.8962797e-05, 3.721352e-05, 8.860266e-05, 0.00039464454, 6.6442367e-06, 0.99944395], [0.0032606511, 0.011139718, 0.030814923, 0.00024155801, 0.06561669, 0.8889264], [2.5870377e-06, 2.7707129e-06, 2.0302086e-05, 1.6544083e-06, 1.1963882e-05, 0.9999608], [0.000202565, 2.6852873e-05, 0.0003634819, 0.00024846158, 0.0007974159, 0.9983612], [2.0926058e-05, 1.8182202e-06, 1.1790693e-07, 4.8341514e-07, 6.0446206e-07, 0.99997604], [0.028095853, 4.3367876e-05, 0.0016105362, 2.8183706e-05, 0.0017630305, 0.968459], [1.9018269e-05, 6.0379352e-06, 4.9211743e-05, 1.0085847e-05, 0.00012530744, 0.99979025], [0.060545366, 0.0004554764, 0.00046816227, 0.002502421, 0.00044651356, 0.93558204], [0.27325073, 0.00771274, 0.019479744, 0.0021354912, 0.005549205, 0.6918721], [5.6169597e-06, 0.00011384774, 2.1649846e-07, 0.9998567, 9.4081577e-07, 2.2666374e-05], [5.695287e-07, 0.00030449417, 8.784171e-07, 0.9996848, 5.7088266e-07, 8.743865e-06], [0.9551394, 4.7516158e-05, 0.0074541913, 0.0037016356, 0.028489739, 0.0051675453], [0.99861693, 1.0142513e-05, 0.00060740334, 0.0002856261, 0.00034672575, 0.00013314377], [0.9792437, 2.468583e-05, 0.00081091846, 0.00016031889, 0.013843318, 0.005916974], [1.6959195e-06, 5.918661e-05, 0.9998678, 5.2934445e-07, 6.7718014e-05, 3.1154234e-06], [0.00012123352, 4.2831598e-05, 1.216062e-05, 4.2108844e-05, 6.7003886e-05, 0.9997147], [0.01510195, 0.011391317, 0.96790373, 0.0018710705, 0.003040597, 0.00069139147], [0.047799855, 0.0008591413, 0.91728127, 0.00034789066, 0.01108827, 0.022623597], [6.4263186e-05, 0.9823163, 0.0018647922, 0.0005431149, 0.002682755, 0.012528814], [1.060747e-05, 1.8184604e-05, 1.4086738e-05, 3.2358553e-06, 0.0010587095, 0.9988952], [3.5722267e-05, 2.5053894e-05, 0.9998696, 4.995491e-06, 1.1931344e-05, 5.2669675e-05], [0.0001805724, 0.0001780914, 9.717351e-05, 2.4770374e-05, 0.00036272118, 0.9991567], [0.000122015765, 0.037958253, 0.95994574, 4.104272e-05, 0.0018042325, 0.00012869414], [0.0030229634, 0.0020233607, 0.09346539, 0.0017162731, 0.8942453, 0.005526651], [0.9937542, 3.3676264e-05, 2.9989189e-05, 0.00026614027, 0.0015670614, 0.004348961], [1.9596002e-05, 5.460919e-07, 2.370617e-05, 4.5871317e-07, 2.7834278e-05, 0.99992776], [0.9841687, 8.3942214e-05, 0.0048708566, 0.00049588655, 0.0062702587, 0.004110364], [0.0012712743, 0.010262053, 0.7756221, 0.00039430457, 0.015086965, 0.19736323], [3.714552e-05, 1.3856393e-07, 5.570143e-07, 1.12131616e-07, 3.007406e-06, 0.999959], [0.0013944668, 0.26817438, 0.00019180149, 0.2411945, 0.0015136658, 0.4875312], [0.97960424, 0.00090531213, 0.003989057, 6.44968e-05, 0.014767058, 0.00066983316], [9.791337e-05, 0.0020514317, 0.9841254, 8.117802e-06, 0.008534896, 0.0051823542], [6.738693e-05, 0.99966264, 3.6401958e-05, 1.3951828e-05, 8.889249e-05, 0.00013071796], [4.5688095e-05, 0.9969543, 0.00018425529, 0.002266784, 0.0004785581, 7.049832e-05], [2.6118161e-05, 2.821403e-05, 0.00042664632, 2.667754e-06, 0.00010307418, 0.9994134], [0.55532414, 0.06486542, 0.13558346, 0.14332081, 0.09034258, 0.010563576], [0.02537992, 0.012770072, 0.011560863, 0.002702126, 0.01272034, 0.93486667], [1.4797672e-05, 3.2593474e-05, 2.8730577e-07, 6.1165274e-06, 8.588703e-05, 0.9998604], [1.4056281e-05, 0.9992576, 0.0001368214, 0.0001096892, 0.000101724356, 0.00037998933], [3.2113876e-05, 4.381272e-06, 1.3588096e-05, 5.2938367e-06, 0.00019010111, 0.9997545], [3.980216e-05, 0.9927362, 1.9653033e-05, 0.0026291257, 0.00011515237, 0.0044601825], [3.3344858e-07, 0.9999144, 7.431238e-06, 