How to use getlist method in tox

Best Python code snippet using tox_python

app.py

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...6162@app.route('/index', methods=['GET', 'POST'])63def index():64 if request.method == 'POST':65 print(request.form.getlist('hello'))66 z = request.form.getlist('hello')67 if z[0] == '1':68 return redirect(url_for('svc'))69 if z[0] == '2':70 return redirect(url_for('knn'))71 if z[0] == '3':72 return redirect(url_for('logistic'))73 if z[0] == '4':74 return redirect(url_for('adaboost'))75 if z[0] == '5':76 return redirect(url_for('naive'))77 if z[0] == '6':78 return redirect(url_for('resnet18'))79 if z[0] == '7':80 return redirect(url_for('resnet34'))81 if z[0] == '8':82 return redirect(url_for('resnet50'))83 if z[0] == '9':84 return redirect(url_for('resnet101'))85 if z[0] == '10':86 return redirect(url_for('resnet152'))87 if z[0] == '11':88 return redirect(url_for('densenet121'))89 if z[0] == '12':90 return redirect(url_for('densenet169'))91 if z[0] == '13':92 return redirect(url_for('densenet201'))93 if z[0] == '14':94 return redirect(url_for('densenet161'))95 if z[0] == '15':96 return redirect(url_for('alexnet'))97 if z[0] == '16':98 return redirect(url_for('squeezenet1_0'))99 if z[0] == '17':100 return redirect(url_for('squeezenet1_1'))101 if z[0] == '18':102 return redirect(url_for('vgg11'))103 if z[0] == '19':104 return redirect(url_for('vgg11_bn'))105 if z[0] == '20':106 return redirect(url_for('vgg13'))107 if z[0] == '21':108 return redirect(url_for('vgg13_bn'))109 if z[0] == '22':110 return redirect(url_for('vgg16'))111 if z[0] == '23':112 return redirect(url_for('vgg16_bn'))113 if z[0] == '24':114 return redirect(url_for('vgg19'))115 if z[0] == '25':116 return redirect(url_for('vgg19_bn'))117 try:118 #global filename119 import os120 num_classes = len(os.listdir(os.path.splitext(filename)[0]))121 print("Number of classes : ",num_classes)122 except:123 num_classes = "error"124 return render_template('select_model.html',filename=filename,num_class=num_classes)125126@app.route('/returnmodel/')127def returnmodel():128 try:129 return send_file("model.pkl")130 except Exception as e:131 return str(e)132133@app.route('/svc_classifier', methods=['GET', 'POST'])134def svc():135 global filename136 if request.method == 'POST':137 print(request.form.getlist('kernal')[0])138 kernel = request.form.getlist('kernal')[0]139 print(request.form.getlist('gamma')[0])140 gamma = request.form.getlist('gamma')[0]141 print(request.form.getlist('decision_function_shape')[0])142 decision_function_shape = request.form.getlist('decision_function_shape')[0]143 print(request.form.getlist('max_iter')[0])144 try:145 max_iter = int(request.form.getlist('max_iter')[0])146 except:147 max_iter = -1148 print(request.form.getlist('random_state')[0])149 try:150 random_state = int(request.form.getlist('random_state')[0])151 except:152 random_state = None153 print(request.form.getlist('shape1')[0])154 shape11 = int(request.form.getlist('shape1')[0])155 print(request.form.getlist('shape2')[0])156 shape22 = int(request.form.getlist('shape2')[0])157 import trainer158 a,bz = trainer.svc_model(kernel1=kernel,gamma1=gamma,max_iter1=max_iter, decision_function_shape1=decision_function_shape,random_state1=random_state,shape1 = shape11,shape2=shape22,file=os.path.splitext(filename)[0])159 global filename2160 filename2 = a161 global b162 b = bz163 return redirect(url_for('svc'))164 return render_template('upload_svc.html',something=str(b))165166@app.route('/logistic_classifier', methods=['GET', 'POST'])167def logistic():168 if request.method == 'POST':169 print(request.form.getlist('criterion'))170 print(request.form.getlist('splitter'))171 print(request.form.getlist('max_depth'))172 print(request.form.getlist('random_state'))173 print(request.form.getlist('min_samples_split'))174 print(request.form.getlist('max_features'))175 print(request.form.getlist('shape1')[0])176 shape11 = int(request.form.getlist('shape1')[0])177 print(request.form.getlist('shape2')[0])178 shape22 = int(request.form.getlist('shape2')[0])179 try:180 max_depth = int(request.form.getlist('max_depth')[0])181 except:182 max_depth = None183 try:184 min_samples_split = int(request.form.getlist('min_samples_split')[0])185 except:186 min_samples_split = 2187 try:188 min_samples_leaf = int(request.form.getlist('min_samples_leaf')[0])189 except:190 min_samples_leaf = 2191 try:192 random_state = int(request.form.getlist('random_state')[0])193 except:194 random_state = None195 if request.form.getlist('max_features')[0] == "None":196 max_features = None197 else:198 max_features = request.form.getlist('max_features')[0]199 criterion = request.form.getlist('criterion')[0]200 splitter = request.form.getlist('splitter')[0]201 global filename202 import trainer203 a,bz = trainer.logistic(criterion1=criterion, splitter1=splitter, max_depth1=max_depth, min_samples_split1=min_samples_split,min_samples_leaf1 =min_samples_leaf ,max_features1=max_features,random_state1=random_state,shape1 = shape11,shape2= shape22,file=os.path.splitext(filename)[0])204 filename2 = a205 global b206 b = bz207 return redirect(url_for('logistic'))208 return render_template('upload_logistic.html',something=str(b))209210@app.route('/adaboost_classifier', methods=['GET', 'POST'])211def adaboost():212 global filename213 if request.method == 'POST':214 print(request.form.getlist('learning_rate'))215 print(request.form.getlist('random_state'))216 print(request.form.getlist('n_estimators'))217 try:218 learning_rate = float(request.form.getlist('learning_rate')[0])219 except:220 learning_rate = 0.1221 try:222 random_state = int(request.form.getlist('random_state')[0])223 except:224 random_state = None225 try:226 n_estimators = int(request.form.getlist('n_estimators')[0])227 except:228 