Reviewed code.

.gitignore

@@ -0,0 +1,4 @@
+*.tar.gz
+*.pyc
+results/
+models/

.gitignore/.gitignore

@@ -1,2 +0,0 @@
-*.tar.gz
-*.pyc

README

@@ -1,39 +0,0 @@
-# Readme
-
-The source code in this repository shows a simple rasa chatbot fallback mechanism, which can get relavent information from Zir-AI search platform.
-
-## Data
-
-reviews.db contains the hotel reviews, its generated by using repo https://github.com/amin3141/zir-souffle
-
-# How-to
-
-First you need to have the rasa shell installed. You can do that by following the rasa tutorials https://rasa.com/docs/rasa/installation
-
-Next is training the model
-
-```
-rasa train
-```
-
-Then to run in one terminal you need to run 
-
-```
-rasa run --credentials ./credentials.yml  --enable-api --auth-token XYZ123 --model ./models --endpoints ./endpoints.yml --cors "*"
-```
-
-And in the background or in another terminal run
-
-```
-rasa run actions
-```
-
-Lastly, to view the bot in the UI either simply load the `www/index.html` file in the browser or run `python3 server.py` and open `localhost:8080` in browser.
-
-You could also just run the two commands and talk with the bot in shell
-
-```
-rasa run actions &
-rasa shell
-```
-

README.md

@@ -0,0 +1,159 @@
+<!--lint disable no-literal-urls-->
+<p align="center">
+  <a href="https://zir-ai.com/">
+    <img
+      alt="Zir AI"
+      src="https://zir-ai.com/static/media/logo-light.637c616a.svg"
+      width="400"
+    />
+  </a>
+</p>
+
+# Readme
+
+The source code in this repository shows a simple Rasa chatbot fallback
+mechanism, which gets relevant information from ZIR Semantic Search.
+
+# Table of contents
+
+- [Rasa Bot & Zir](#why)
+- [Setup & Play](#setup-play)
+  - [Set-up Rasa](#setup-rasa)
+  - [Set-up repo](#setup-demo)
+  - [Running the demo](#run-demo)
+- [Customizing the bot](#bot-customization)
+  - [Data](#data)
+  - [Rasa Custom Action](#rasa-cust-action)
+
+## Rasa Bot & Zir
+
+[Rasa](https://rasa.com/) is the leading conversational AI platform, for
+personalized conversations at scale. Developers provide the questsions they
+expect the end customer to ask and Rasa bot, using AI, predicts and matches the
+what the customer intended to ask. With this information at hand, developers can
+easily match what the bot should respond.
+
+This is all well unless when the customer asks a valid question the developer
+did not expect, for such a scenario Rasa provides a
+[fallback mechanism](https://rasa.com/docs/rasa/fallback-handoff#fallbacks).
+
+> Although Rasa will generalize to unseen messages, some messages might receive
+> a low classification confidence. Using Fallbacks will help ensure that these
+> low confidence messages are handled gracefully, giving your assistant the
+> option to either respond with a default message or attempt to disambiguate the
+> user input.
+
+Using the mechanism the bot can ask fallback and search through the reviews
+uploaded in [Zir-AI](https://zir-ai.com) with the help of
+[custom actions](https://rasa.com/docs/rasa/custom-actions). If the reviews have
+the relavent information, it is shown as a result otherwise if the confidence of
+the question to review matching is low, we fallback to a generic statement.
+
+The end results sums up to be this:
+
+## Setup & Play
+
+You'll need to have Rasa installed and this repository. You might want to create
+a [virtual environment](#https://docs.python.org/3/library/venv.html) to isolate
+the dependencies rasa requires.
+
+### Set-up Rasa
+
+Follow the
+[rasa installation instrcutions](#https://rasa.com/docs/rasa/installation) or
+copy paste the following commands in terminal
+
+```bash
+pip3 install -U pip
+pip3 install rasa
+rasa --version
+```
+
+This should print the rasa version similar to follow
+
+```bash
+Rasa Version      :         2.7.0
+Minimum Compatible Version: 2.6.0
+Rasa SDK Version  :         2.7.0
+Rasa X Version    :         None
+Python Version    :         3.8.5
+Operating System  :         Linux
+Python Path       :         /bin/python3
+```
+
+> Windows & WSL works too
+
+### Set-up repo
+
+To install dependecies for this repository to work install python dependencies
+from requirements.txt
+
+```bash
+pip install -r requirements.txt
+python3 -m spacy download en_core_web_md
+```
+
+This will install the spacy model `en_core_web_md` this bot is configured with.
+Now you'll need to train the rasa bot
+
+```bash
+rasa train
+```
+
+### Running the demo
+
+While running the model, you are require to run the rasa action server along
+with the rasa bot. So in one terminal run
+
+```bash
+rasa run actions
+```
+
+In another terminal you can either play with the bot in your `shell` or on the
+browser.
+
+To talk to the bot in shell run
+
+```bash
+rasa shell
+```
+
+Or to talk to the bot in the browser run
+
+```bash
+rasa run --credentials ./credentials.yml  --enable-api --auth-token XYZ123 --model ./models --endpoints ./endpoints.yml --cors "*"
+```
+
+Now, open the `index.html` in your browser to talk to the bot.
+
+## Customizing the bot
+
+Use the following information to customize the bot and the source of data.
+
+### Data
+
+Data for the bot is sourced from [OpinRank](https://github.com/kavgan/OpinRank/)
+and reviews for Hotel Jumeirah is specifically used for this bot. Reviews are
+parsed from https://github.com/amin3141/zir-souffle. Use `hotels.py` to generate
+the output `json` and the `reviews.db`.
+
+Create a corpus on [Zir AI](http://zir-ai.com) and upload all the `json` files.
+Create an api key for access and note down the following information
+
+- Api key
+- corpus id
+- customer id
+
+## Rasa Custom Action
+
+Use the above collected information and replace the information in
+`actions/actions.py` and you're good to go.
+
+```python
+customer_id = "1835841754"
+corpus_id = 1
+header = {
+    "customer-id": customer_id,
+    "x-api-key": "zqt_WvU_2atFHgqYxxT2sQswwIUgogI8K3QeWs0oqA"
+}
+```

