def predict(): preproses() td = TFIDF([xdata, ydata]) clasification = [] # Receives the input query from form if request.method == 'POST': namequery = request.form['namequery'] spliter = namequery.split(',') for row in spliter: clasification.append(testFromTrained([td.transform(row)])) print(clasification) keras.clear_session() labels, values = np.unique(clasification, return_counts=True) lbls, vals = np.unique(clasification, return_counts=True) pie_labels = labels pie_values = values colors = ["#F7464A", "#46BFBD"] return render_template('hasil.html', set=zip(values, labels, colors), clasification=zip(spliter, clasification), legenda=zip(lbls, vals))
def upload_file(): if request.method == 'POST': if 'file' not in request.files: flash('Not file part') # return redirect(request.url) file = request.files['file'] if file.filename == '': flask('not select file') # return redirect(request.url) if file and allowed_file(file.filename): filename = secure_filename(file.filename) file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) # return redirect(url_for('upload_file', filename=filename)) print(filename) fold = "data/" + filename print(fold) with open(fold, 'r') as csv_par: preproses() td = TFIDF([xdata, ydata]) clasification = [] csv_reader = csv_par.read().split('\n') for row in csv_reader: clasification.append(testFromTrained([td.transform(row)])) keras.clear_session() labels, values = np.unique(clasification, return_counts=True) lbls, vals = np.unique(clasification, return_counts=True) pie_labels = labels pie_values = values colors = ["#F7464A", "#46BFBD"] return render_template('hasil.html', set=zip(values, labels, colors), clasification=zip(csv_reader, clasification), legenda=zip(lbls, vals))
def parsing(): with open('data/test.csv', 'r') as csv_par: preproses() td = TFIDF([xdata, ydata]) rowdata = [] clasification = [] csv_reader = csv_par.read().split('\n') for row in csv_reader: rowdata.append(row) clasification.append(testFromTrained([td.transform(row)])) keras.clear_session() labels, values = np.unique(clasification, return_counts=True) lbls, vals = np.unique(clasification, return_counts=True) pie_labels = labels pie_values = values colors = ["#F7464A", "#46BFBD"] return render_template('hasil.html', set=zip(values, labels, colors), clasification=zip(csv_reader, clasification), legenda=zip(lbls, vals))
json_file.close() model = model_from_json(loaded_model_json) # load weights into new self.model model.load_weights("model/model.h5") print("Loaded model from disk") sgd = SGD(lr=0.01) model.compile(loss='binary_crossentropy', optimizer=sgd) return getBinaryResult(model.predict_proba(np.array(x))) preproses() td = TFIDF([xdata, ydata]) # TRAINING # train(td.getOnlyX(), ydata) # RETRAINING # retrain_model(td.getOnlyX(), ydata) # TESTING test = "ahok itu pemimpin yang beres memimpin" print test print testFromTrained([td.transform(test)]) test = "ahok itu pemimpin yang ga beres memimpin" print test print testFromTrained([td.transform(test)])