n_estimators = 100229 print(request.form.getlist('shape1')[0])230 shape11 = int(request.form.getlist('shape1')[0])231 print(request.form.getlist('shape2')[0])232 shape22 = int(request.form.getlist('shape2')[0])233 import trainer234 global filename235 a,bz = trainer.adaboost(n_estimators1=n_estimators,random_state1=random_state,learning_rate1=learning_rate,shape1 = shape11,shape2= shape22,file=os.path.splitext(filename)[0])236 global filename2237 filename2 = a238 global b239 b = bz240 return redirect(url_for('adaboost'))241 return render_template('upload_adaboost.html',something=str(b))242243@app.route('/knn_classifier', methods=['GET', 'POST'])244def knn():245 global filename246 if request.method == 'POST':247 print(request.form.getlist('weights'))248 print(request.form.getlist('algorithm'))249 print(request.form.getlist('leaf_size'))250 print(request.form.getlist('n_neighbors'))251 weights = request.form.getlist('weights')[0]252 algorithm = request.form.getlist('algorithm')[0]253 try:254 leaf_size = int(request.form.getlist('leaf_size')[0])255 except:256 leaf_size = 30257 try:258 n_neighbors = int(request.form.getlist('n_neighbors')[0])259 except:260 n_neighbors = 5261 print(request.form.getlist('shape1')[0])262 shape11 = int(request.form.getlist('shape1')[0])263 print(request.form.getlist('shape2')[0])264 shape22 = int(request.form.getlist('shape2')[0])265 import trainer266 a,bz = trainer.knn(weights1 =weights,leaf_size1=leaf_size,n_neighbors1 = n_neighbors,algorithm1 = algorithm,shape1 = shape11,shape2= shape22,file=os.path.splitext(filename)[0])267 global filename2268 filename2 = a269 global b270 b = bz271 return redirect(url_for('knn'))272 return render_template('upload_knn.html',something=str(b))273274@app.route('/naive_classifier', methods=['GET', 'POST'])275def naive():276 if request.method == 'POST':277 print(request.form.getlist('shape1')[0])278 shape11 = int(request.form.getlist('shape1')[0])279 print(request.form.getlist('shape2')[0])280 shape22 = int(request.form.getlist('shape2')[0])281 import trainer282 a,bz = trainer.naivebayes(shape1 = shape11,shape2= shape22,file=os.path.splitext(filename)[0])283 global filename2284 filename2 = a285 global b286 b = bz287 return redirect(url_for('naive'))288 return render_template('upload_naive.html',something=str(b))289290291292@app.route('/resnet18_classifier', methods=['GET', 'POST'])293def resnet18():294 global filename295 if request.method == 'POST':296 print(request.form.getlist('augmentation')[0])297 try:298 augmentation = int(request.form.getlist('augmentation')[0])299 except:300 augmentation = 1301 try:302 epochs = int(request.form.getlist('epochs')[0])303 except:304 epochs = 4305 print(request.form.getlist('epochs')[0])306 print(request.form.getlist('validation_split')[0])307 validation_split = float(request.form.getlist('validation_split')[0])308 import dl_trainer309 file = os.path.splitext(filename)[0]310 a,bz = dl_trainer.dl_models(filename=file,model=1,aug=augmentation,epochs=epochs,validation_split=validation_split)311 global filename2312 filename2 = a313 global b314 b = bz315 return redirect(url_for('resnet18'))316 return render_template('upload_resnet18.html',something=str(b))317318@app.route('/resnet34_classifier', methods=['GET', 'POST'])319def resnet34():320 global filename321 if request.method == 'POST':322 print(request.form.getlist('augmentation')[0])323 try:324 augmentation = int(request.form.getlist('augmentation')[0])325 except:326 augmentation = 1327 try:328 epochs = int(request.form.getlist('epochs')[0])329 except:330 epochs = 4331 print(request.form.getlist('epochs')[0])332 print(request.form.getlist('validation_split')[0])333 validation_split = float(request.form.getlist('validation_split')[0])334 import dl_trainer335 file = os.path.splitext(filename)[0]336 a,bz = dl_trainer.dl_models(filename=file,model=2,aug=augmentation,epochs=epochs,validation_split=validation_split)337 global filename2338 filename2 = a339 global b340 b = bz341 return redirect(url_for('resnet34'))342 return render_template('upload_resnet34.html',something=str(b))343344@app.route('/resnet50_classifier', methods=['GET', 'POST'])345def resnet50():346 global filename347 if request.method == 'POST':348 print(request.form.getlist('augmentation')[0])349 try:350 augmentation = int(request.form.getlist('augmentation')[0])351 except:352 augmentation = 1353 try:354 epochs = int(request.form.getlist('epochs')[0])355 except:356 epochs = 4357 print(request.form.getlist('epochs')[0])358 print(request.form.getlist('validation_split')[0])359 validation_split = float(request.form.getlist('validation_split')[0])360 import dl_trainer361 file = os.path.splitext(filename)[0]362 a,bz = dl_trainer.dl_models(filename=file,model=3,aug=augmentation,epochs=epochs,validation_split=validation_split)363 global filename2364 filename2 = a365 global b366 b = bz367 return redirect(url_for('resnet50'))368 return render_template('upload_resnet50.html',something=str(b))369370@app.route('/resnet101_classifier', methods=['GET', 'POST'])371def resnet101():372 global filename373 if request.method == 'POST':374 print(request.form.getlist('augmentation')[0])375 try:376 augmentation = int(request.form.getlist('augmentation')[0])377 except:378 augmentation = 1379 try:380 epochs = int(request.form.getlist('epochs')[0])381 except:382 epochs = 4383 print(request.form.getlist('epochs')[0])384 print(request.form.getlist('validation_split')[0])385 validation_split = float(request.form.getlist('validation_split')[0])386 import dl_trainer387 file = os.path.splitext(filename)[0]388 a,bz = dl_trainer.dl_models(filename=file,model=4,aug=augmentation,epochs=epochs,validation_split=validation_split)389 global filename2390 filename2 = a391 global b392 b = bz393 return redirect(url_for('resnet101'))394 return