actions/actions.py

@@ -1,31 +1,3 @@
-# This files contains your custom actions which can be used to run
-# custom Python code.
-#
-# See this guide on how to implement these action:
-# https://rasa.com/docs/rasa/custom-actions
-
-
-# This is a simple example for a custom action which utters "Hello World!"
-
-# from typing import Any, Text, Dict, List
-#
-# from rasa_sdk import Action, Tracker
-# from rasa_sdk.executor import CollectingDispatcher
-#
-#
-# class ActionHelloWorld(Action):
-#
-#     def name(self) -> Text:
-#         return "action_hello_world"
-#
-#     def run(self, dispatcher: CollectingDispatcher,
-#             tracker: Tracker,
-#             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
-#
-#         dispatcher.utter_message(text="Hello World!")
-#
-#         return []
-
 from typing import Any, Text, Dict, List
 import json
 import requests
@@ -37,10 +9,17 @@ from rasa_sdk import Action, Tracker
 from rasa_sdk.events import UserUtteranceReverted
 from rasa_sdk.executor import CollectingDispatcher
 
+
 class ActionDefaultFallback(Action):
     """Executes the fallback action and goes back to the previous state
     of the dialogue"""
 
+    customer_id = "1835841754"
+    corpus_id = 1
+    header = {
+        "customer-id": customer_id,
+        "x-api-key": "zqt_WvU_2atFHgqYxxT2sQswwIUgogI8K3QeWs0oqA"
+    }
     con = None
 
     def name(self) -> Text:
@@ -63,22 +42,19 @@ class ActionDefaultFallback(Action):
                     "num_results": number_results,
                     "corpus_key": [
                         {
-                            "customer_id": "1526022105",
-                            "corpus_id": 40
+                            "customer_id": self.customer_id,
+                            "corpus_id": self.corpus_id
                         }
                     ]
                 }
             ]
         }
-        header = {
-            "customer-id": "1526022105",
-            "x-api-key": "zqt_WvU_2atFHgqYxxT2sQswwIUgogI8K3QeWs0oqA"
-        }
+
         payload = json.dumps(data_dict)
         response = requests.post(f"https://h.serving.zir-ai.io:443/v1/query",
                                  data=payload,
                                  verify=True,
-                                 headers=header,
+                                 headers=self.header,
                                  timeout=10)
         parsed = json.loads(response.text)
         first_resp = parsed["responseSet"][0]["response"][0]
@@ -87,21 +63,22 @@ class ActionDefaultFallback(Action):
         print(last_resp["score"])
         print(first_resp["score"])
         if last_resp["score"] < 0.3 or first_resp["score"] < 0.3:
-            textQuery.append("I'm sorry, I can't help you.")
+            textQuery.append(
+                "I'm sorry, I don't have any information about that.")
         else:
             textQuery = print_responses(parsed["responseSet"][0], self.cur)
-            # items = [d['text'] for d in parsed["responseSet"][0]["response"]]
-            textQuery.insert(0, "\n" )
-            textQuery.insert(0, "This is what i found in the reviews:" )
+            textQuery.insert(0, "\n")
+            textQuery.insert(0, "This is what i found in the reviews:")
         textQuery = "\n".join(textQuery)
         dispatcher.utter_message(text=textQuery)
+
         # Revert user message which led to fallback.
         return [UserUtteranceReverted()]
 
