def home():
if request.method == 'POST':
file = request.files['file'].read()
result = predictor.predict(file)
return result[0]
else:
return render_template('index.html')
def serialized_prediction(df):
response = pd.DataFrame(np.vstack(
[predictor.predict(df),
predictor.predict_proba(df).max(axis=1)]).T,
columns=["Survived", "Probability"],
index=df['passengerid'])
return response.to_json(orient='index')
def index():
if request.method=='POST':
lyrics = request.form['song_text']
lyrics = str(lyrics)
result_string, results_probability = pred.predict(lyrics)
return render_template('show_results.html', result_string=result_string, result_probability=results_probability)
else:
return render_template('index.html')
def start():
if request.method == 'POST':
f = request.files['file']
df = pd.read_csv(f)
y = predict(df)
df = pd.concat([df, y], axis=1)
now = datetime.datetime.now()
filepath = 'files/{}.csv'.format(now.strftime("%Y-%m-%d %H:%M:%S"))
df.to_csv(filepath)
return render_template('finished.html', url='/{}'.format(filepath))
return render_template('start.html')
import pandas
import os
import settings
from clusterGenerator.clusterController import generateCluster
print(os.getcwd())
from predictor.predictor import predict
Main_Path = os.path.join(settings.default_path, 'data')
os.chdir(Main_Path)
resulting_input_cluster = []
print("Loading test data")
input_data = pandas.read_csv('yelp_labelled.csv', delimiter="\t")
trunc_input_data = input_data.iloc[0:20, 0:2]
trunc_input_labels = trunc_input_data.iloc[:, 1].values.tolist()
trunc_input_reviews = input_data.iloc[0:20, 0:2].values.tolist()
print("Loaded")
trunc_input_data_length = trunc_input_data.shape[0]
for sentence in trunc_input_data.itertuples():
# for i, sentence in enumerate(trunc_input_reviews):
# print(sentence)
resulting_input_cluster.append(generateCluster(sentence._1, sentence.Index))
# resulting_input_cluster.append(generateCluster(sentence, i))
# print(resulting_input_cluster)
predict(resulting_input_cluster , trunc_input_labels)
def testing(predictor, data, labels):
predictions = predictor.predict(data)
print "Test accuracy: %.1f%%" % accuracy(predictions, labels)