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)])