render_template('upload_resnet101.html',something=str(b))395396@app.route('/resnet152_classifier', methods=['GET', 'POST'])397def resnet152():398 global filename399 if request.method == 'POST':400 print(request.form.getlist('augmentation')[0])401 try:402 augmentation = int(request.form.getlist('augmentation')[0])403 except:404 augmentation = 1405 try:406 epochs = int(request.form.getlist('epochs')[0])407 except:408 epochs = 4409 print(request.form.getlist('epochs')[0])410 print(request.form.getlist('validation_split')[0])411 validation_split = float(request.form.getlist('validation_split')[0])412 import dl_trainer413 file = os.path.splitext(filename)[0]414 a,bz = dl_trainer.dl_models(filename=file,model=5,aug=augmentation,epochs=epochs,validation_split=validation_split)415 global filename2416 filename2 = a417 global b418 b = bz419 return redirect(url_for('resnet152'))420 return render_template('upload_resnet152.html',something=str(b))421422@app.route('/densenet121_classifier', methods=['GET', 'POST'])423def densenet121():424 global filename425 if request.method == 'POST':426 print(request.form.getlist('augmentation')[0])427 try:428 augmentation = int(request.form.getlist('augmentation')[0])429 except:430 augmentation = 1431 try:432 epochs = int(request.form.getlist('epochs')[0])433 except:434 epochs = 4435 print(request.form.getlist('epochs')[0])436 print(request.form.getlist('validation_split')[0])437 validation_split = float(request.form.getlist('validation_split')[0])438 import dl_trainer439 file = os.path.splitext(filename)[0]440 a,bz = dl_trainer.dl_models(filename=file,model=6,aug=augmentation,epochs=epochs,validation_split=validation_split)441 global filename2442 filename2 = a443 global b444 b = bz445 return redirect(url_for('densenet121'))446 return render_template('upload_densenet121.html',something=str(b))447448@app.route('/densenet169_classifier', methods=['GET', 'POST'])449def densenet169():450 global filename451 if request.method == 'POST':452 print(request.form.getlist('augmentation')[0])453 try:454 augmentation = int(request.form.getlist('augmentation')[0])455 except:456 augmentation = 1457 try:458 epochs = int(request.form.getlist('epochs')[0])459 except:460 epochs = 4461 print(request.form.getlist('epochs')[0])462 print(request.form.getlist('validation_split')[0])463 validation_split = float(request.form.getlist('validation_split')[0])464 import dl_trainer465 file = os.path.splitext(filename)[0]466 a,bz = dl_trainer.dl_models(filename=file,model=7,aug=augmentation,epochs=epochs,validation_split=validation_split)467 global filename2468 filename2 = a469 global b470 b = bz471 return redirect(url_for('densenet169'))472 return render_template('upload_densenet169.html',something=str(b))473474@app.route('/densenet201_classifier', methods=['GET', 'POST'])475def densenet201():476 global filename477 if request.method == 'POST':478 print(request.form.getlist('augmentation')[0])479 try:480 augmentation = int(request.form.getlist('augmentation')[0])481 except:482 augmentation = 1483 try:484 epochs = int(request.form.getlist('epochs')[0])485 except:486 epochs = 4487 print(request.form.getlist('epochs')[0])488 print(request.form.getlist('validation_split')[0])489 validation_split = float(request.form.getlist('validation_split')[0])490 import dl_trainer491 file = os.path.splitext(filename)[0]492 a,bz = dl_trainer.dl_models(filename=file,model=8,aug=augmentation,epochs=epochs,validation_split=validation_split)493 global filename2494 filename2 = a495 global b496 b = bz497 return redirect(url_for('densenet201'))498 return render_template('upload_densenet201.html',something=str(b))499500@app.route('/densenet161_classifier', methods=['GET', 'POST'])501def densenet161():502 global filename503 if request.method == 'POST':504 print(request.form.getlist('augmentation')[0])505 try:506 augmentation = int(request.form.getlist('augmentation')[0])507 except:508 augmentation = 1509 try:510 epochs = int(request.form.getlist('epochs')[0])511 except:512 epochs = 4513 print(request.form.getlist('epochs')[0])514 print(request.form.getlist('validation_split')[0])515 validation_split = float(request.form.getlist('validation_split')[0])516 import dl_trainer517 file = os.path.splitext(filename)[0]518 a,bz = dl_trainer.dl_models(filename=file,model=9,aug=augmentation,epochs=epochs,validation_split=validation_split)519 global filename2520 filename2 = a521 global b522 b = bz523 return redirect(url_for('densenet161'))524 return render_template('upload_densenet161.html',something=str(b))525526@app.route('/alexnet_classifier', methods=['GET', 'POST'])527def alexnet():528 global filename529 if request.method == 'POST':530 print(request.form.getlist('augmentation')[0])531 try:532 augmentation = int(request.form.getlist('augmentation')[0])533 except:534 augmentation = 1535 try:536 epochs = int(request.form.getlist('epochs')[0])537 except:538 epochs = 4539 print(request.form.getlist('epochs')[0])540 print(request.form.getlist('validation_split')[0])541 validation_split = float(request.form.getlist('validation_split')[0])542 import dl_trainer543 file = os.path.splitext(filename)[0]544 a,bz = dl_trainer.dl_models(filename=file,model=10,aug=augmentation,epochs=epochs,validation_split=validation_split)545 global filename2546 filename2 = a547 global b548 b = bz549 return redirect(url_for('alexnet'))550 return render_template('upload_alexnet.html',something=str(b))551552@app.route('/squeezenet1_0_classifier', methods=['GET', 'POST'])553def squeezenet1_0():554 global filename555 if request.method == 'POST':556 print(request.form.getlist('augmentation')[0])557 try:558 augmentation = int(request.form.getlist('augmentation')[0])559 except:560 augmentation = 1561 try:562 epochs = int(request.form.getlist('epochs')[0])563 except:564 epochs = 