NLG v2
This commit is contained in:
Wojciech Lidwin
parent
8542f23871
commit
9a0c37e4cf
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@ -0,0 +1,342 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4af8e091",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"class Rules_DST(): \n",
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"\n",
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" def __init__(self):\n",
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" self.state = json.load(open('data.json'))\n",
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"\n",
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" def update_user(self, user_acts=None):\n",
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" for intent, domain, slot, value in user_acts:\n",
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" domain = domain.lower()\n",
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" intent = intent.lower()\n",
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" slot = slot.lower()\n",
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" if intent == 'start_conversation':\n",
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" continue\n",
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"\n",
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" elif intent == 'end_conversation':\n",
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" self.state = json.load(open('data.json'))\n",
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" elif domain not in self.state['belief_state']:\n",
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" continue\n",
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" \n",
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" \n",
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" elif 'inform' in intent:\n",
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" if (slot == 'inform'):\n",
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" continue\n",
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" \n",
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" if(domain in slot):\n",
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" slot.replace(domain + \"/\", '')\n",
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"\n",
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" domain_dic = self.state['belief_state'][domain]\n",
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" if slot in domain_dic:\n",
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" self.state['belief_state'][domain][slot] = value\n",
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" \n",
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" \n",
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" elif intent == 'request':\n",
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" if domain not in self.state['request_state']:\n",
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" self.state['request_state'][domain] = {}\n",
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" if slot not in self.state['request_state'][domain]:\n",
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" self.state['request_state'][domain][slot] = 0\n",
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" else:\n",
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" self.state['request_state'][domain][slot] = value\n",
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" \n",
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" elif intent == 'start_conversation':\n",
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" self.state[\"user_action\"].append([intent, domain, slot, value])\n",
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" continue\n",
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"\n",
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" elif intent == 'end_conversation':\n",
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" self.state = json.load(open('data.json'))\n",
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" \n",
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" self.state[\"user_action\"].append([intent, domain, slot, value])\n",
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" return self.state"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "09903205",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'user_action': [],\n",
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" 'system_action': [],\n",
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" 'belief_state': {'food': {'name': '',\n",
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" 'type': '',\n",
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" 'price range': '',\n",
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" 'size': '',\n",
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" 'ingredients': ''},\n",
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" 'drink': {'name': '', 'price range': '', 'size': ''},\n",
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" 'sauce': {'name': '', 'price range': '', 'size': ''},\n",
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" 'order': {'type': '',\n",
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" 'price range': '',\n",
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" 'restaurant_name': '',\n",
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" 'area': '',\n",
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" 'book time': '',\n",
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" 'book day': ''},\n",
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" 'booking': {'restaurant_name': '',\n",
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" 'area': '',\n",
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" 'book time': '',\n",
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" 'book day': '',\n",
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" 'book people': ''},\n",
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" 'payment': {'type': '', 'amount': '', 'vat': ''}},\n",
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" 'request_state': {},\n",
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" 'terminated': False,\n",
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" 'history': []}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst = Rules_DST()\n",
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"dst.state"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ec2b40d2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst.state['user_action']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ca5ec2f3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'name': '', 'type': '', 'price range': '', 'size': '', 'ingredients': ''}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst.update_user([['star_conversation',\"\",\"\",\"\"], ['inform', 'drink', 'size', 'duża']])\n",
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"dst.state['belief_state']['food']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2a36fa8c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['inform', 'drink', 'size', 'duża']]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst.state['user_action']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "67fd77b2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'drink': {'price range': 0}}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst.update_user([['request', 'drink', 'price range', '?']])\n",
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"dst.state['request_state']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "834ebb03",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'name': '',\n",
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" 'type': 'pizza',\n",
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" 'price range': '',\n",
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" 'size': 'duża',\n",
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" 'ingredients': ''}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst.update_user([['inform', 'food', 'type', 'pizza'], ['inform', 'food', 'size', 'duża']])\n",
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"dst.state['belief_state']['food']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "4b61083c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"import jmespath\n",
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"\n",
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"class DP():\n",
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" def __init__(self):\n",
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" with open('database.json', encoding='utf-8-sig') as json_file:\n",
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" self.db = json.load(json_file)\n",
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" \n",
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"\n",
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" def predict(self, state):\n",
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" self.results = []\n",
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" system_action = defaultdict(list)\n",
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" user_action = defaultdict(list)\n",
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" system_acts = []\n",
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" for idx in range(len(state['user_action'])):\n",
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" intent, domain, slot, value = state['user_action'][idx]\n",
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" user_action[(domain, intent)].append((slot, value))\n",
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"\n",
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" for user_act in user_action:\n",
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" system_acts.append(self.update_system_action(user_act, user_action, state, system_action))\n",
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" state['system_action'] = system_acts\n",
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" return system_acts[-1]\n",
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"\n",
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"\n",
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" def update_system_action(self, user_act, user_action, state, system_action):\n",
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" \n",
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" domain, intent = user_act \n",
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" \n",
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" #Reguła 3\n",
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" if intent == 'end_conversation':\n",
