def predict():
user_id = g.user['id']
path = USER_PATH / str(g.user["id"])
list_names = [f for f in os.listdir(path) if os.path.isfile((path / f))]
annotation_list = []
annotation_db = UserData.get_annotation_file(g.user["id"])
for f in annotation_db:
annotation_list.append([f['file_name'], f['path']])
r = UserData.get_model(user_id)
if r['accuracy'] is None:
return redirect(url_for('modeling.index') + "?s=2")
features = r['features'].split(',')
trained_file = r['trained_file']
clasifier = r['clasifier']
accuracy = r['accuracy']
accuracy = str(round(float(accuracy), 2))
details = [features, trained_file, clasifier, accuracy]
return render_template("modeling/predict.html",
available_list=list_names,
details=details,
annotation_list=annotation_list)
def index():
annotation_list = []
path = USER_PATH / str(g.user["id"])
list_names = [f for f in os.listdir(path) if os.path.isfile((path / f))]
annotation_db = UserData.get_annotation_file(g.user["id"])
for f in annotation_db:
annotation_list.append([f['file_name'], f['path']])
if len(list_names) == 0:
flash("Error: You don't have uploaded file.")
return render_template("preprocess/step-1.html",
available_list=list_names,
annotation_list=annotation_list)