4565 print(request.form.getlist('epochs')[0])566 print(request.form.getlist('validation_split')[0])567 validation_split = float(request.form.getlist('validation_split')[0])568 import dl_trainer569 file = os.path.splitext(filename)[0]570 a,bz = dl_trainer.dl_models(filename=file,model=11,aug=augmentation,epochs=epochs,validation_split=validation_split)571 global filename2572 filename2 = a573 global b574 b = bz575 return redirect(url_for('squeezenet1_0'))576 return render_template('upload_squeezenet1_0.html',something=str(b))577578@app.route('/squeezenet1_1_classifier', methods=['GET', 'POST'])579def squeezenet1_1():580 global filename581 if request.method == 'POST':582 print(request.form.getlist('augmentation')[0])583 try:584 augmentation = int(request.form.getlist('augmentation')[0])585 except:586 augmentation = 1587 try:588 epochs = int(request.form.getlist('epochs')[0])589 except:590 epochs = 4591 print(request.form.getlist('epochs')[0])592 print(request.form.getlist('validation_split')[0])593 validation_split = float(request.form.getlist('validation_split')[0])594 import dl_trainer595 file = os.path.splitext(filename)[0]596 a,bz = dl_trainer.dl_models(filename=file,model=12,aug=augmentation,epochs=epochs,validation_split=validation_split)597 global filename2598 filename2 = a599 global b600 b = bz601 return redirect(url_for('squeezenet1_1'))602 return render_template('upload_squeezenet1_1.html',something=str(b))603604@app.route('/vgg11_classifier', methods=['GET', 'POST'])605def vgg11():606 global filename607 if request.method == 'POST':608 print(request.form.getlist('augmentation')[0])609 try:610 augmentation = int(request.form.getlist('augmentation')[0])611 except:612 augmentation = 1613 try:614 epochs = int(request.form.getlist('epochs')[0])615 except:616 epochs = 4617 print(request.form.getlist('epochs')[0])618 print(request.form.getlist('validation_split')[0])619 validation_split = float(request.form.getlist('validation_split')[0])620 import dl_trainer621 file = os.path.splitext(filename)[0]622 a,bz = dl_trainer.dl_models(filename=file,model=13,aug=augmentation,epochs=epochs,validation_split=validation_split)623 global filename2624 filename2 = a625 global b626 b = bz627 return redirect(url_for('vgg11'))628 return render_template('upload_vgg11.html',something=str(b))629630@app.route('/vgg11_bn_classifier', methods=['GET', 'POST'])631def vgg11_bn():632 global filename633 if request.method == 'POST':634 print(request.form.getlist('augmentation')[0])635 try:636 augmentation = int(request.form.getlist('augmentation')[0])637 except:638 augmentation = 1639 try:640 epochs = int(request.form.getlist('epochs')[0])641 except:642 epochs = 4643 print(request.form.getlist('epochs')[0])644 print(request.form.getlist('validation_split')[0])645 validation_split = float(request.form.getlist('validation_split')[0])646 import dl_trainer647 file = os.path.splitext(filename)[0]648 a,bz = dl_trainer.dl_models(filename=file,model=14,aug=augmentation,epochs=epochs,validation_split=validation_split)649 global filename2650 filename2 = a651 global b652 b = bz653 return redirect(url_for('vgg11_bn'))654 return render_template('upload_vgg11_bn.html',something=str(b))655656@app.route('/vgg13_classifier', methods=['GET', 'POST'])657def vgg13():658 global filename659 if request.method == 'POST':660 print(request.form.getlist('augmentation')[0])661 try:662 augmentation = int(request.form.getlist('augmentation')[0])663 except:664 augmentation = 1665 try:666 epochs = int(request.form.getlist('epochs')[0])667 except:668 epochs = 4669 print(request.form.getlist('epochs')[0])670 print(request.form.getlist('validation_split')[0])671 validation_split = float(request.form.getlist('validation_split')[0])672 import dl_trainer673 file = os.path.splitext(filename)[0]674 a,bz = dl_trainer.dl_models(filename=file,model=15,aug=augmentation,epochs=epochs,validation_split=validation_split)675 global filename2676 filename2 = a677 global b678 b = bz679 return redirect(url_for('vgg13'))680 return render_template('upload_vgg13.html',something=str(b))681682@app.route('/vgg13_bn_classifier', methods=['GET', 'POST'])683def vgg13_bn():684 global filename685 if request.method == 'POST':686 print(request.form.getlist('augmentation')[0])687 try:688 augmentation = int(request.form.getlist('augmentation')[0])689 except:690 augmentation = 1691 try:692 epochs = int(request.form.getlist('epochs')[0])693 except:694 epochs = 4695 print(request.form.getlist('epochs')[0])696 print(request.form.getlist('validation_split')[0])697 validation_split = float(request.form.getlist('validation_split')[0])698 import dl_trainer699 file = os.path.splitext(filename)[0]700 a,bz = dl_trainer.dl_models(filename=file,model=16,aug=augmentation,epochs=epochs,validation_split=validation_split)701 global filename2702 filename2 = a703 global b704 b = bz705 return redirect(url_for('vgg13_bn'))706 return render_template('upload_vgg13_bn.html',something=str(b))707708@app.route('/vgg16_classifier', methods=['GET', 'POST'])709def vgg16():710 global filename711 if request.method == 'POST':712 print(request.form.getlist('augmentation')[0])713 try:714 augmentation = int(request.form.getlist('augmentation')[0])715 except:716 augmentation = 1717 try:718 epochs = int(request.form.getlist('epochs')[0])719 except:720 epochs = 4721 print(request.form.getlist('epochs')[0])722 print(request.form.getlist('validation_split')[0])723 validation_split = float(request.form.getlist('validation_split')[0])724 import dl_trainer725 file = os.path.splitext(filename)[0]726 a,bz = dl_trainer.dl_models(filename=file,model=17,aug=augmentation,epochs=epochs,validation_split=validation_split)727 global filename2728 filename2 = a729 global b730 b = bz731 return redirect(url_for('vgg16'))732 return