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)