 
 def print_responses(response_set, sqlite_cursor):
     """Print responses to the console."""
-    textList = []
+    text_list = []
     for result in response_set["response"]:
         doc = response_set["document"][result["documentIndex"]]
         query = f"""
@@ -110,26 +87,17 @@ def print_responses(response_set, sqlite_cursor):
         """
         for row in sqlite_cursor.execute(query):
             title, date, hotel, fulltext = row
-            textList.append("**" + title + "**")
+            text_list.append("**" + title + "**")
             if is_title(result):
-                textList.append(f"{head(fulltext)}")
+                text_list.append(f"{head(fulltext)}")
             else:
                 t = result["text"]
-                textList.append(f"{highlight(fulltext, t)}")
-            textList.append("*" + f"{hotel_name(hotel)} reviewed on {date}" + "*")
-            textList.append("\n")
+                text_list.append(f"{highlight(fulltext, t)}")
+            text_list.append(
+                "*" + f"{" > \"Jumeirah Hotels & Resorts\" reviewed on {date}" + " *")
+            text_list.append("\n")
             break
-    return textList
-
-def hotel_name(hotel):
-    """Returns a human-readable name for a hotel."""
-    if hotel == "sheraton_fisherman_s_wharf_hotel":
-        return "Sheraton Fisherman's Wharf Hotel"
-    if hotel == "the_westin_st_francis":
-        return "The Westin St. Francis San Francisco on Union Square"
-    if hotel == "best_western_tuscan_inn_fisherman_s_wharf_a_kimpton_hotel":
-        return "Best Western Fishermans Wharf"
-    return hotel
+    return text_list
 
 
 def highlight(fulltext, snippet):

config.yml

@@ -3,9 +3,6 @@
 language: en
 
 pipeline:
-# # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
-# # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
   - name: SpacyNLP
   - name: SpacyTokenizer
   - name: SpacyFeaturizer

data/nlu.yml

@@ -92,9 +92,3 @@ nlu:
     - Rooms available
     - Book a room
     - Need a place to stay for the night
-
-- intent: out_of_scope
-  examples: |
-    - I want to order food
-    - What is 2 + 2?
-    - Who's the US President?

data/rules.yml

@@ -11,8 +11,3 @@ rules:
   steps:
   - intent: nlu_fallback
   - action: zir_action_fallback
-    
-- rule: out of scope
-  steps:
-  - intent: out_of_scope
-  - action: zir_action_fallback

domain.yml

@@ -19,10 +19,6 @@ responses:
   utter_greet:
   - text: "Hey! How are you?"
 
-  utter_cheer_up:
-  - text: "Here is something to cheer you up:"
-    image: "https://i.imgur.com/nGF1K8f.jpg"
-
   utter_did_that_help:
   - text: "Did that help you?"
 

requirements.txt

@@ -1,2 +1,3 @@
 sqlite3
-textwrap
+textwrap
+spacy

server.py

@@ -1,13 +0,0 @@
-import http.server
-import socketserver
-import os
-
-PORT = 8080
-
-web_dir = os.path.join(os.path.dirname(__file__), 'web')
-os.chdir(web_dir)
-
-Handler = http.server.SimpleHTTPRequestHandler
-httpd = socketserver.TCPServer(("0.0.0.0", PORT), Handler)
-print("serving at port", PORT)
-httpd.serve_forever()

tests/test_stories.yml

@@ -1,91 +1,26 @@
 #### This file contains tests to evaluate that your bot behaves as expected.
 #### If you want to learn more, please see the docs: https://rasa.com/docs/rasa/testing-your-assistant
-
 stories:
-- story: happy path 1
-  steps:
-  - user: |
-      hello there!
-    intent: greet
-  - action: utter_greet
-  - user: |
-      amazing
-    intent: mood_great
-  - action: utter_happy
-
-- story: happy path 2
-  steps:
-  - user: |
-      hello there!
-    intent: greet
-  - action: utter_greet
-  - user: |
-      amazing
-    intent: mood_great
-  - action: utter_happy
-  - user: |
-      bye-bye!
-    intent: goodbye
-  - action: utter_goodbye
 
-- story: sad path 1
+- story: A basic story test
   steps:
   - user: |
       hello
     intent: greet
   - action: utter_greet
   - user: |
-      not good
-    intent: mood_unhappy
-  - action: utter_cheer_up
-  - action: utter_did_that_help
-  - user: |
-      yes
-    intent: affirm
-  - action: utter_happy
+     Book a room for two please.
+    intent: reservations
+  - action: utter_reservation
+    
 
-- story: sad path 2
+- story: Fallback test
   steps:
   - user: |
       hello
     intent: greet
   - action: utter_greet
   - user: |
-      not good
-    intent: mood_unhappy
-  - action: utter_cheer_up
-  - action: utter_did_that_help
-  - user: |
-      not really
-    intent: deny
-  - action: utter_goodbye
-
-- story: sad path 3
-  steps:
-  - user: |
-      hi
-    intent: greet
-  - action: utter_greet
-  - user: |
-      very terrible
-    intent: mood_unhappy
-  - action: utter_cheer_up
-  - action: utter_did_that_help
-  - user: |
-      no
-    intent: deny
-  - action: utter_goodbye
-
-- story: say goodbye
-  steps:
-  - user: |
-      bye-bye!
-    intent: goodbye
-  - action: utter_goodbye
-
-- story: bot challenge
-  steps:
-  - user: |
-      are you a bot?
-    intent: bot_challenge
-  - action: utter_iamabot
+     Can we play golf?
+    intent: nlu_fallback
+  - action: zir_action_fallback