render_template('upload_vgg16.html',something=str(b))733734@app.route('/vgg16_bn_classifier', methods=['GET', 'POST'])735def vgg16_bn():736 global filename737 if request.method == 'POST':738 print(request.form.getlist('augmentation')[0])739 try:740 augmentation = int(request.form.getlist('augmentation')[0])741 except:742 augmentation = 1743 try:744 epochs = int(request.form.getlist('epochs')[0])745 except:746 epochs = 4747 print(request.form.getlist('epochs')[0])748 print(request.form.getlist('validation_split')[0])749 validation_split = float(request.form.getlist('validation_split')[0])750 import dl_trainer751 file = os.path.splitext(filename)[0]752 a,bz = dl_trainer.dl_models(filename=file,model=18,aug=augmentation,epochs=epochs,validation_split=validation_split)753 global filename2754 filename2 = a755 global b756 b = bz757 return redirect(url_for('vgg16_bn'))758 return render_template('upload_vgg16_bn.html',something=str(b))759760@app.route('/vgg19_classifier', methods=['GET', 'POST'])761def vgg19():762 global filename763 if request.method == 'POST':764 print(request.form.getlist('augmentation')[0])765 try:766 augmentation = int(request.form.getlist('augmentation')[0])767 except:768 augmentation = 1769 try:770 epochs = int(request.form.getlist('epochs')[0])771 except:772 epochs = 4773 print(request.form.getlist('epochs')[0])774 print(request.form.getlist('validation_split')[0])775 validation_split = float(request.form.getlist('validation_split')[0])776 import dl_trainer777 file = os.path.splitext(filename)[0]778 a,bz = dl_trainer.dl_models(filename=file,model=19,aug=augmentation,epochs=epochs,validation_split=validation_split)779 global filename2780 filename2 = a781 global b782 b = bz783 return redirect(url_for('vgg19'))784 return render_template('upload_vgg19.html',something=str(b))785786@app.route('/vgg19_bn_classifier', methods=['GET', 'POST'])787def vgg19_bn():788 global filename789 if request.method == 'POST':790 print(request.form.getlist('augmentation')[0])791 try:792 augmentation = int(request.form.getlist('augmentation')[0])793 except:794 augmentation = 1795 try:796 epochs = int(request.form.getlist('epochs')[0])797 except:798 epochs = 4799 print(request.form.getlist('epochs')[0])800 print(request.form.getlist('validation_split')[0])801 validation_split = float(request.form.getlist('validation_split')[0])802 import dl_trainer803 file = os.path.splitext(filename)[0]804 a,bz = dl_trainer.dl_models(filename=file,model=20,aug=augmentation,epochs=epochs,validation_split=validation_split)805 global filename2806 filename2 = a807 global b808 b = bz809 return redirect(url_for('vgg19_bn'))810 return render_template('upload_vgg19_bn.html',something=str(b))811812if __name__ == "__main__":813 app.run() ...

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NeuralNetUtil.py

Source:NeuralNetUtil.py Github

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1#utility functions for neural net project2import random3def getNNPenData(fileString="datasets/pendigits.txt", limit=100000):4 """5 returns limit # of examples from penDigits file6 """7 examples=[]8 data = open(fileString)9 lineNum = 010 for line in data:11 inVec = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]12 outVec = [0,0,0,0,0,0,0,0,0,0] #which digit is output13 count=014 for val in line.split(','):15 if count==16:16 outVec[int(val)] = 117 else:18 inVec[count] = int(val)/100.0 #need to normalize values for inputs19 count+=120 examples.append((inVec,outVec))21 lineNum += 122 if (lineNum >= limit):23 break24 return examples2526def getList(num,length):27 list = [0]*length28 list[num-1] = 129 return list30 31def getNNCarData(fileString ="datasets/car.data.txt", limit=100000 ):32 """33 returns limit # of examples from file passed as string34 """35 examples=[]36 attrValues={}37 data = open(fileString)38 attrs = ['buying','maint','doors','persons','lug_boot','safety']39 attr_values = [['vhigh', 'high', 'med', 'low'],40 ['vhigh', 'high', 'med', 'low'],41 ['2','3','4','5more'],42 ['2','4','more'],43 ['small', 'med', 'big'],44 ['high', 'med', 'low']]45 46 attrNNList = [('buying', {'vhigh' : getList(1,4), 'high' : getList(2,4), 'med' : getList(3,4), 'low' : getList(4,4)}),47 ('maint',{'vhigh' : getList(1,4), 'high' : getList(2,4), 'med' : getList(3,4), 'low' : getList(4,4)}),48 ('doors',{'2' : getList(1,4), '3' : getList(2,4), '4' : getList(3,4), '5more' : getList(4,4)}),49 ('persons',{'2' : getList(1,3), '4' : getList(2,3), 'more' : getList(3,3)}),50 ('lug_boot',{'small' : getList(1,3),'med' : getList(2,3),'big' : getList(3,3)}),51 ('safety',{'high' : getList(1,3), 'med' : getList(2,3),'low' : getList(3,3)})]5253 classNNList = {'unacc' : [1,0,0,0], 'acc' : [0,1,0,0], 'good' : [0,0,1,0], 'vgood' : [0,0,0,1]}54 55 for index in range(len(attrs)):56 attrValues[attrs[index]]=attrNNList[index][1]5758 lineNum = 059 for line in data:60 inVec = []61 outVec = []62 count=063 for val in line.split(','):64 if count==6:65 outVec = classNNList[val[:val.find('\n')]]66 else:67 inVec.append(attrValues[attrs[count]][val])68 count+=169 examples.append((inVec,outVec))70 lineNum += 171 if (lineNum >= limit):72 break73 random.shuffle(examples)74 return examples757677def buildExamplesFromPenData(size=10000):78 """79 build Neural-network friendly data struct80 81 pen data format82 16 input(attribute) values from 0 to 10083 10 possible output values, corresponding to a digit from 0 to 98485 """86 if (size != 10000):87 penDataTrainList = getNNPenData("datasets/pendigitsTrain.txt",int(.8*size))88 penDataTestList = getNNPenData("datasets/pendigitsTest.txt",int(.2*size))89 else : 90 penDataTrainList = getNNPenData("datasets/pendigitsTrain.txt")91 penDataTestList = getNNPenData("datasets/pendigitsTest.txt")92 return penDataTrainList, penDataTestList939495def buildExamplesFromCarData(size=200):96 """97 build Neural-network friendly data struct98 99 car data format100 | names file (C4.5 format) for car evaluation domain101102 | class values - 4 value output vector103104 unacc, acc, good, vgood105106 | attributes107108 buying: vhigh, high, med, low.109 maint: vhigh, high, med, low.110 doors: 2, 3, 4, 5more.111 persons: 2, 4, more.112 lug_boot: small, med, big.113 safety: low, med, high.114 """115 carData = getNNCarData()116 carDataTrainList = []117 for cdRec in carData:118 tmpInVec = []119 for cdInRec in cdRec[0] :120 for val in cdInRec :121 tmpInVec.append(val)122 #print "in :" + str(cdRec) + " in vec : " + str(tmpInVec)123 tmpList = (tmpInVec, cdRec[1])124 carDataTrainList.append(tmpList)125 #print "car data list : " + str(carDataList)126 tests = len(carDataTrainList)-size127 carDataTestList = [carDataTrainList.pop(random.randint(0,tests+size-t-1)) for t in xrange(tests)]128 return carDataTrainList, carDataTestList129 130131def buildPotentialHiddenLayers(numIns, numOuts):132 """133 This builds a list of lists of hidden layer layouts134 numIns - number of inputs for data135 some -suggestions- for hidden layers - no more than 2/3 # of input nodes per layer, and136 no more than 2x number of input nodes total (so up to 3 layers of 2/3 # ins max137 """138 resList = []139 tmpList = []140 maxNumNodes = max(numOuts+1, 2 * numIns)141 if (maxNumNodes > 15):142 maxNumNodes = 15143144 for lyr1cnt in range(numOuts,maxNumNodes):145 for lyr2cnt in range(numOuts-1,lyr1cnt+1):146 for lyr3cnt in range(numOuts-1,lyr2cnt+1):147 if (lyr2cnt == numOuts-1):148 lyr2cnt = 0149 150 if (lyr3cnt == numOuts-1):151 lyr3cnt = 0152 tmpList.append(lyr1cnt)153 tmpList.append(lyr2cnt)154 tmpList.append(lyr3cnt)155 resList.append(tmpList)156 tmpList = []157 return resList158159def getNNExtraData(fileString ="datasets/extra.txt", limit=10000 ):160 """161 returns limit # of examples from file passed as string162 """163 examples=[]164 attrValues={}165 data = open(fileString)166 attrs = ['top-left-square','top-middle-square','top-right-square','middle-left-square','middle-middle-square','middle-right-square',167 'bottom-left-square', 'bottom-middle-square', 'bottom-right-square']168 attr_values = [['x', 'o', 'b'],169 ['x', 'o', 'b'],170 ['x', 'o', 'b'],171 ['x', 'o', 'b'],172 ['x', 'o', 'b'],173 ['x', 'o', 'b'],174 ['x', 'o', 'b'],175 ['x', 'o', 'b'],176 ['x', 'o', 'b']]177 178 attrNNList = [('top-left-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),179 ('top-middle-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),180 ('top-right-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),181 ('middle-left-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),182 ('middle-middle-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),183 ('middle-right-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),184 ('bottom-left-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),185 ('bottom-middle-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)}),186 ('bottom-right-square', {'x' : getList(1,3), 'o' : getList(2,3), 'b' : getList(3,3)})]187188 classNNList = {'positive': [1,0], 'negative': [0,1]}189 190 for index in range(len(attrs)):191 attrValues[attrs[index]]=attrNNList[index][1]192193 lineNum = 0194 for line in data:195 inVec = []196 outVec = []197 count=0198 for val in line.split(','):199 if count==9:200 #print(lineNum)201 outVec = classNNList[val.strip()]202 else:203 inVec.append(attrValues[attrs[count]][val])204 count+=1205 examples.append((inVec,outVec))206 lineNum += 1207 if (lineNum >= limit):208 break209 random.shuffle(examples)210 return examples211212def getNNXORData(fileString="datasets/xordata.txt", limit=100000):213 """214 returns limit # of examples from penDigits file215 """216 examples=[]217 attrValues={}218 data = open(fileString)219 attrs = ['X','Y']220 attr_values = [['0', '1'], ['0', '1']]221 222 attrNNList = [('X', {'0' : getList(1,2), '1' : getList(2,2)}), ('Y',{'0' : getList(1,2), '1' : getList(2,2)})]223224 classNNList = {'0' : [1,0], '1' : [0,1]}225 226 for index in range(len(attrs)):227 attrValues[attrs[index]]=attrNNList[index][1]228229 lineNum = 0230 for line in data:231 inVec = []232 outVec = []233 count=0234 for val in line.split(','):235 if count==2:236 #print(type(val[:val.find('\n')]))237 outVec = classNNList[val.strip()]238 else:239 inVec.append(attrValues[attrs[count]][val])240 count+=1241 examples.append((inVec,outVec))242 lineNum += 1243 if (lineNum >= limit):244 break245 return examples246247def buildExamplesFromXORData(size=4):248 """249 build Neural-network friendly data struct250 251 pen data format252 16 input(attribute) values from 0 to 100253 10 possible output values, corresponding to a digit from 0 to 9254255 """256 XORData = getNNXORData()257 XORDataTrainList = []258 for cdRec in XORData:259 tmpInVec = []260 for cdInRec in cdRec[0] :261 for val in cdInRec :262 tmpInVec.append(val)263 #print "in :" + str(cdRec) + " in vec : " + str(tmpInVec)264 tmpList = (tmpInVec, cdRec[1])265 XORDataTrainList.append(tmpList)266 #print "car data list : " + str(carDataList)267 #tests = len(XORDataTrainList)-size268 XORDataTestList = XORDataTrainList269 return XORDataTrainList, XORDataTestList270271def buildExamplesFromExtraData(size = 200):272 extraData = getNNExtraData()273 extraDataTrainList = []274 for cdRec in extraData:275 tmpInVec = []276 for cdInRec in cdRec[0] :277 for val in cdInRec :278 tmpInVec.append(val)279 #print "in :" + str(cdRec) + " in vec : " + str(tmpInVec)280 tmpList = (tmpInVec, cdRec[1])281 extraDataTrainList.append(tmpList)282 #print "car data list : " + str(carDataList)283 tests = len(extraDataTrainList)-size284 extraDataTestList = [extraDataTrainList.pop(random.randint(0,tests+size-t-1)) for t in xrange(tests)]285 return extraDataTrainList, extraDataTestList ...