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1.1580972e-06, 0.9999832, 1.3364505e-06], [8.427378e-05, 7.289212e-05, 0.0001431556, 8.192274e-06, 0.9995493, 0.00014225848], [0.0003929598, 0.011132104, 6.5121356e-05, 0.0011249633, 0.0019643756, 0.9853205], [1.3171218e-06, 3.5009045e-06, 3.7294754e-05, 4.7362425e-07, 0.99994314, 1.4257092e-05], [0.39438167, 0.0017286014, 0.00032493993, 0.0008990323, 0.57731605, 0.025349764], [5.414873e-05, 0.00012150001, 0.00020906788, 0.00017998062, 0.99937856, 5.6598696e-05], [0.00014981766, 0.0005432433, 0.0008475171, 0.00017207977, 0.99797136, 0.00031606277], [3.8767907e-06, 7.4034324e-07, 1.3048981e-05, 1.7675501e-07, 0.99996173, 2.046132e-05], [1.5952372e-05, 0.95860034, 0.00011489859, 1.2939016e-05, 0.04114782, 0.000108118664], [3.5599953e-06, 2.2279166e-05, 5.5515837e-05, 1.1491754e-06, 0.99984384, 7.356449e-05], [2.9583014e-06, 2.6741093e-06, 0.9999702, 1.764473e-07, 2.3263062e-06, 2.1712807e-05], [0.0033311204, 0.0003831879, 0.00024211868, 0.0001649175, 0.9957098, 0.00016881713], [0.0021427542, 0.00037256672, 0.007704302, 2.6914771e-05, 0.000105511346, 0.9896479], [0.0010947735, 0.89787227, 0.004901295, 0.052842747, 0.024325205, 0.01896374], [7.556153e-08, 1.9007002e-05, 5.9896195e-08, 9.409651e-08, 0.9999758, 4.8523098e-06], [0.98385423, 2.769718e-05, 0.0007237124, 8.33519e-05, 0.0018718238, 0.013439039], [4.482766e-06, 6.223699e-06, 3.5390046e-06, 4.342299e-06, 0.99970275, 0.00027860425], [3.9287097e-05, 0.0020286934, 0.0008471617, 8.362652e-05, 0.9968947, 0.0001063732], [0.0129518965, 0.00033974875, 0.08617414, 0.00016099395, 0.69730043, 0.20307274]]) import gradio as gr from transformers import BertTokenizer, BertForSequenceClassification import re import torch from sklearn.metrics.pairwise import cosine_similarity tokenizer = BertTokenizer.from_pretrained(r'./model') model = BertForSequenceClassification.from_pretrained(r'./model', num_labels = 6) labels = ['伤感', '快乐', '励志', '宣泄', '平静', '感人'] def preprocess(temp): temp = re.sub(u"\n\n", "\n", temp) temp = re.sub(u"(^\n)|(\n$)", "", temp) temp = re.sub(u",", "，", temp) temp = re.sub(u"\?", "？", temp) temp = re.sub(u"!", "！", temp) temp = re.sub(u"\.", "。", temp) temp = re.sub('[^\u4e00-\u9fa5，。？！\n]+', '', temp) temp = re.sub(u"\n", "，", temp) for _ in range(int(len(temp) / 2)): temp = re.sub(u"，，|！！|？？|。。", "，", temp) temp = re.sub(u"，！|！，", "！", temp) temp = re.sub(u"，？|？，", "？", temp) temp = re.sub(u"，。|。，", "。", temp) temp = temp.strip('，') return temp def match_img(txt_vec): sim_matrix = [] for vec in img_vec: sim = cosine_similarity(txt_vec, vec) sim_matrix.append(sim) img_idx = np.argmax(sim_matrix) return img_idx def classify_text(inp): # print(inp) inp = preprocess(inp) # print(inp) inp = tokenizer(inp, padding=True, max_length=512, truncation=True, return_tensors="pt") # print(inp) with torch.no_grad(): logits = model(**inp).logits # print(logits) # print(logits.shape) # logits = logits.argsort().squeeze(0) logits = torch.nn.Softmax(dim=1)(logits).squeeze(0).cpu().numpy() img_idx = match_img(logits) # print(logits) return {labels[i]: float(logits[i].item()) for i in range(len(labels))} # return {logits.argmax().item() : labels[logits.argmax().item()]} input = '明天又是好日子\n千金的光阴不能等\n明天又是好日子\n赶上了盛世咱享太平\n今天是个好日子\n心想的事儿都能成\n明天又是好日子\n千金的光阴不能等\n今天明天都是好日子\n赶上了盛世咱享太平' gr.Interface( classify_text, # gr.inputs.Image(), gr.inputs.Textbox(lines=5,default=""), outputs = 'label' # inputs='image', # outputs='label', # examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]], ).launch(debug=True)