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" return None\n",
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" \n",
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" constraints = [(slot, value) for slot, value in state['belief_state'][domain].items() if value != '']\n",
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" \n",
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" # Reguła 1\n",
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" if intent == 'request':\n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" for slot in user_action[user_act]: \n",
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" if slot[0] in self.results[0]:\n",
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" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])\n",
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"\n",
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" # Reguła 2\n",
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" elif intent == 'inform':\n",
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" if len(constraints)>1:\n",
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" arg=f\"{constraints[0]}\".replace(f\"\\'{constraints[0][0]}\\'\",f\"{constraints[0][0]}\")\n",
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" arg = arg.replace(\"[\",\"\").replace(\"]\",\"\")\n",
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" for cons in constraints[1:]:\n",
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" arg+=f\" && contains{cons}\".replace(f\"\\'{cons[0]}\\'\",f\"{cons[0]}\").replace(\"[\",\"\").replace(\"]\",\"\")\n",
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" else:\n",
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" arg=f\"{constraints}\".replace(f\"\\'{constraints[0]}\\'\",f\"{constraints[0]}\").replace(\"[\",\"\").replace(\"]\",\"\").replace(\"(\\'\",\"(\").replace(\"\\',\",\",\") \n",
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" self.results = jmespath.search(f\"database.{domain}[?contains{arg} == `true` ]\", self.db) \n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
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" choice = self.results[0]\n",
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"\n",
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" if domain in [\"food\", \"drink\", \"sauce\"]:\n",
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" system_action[(domain, 'Recommend')].append(['Name', choice['name']])\n",
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" elif domain in [\"order\", \"booking\", \"payment\"]:\n",
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" system_action[(domain, 'Recommend')].append(['Type', choice['type']])\n",
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" return system_action\n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "e587661a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"defaultdict(list,\n",
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" {('drink', 'Inform'): [['Choice', '1'],\n",
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" ['price range', 'średnia']],\n",
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" ('drink', 'Recommend'): [['Name', 'lemoniada']],\n",
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" ('food', 'Inform'): [['Choice', '4']],\n",
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" ('food', 'Recommend'): [['Name', 'pizza margherita']]})"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dp= DP()\n",
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"dp.predict(dst.state)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -282,8 +282,10 @@
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" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
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" choice = self.results[0]\n",
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"\n",
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" if domain in [\"food\", \"drink\", \"police\", \"sauce\", \"order\", \"booking\", \"payment\"]:\n",
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" if domain in [\"food\", \"drink\", \"sauce\"]:\n",
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" system_action[(domain, 'Recommend')].append(['Name', choice['name']])\n",
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" elif domain in [\"order\", \"booking\", \"payment\"]:\n",
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" system_action[(domain, 'Recommend')].append(['Type', choice['type']])\n",
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" return system_action\n",
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" \n"
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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83
NLG.ipynb
83
NLG.ipynb
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@ -3,7 +3,7 @@
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "9a772dd1",
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"id": "c31a012f",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -12,8 +12,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "54502dca",
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"execution_count": 28,
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"id": "21c8e0a4",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -69,21 +69,36 @@
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" if slot == \"book time\":\n",
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" return f\"Godzina Twojego zamówienia to: {value}.\"\n",
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" if slot == \"book day\":\n",
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" return f\"Dzień Twojego zamówienia to: {value}.\"\n",
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" \n",
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" return f\"Dzień Twojego zamówienia to: {value}.\" \n",
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"\n",
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" elif intent == 'end_conversation':\n",
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" return random.choice(['Żegnam i dziękuję za skorzystanie z usługi.', 'Do widzenia.', \"Dziękuję za skorzystanie z usługi, do kolejnego razu.\"])\n",
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" elif intent == 'start_conversation':\n",
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" return random.choice(['Witaj, jestem asystentem dialogowym, dzięki któremu złożysz zamówienie w restuaracji.', 'Cześć, w czym mogę pomóc?', \"Witam w usłudze, w czym mogę pomóc?\"])\n",
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" \n",
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" "
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" elif intent == 'affirm':\n",
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" return random.choice(['Tak.', 'Potwierdzam.', \"Mozliwe.\"])\n",
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" elif intent == 'deny':\n",
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" return random.choice(['Nie.', 'Nie potwierdzam.', \"Niemożliwe.\"]) \n",
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" elif intent == 'recommend' and slot==\"name\":\n",
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" if domain==\"food\":\n",
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" return f\"Rekomendowany posiłek to: {value}.\"\n",
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" if domain==\"drink\":\n",
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" return f\"Rekomendowany napój to: {value}.\" \n",
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" if domain==\"sauce\":\n",
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" return f\"Rekomendowany sos to: {value}.\"\n",
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" elif intent == 'recommend' and slot==\"type\":\n",
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" if domain==\"booking\":\n",
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" return f\"Rekomenduję następującą rezerwację stolika: {value}.\" \n",
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" if domain==\"order\":\n",
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" return f\"Rekomenduję takie zamówienie: {value}.\"\n",
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" if domain==\"payment\":\n",
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" return f\"Rekomendowana metoda płatności to: {value}.\" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "8568ac20",
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"id": "185b85f0",
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"metadata": {},
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"outputs": [
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{
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@ -104,7 +119,7 @@
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "c3fa168f",
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"id": "b276b31f",
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"metadata": {},
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"outputs": [
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{
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@ -125,7 +140,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "5ac835de",
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"id": "3ae7e57f",
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"metadata": {},
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"outputs": [
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{
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|
|
@ -146,7 +161,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "6c06463d",
|
||||
"id": "90303149",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -167,7 +182,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "aeaa9968",
|
||||
"id": "b78e9d4a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -185,10 +200,52 @@
|
|||
"nlg(['start_conversation', '', '', ''])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "37782f98",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Rekomendowany posiłek to: Zupa grzybowa.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nlg(['recommend', 'food', 'name', 'zupa grzybowa'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "c74610a3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Rekomendowana metoda płatności to: blik.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nlg(['recommend', 'payment', 'type', 'blik'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e48c1960",
|
||||
"id": "1f6859ea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
|
|
|
|||
Loading…
Reference in New Issue