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bitter.cgi

Source:bitter.cgi Github

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...24# --------------------------------------------------------------------------------------------25# Functions to handle all the back-end functions i.e. controller26# Each html form is an "operation" and the following ifs handle each operation appropriately27# --------------------------------------------------------------------------------------------28if "operation" in form and "Log In" in form.getlist("operation"):29 if userActions.checkLogin(form.getfirst("username"), form.getfirst("password")):30 cookieCreated = userActions.createSessionCookie(form.getfirst("username"))31 html = viewPage.userHome(form.getfirst("username"))32 else:33 html = viewPage.message("Incorrect Username or Password")34 35elif "operation" in form and "Sign Up" in form.getlist("operation"):36 if userActions.signUp(form.getfirst("username"), form.getfirst("fullName"), 37 form.getfirst("email"), form.getfirst("password")):38 html = viewPage.message("Account Created, Verification email sent")39 else:40 html = viewPage.message("Sorry that username is unavailable")41elif "operation" in form and "Log Out" in form.getlist("operation"):42 if userActions.isLoggedIn():43 userActions.logOut(userActions.getCurrentUser()) 44 cookieCreated = userActions.deleteSessionCookie()45 loginFile = open("html/login.html","r")46 html = loginFile.read()47 48elif "operation" in form and "search" in form.getlist("operation"):49 html = viewPage.search(form.getfirst("searchQuery"))50elif "operation" in form and "View Profile" in form.getlist("operation"):51 html = viewPage.userProfile(form.getfirst("username"))52elif "operation" in form and "Home" in form.getlist("operation"):53 html = viewPage.userHome(userActions.getCurrentUser())54elif "operation" in form and "Bleat" in form.getlist("operation"):55 bleatActions.insertBleat(userActions.getCurrentUser(),form.getfirst("bleatStr"))56 html = viewPage.userHome(userActions.getCurrentUser()) 57 #html = viewPage.test(userActions.getCurrentUser(), form.getfirst("bleatStr"))58elif "operation" in form and "Unlisten" in form.getlist("operation"):59 userActions.unlisten(form.getfirst("username"))60 html = viewPage.userProfile(form.getfirst("username")) # TEMP61 #show msg page/use js instead62elif "operation" in form and "Listen" in form.getlist("operation"):63 userActions.listen(form.getfirst("username"))64 html = viewPage.userProfile(form.getfirst("username")) #TEMP65 #show msg page/ use js instead66elif "operation" in form and "Reply" in form.getlist("operation"):67 html = viewPage.reply(form.getfirst("bleatID"))68elif "operation" in form and "Reply To Bleat" in form.getlist("operation"):69 bleatActions.replyToBleat(userActions.getCurrentUser(), form.getfirst("bleatid"), 70 form.getfirst("bleatStr"))71 html = viewPage.userHome(userActions.getCurrentUser())72elif "operation" in form and "verify" in form.getlist("operation"):73 userActions.verify(form.getfirst("user"))74 html = viewPage.message("Verified!")75 #show msg page76elif "operation" in form and "Settings" in form.getlist("operation"):77 html = viewPage.settings()78elif "operation" in form and "Update Account" in form.getlist("operation"): # TO DO79 userActions.updateAccount(userActions.getCurrentUser(), form.getfirst("password"), form.getfirst("email"), form.getfirst("fullName"), form.getfirst("homeLatitude"), form.getfirst("homeLongitude"),80 form.getfirst("homeSuburb"), form.getfirst("profileText"))81 html = viewPage.settings()82elif "operation" in form and "Suspend Account" in form.getlist("operation"):83 userActions.suspendAccount(userActions.getCurrentUser())84 html = viewPage.settings()85 86elif "operation" in form and "Unsuspend Account" in form.getlist("operation"):87 userActions.unsuspendAccount(userActions.getCurrentUser())88 html = viewPage.settings()89 90elif "operation" in form and "Delete Account" in form.getlist("operation"): #UNTESTED91 user = userActions.getCurrentUser()92 userActions.logOut(user) 93 userActions.deleteAccount(user)94 cookieCreated = userActions.deleteSessionCookie()95 loginFile = open("html/login.html","r")96 html = loginFile.read()97elif "operation" in form and "RecoverPassword" in form.getlist("operation"):98 if userActions.recoverPassword(form.getfirst("username")):99 html = viewPage.message("An email has been sent to your email address with instructions on how to recover your password")100 else:101 html = viewPage.message("Username doesn't exist")102elif "operation" in form and "Forgot Password" in form.getlist("operation"):103 forgotPassFile = open("html/forgotPassword.html")104 html = forgotPassFile.read()105elif "operation" in form and "resetPasswordView" in form.getlist("operation"):106 html = viewPage.resetPasswordView(form.getfirst("user"))107elif "operation" in form and "resetPassword" in form.getlist("operation"):108 userActions.resetPassword(form.getfirst("username"), form.getfirst("password"))109 loginFile = open("html/login.html","r")110 html = loginFile.read()111 112elif userActions.isLoggedIn(): #otherwise show the user their home page113 html = viewPage.userHome(userActions.getCurrentUser())114else: #otherwise show the login page115 loginFile = open("html/login.html","r")116 html = loginFile.read()117# Printing the HTML generated from the controller above118httpHeader(cookieCreated)...

