| """This script processes and formats ECS logs from stdin using rich""" | |
| # /// script | |
| # dependencies = ["rich"] | |
| # /// | |
| import json | |
| import sys | |
| import re | |
| from datetime import datetime |
Restaurant Bot is a chatbot taking orders for meals.
A meal is composed of a starter, a main dish and a dessert. Furthermore, you have a point system where you pay a fixed price of 8$ giving you 6 points you can spread across your meal.
There is several option for starter :
| from kadot.classifiers import BayesClassifier | |
| # This is a tiny dataset collected on the title of IMDB reviews of "Star Wars: The Force Awakens" | |
| train = { | |
| "Star Wars fans win again": 'positive', | |
| "Greatest movie of all time": 'positive', | |
| "Yes, it really is that good.": 'positive', | |
| "Beyond incredible!": 'positive', | |
| "This is the best Star Wars movie ever.": 'positive', | |
| "Far and way the greatest film of 2015.": 'positive', |
| from kadot.bot_engine import ConversationNode | |
| from kadot.models import EntityRecognizer | |
| from kadot.vectorizers import VectorDict | |
| import random | |
| glove = VectorDict.load_glove(open('glove/glove.6B.50d.txt')) | |
| city_recognizer = EntityRecognizer({ | |
| "I live in Paris ": ('Paris',), | |
| "The city where I live is London": ('London',), | |
| "in berlin, there is many things to do": ('berlin',), |
| import torch | |
| from torch.autograd import Variable | |
| from torch.nn import MSELoss | |
| points = [ | |
| (0, 20), | |
| (2, 16), | |
| (6, 14), | |
| (4, 10), | |
| (10, 10), |
| from collections import Counter | |
| def append_a_graph(graph_dict: dict, key: str, value) -> dict: | |
| """ | |
| Append a value into a graph | |
| :param graph_dict: the Graph.dict value | |
| :param key: the name of the point | |
| :param value: the name of the point to add | |
| :type value: set or list |