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restaurantLike.js

Source:restaurantLike.js Github

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1let cnt = 0;2let tmpNum = -1;3function locationLoadSuccess(pos) {4 // 현재 위치 받아오기5 var currentPos = new kakao.maps.LatLng(pos.coords.latitude, pos.coords.longitude);6 mylat = pos.coords.latitude;7 mylong = pos.coords.longitude;8 console.log("현재위치 : " + currentPos + " 타입: " + typeof currentPos);9 restaurantSearch(mylat, mylong);10};11function locationLoadError(pos) {12 alert('위치 정보를 가져오는데 실패했습니다.');13};14function searchRes(likelist) {15 console.log(likelist);16 navigator.geolocation.getCurrentPosition(locationLoadSuccess, locationLoadError);17};18function restaurantSearch(myY, myX) {19 console.log("**" + myY, myX);20 $.ajax({21 method: 'GET'22 , url: "https://dapi.kakao.com/v2/local/search/keyword.json"23 , data: {24 query: $("#restName").val() //사용자가 검색한 키워드25 , category_group_code: "FD6" //음식점 필터링26 , x: myX //중심좌표 X27 , y: myY //중심좌표 Y28 , sort: "distance" //거리순29 }30 , headers: {Authorization: "KakaoAK f1b2afc29adbbda05eea78825d075ca9"}31 })32 .done(function (data) {33 var getList = "";34 getList += "<table style='display:inline-block; border-collapse: separate; border-spacing: 0 10px;'>"35 for (var i = 0; i < data.documents.length; i++) {36 getList += "<tr>"37 getList += "<td> <a href='/detail?restaurant_id=" + data.documents[i].id + "' style='color:blue; text-decoration: none;'>" + data.documents[i].place_name + "</a></td>"38 getList += "<input type='hidden' value='" + data.documents[i].id + "'>"39 getList += "<input type='hidden' value='" + data.documents[i].place_name + "'>"40 getList += "<input type='hidden' value='" + data.documents[i].x + "'>"41 getList += "<input type='hidden' value='" + data.documents[i].y + "'>"42 getList += "<td>" + data.documents[i].phone + "</td>"43 getList += "<td>" + data.documents[i].category_name + "</td>"44 getList += "<td>" + data.documents[i].address_name + "</td>"45 getList += "<td>"46 getList += "<img src='like/default_like.png' id='likeimg" + i + "' width='50' height='50' onclick='javascript:saveData(" + i + ")' style='cursor: pointer'>"47 getList += "</td>"48 getList += "</tr>"49 }50 getList += "</table>"51 $("#restList").html("");52 $("#restList").html(getList);53 })54}55function enterkey() {56 if (window.event.keyCode == 13) {57 searchRes();58 }59}60function saveData(num) {61 var no = num * 5;62 var inputNum = num * 4 + 1;63 /*var str1 = document.getElementsByTagName('td')[no].childNodes[0].nodeValue; // id64 var str2 = document.getElementsByTagName('td')[no + 1].childNodes[0].nodeValue; // name*/65 var str3 = document.getElementsByTagName('td')[no + 1].childNodes[0].nodeValue; // phone66 var str4 = document.getElementsByTagName('td')[no + 3].childNodes[0].nodeValue; // address67 var str1 = document.getElementsByTagName('input')[inputNum + 1].value; // id68 var str2 = document.getElementsByTagName('input')[inputNum + 2].value; // name69 var x = document.getElementsByTagName('input')[inputNum + 3].value; // x70 var y = document.getElementsByTagName('input')[inputNum + 4].value; // y71 /*alert(x + ", "+ y);72 alert("ID: " + str1 + ", NAME: "+str2 + ", phone: "+str3 + ", 주소: "+str4);*/73 const dataStr = {74 "restaurant_id": str1,75 "restaurant_name": str2,76 "phone": str3,77 "x": x,78 "y": y,79 "address": str480 }81 if (document.getElementById('likeimg' + num).src === "http://localhost:8084/like/default_like.png") {82 cnt += 1;83 $.ajax({84 method: 'POST',85 url: "/like",86 contentType: "application/json",87 dataType: "json",88 data: JSON.stringify(dataStr),89 success: function (data) {90 console.log("찜하기 성공");91 },92 error: function (xhr, status, error) {93 console.log("에러발생");94 }95 });96 document.getElementById('likeimg' + num).src = 'like/like.png'97 }98 else {99 $.ajax({100 method: 'GET',101 url: "/myLikeDelete",102 contentType: "application/json",103 dataType: "json",104 data: {'restaurant_id': str1},105 success: function (data) {106 // if(data.proc == "success") {107 // console.log("DB 저장완료")108 // }109 },110 error: function (xhr, status, error) {111 console.log("에러발생");112 }113 });114 cnt = 0;115 document.getElementById('likeimg' + num).src = 'like/default_like.png'116 }117 tmpNum = num;118 location.href = "#";119}120function checkdata(str){121 $.ajax({122 method: 'POST',123 url: "/checklikes",124 contentType: "application/json",125 dataType: "json",126 data: {'restaurant_id': str},127 success: function (data) {128 // if(data.proc == "success") {129 // console.log("DB 저장완료")130 // }131 return true;132 },133 error: function (xhr, status, error) {134 console.log("에러발생!!");135 }136 });137 return false;...

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