modified basic chatbot to include grabbing information from entities

src/semantic/classes.txt

@@ -1,9 +1,11 @@
-adverse_drug
-blood_pressure
 blood_pressure_search
+exit
 goodbye
 greeting
 hospital_search
+navigation
 options
 pharmacy_search
 thanks
+navigation
+exit

src/semantic/gui_chatbot.py

@@ -1,8 +1,11 @@
+# from python example and tutorial here: https://data-flair.training/blogs/python-chatbot-project/
+
 import nltk
 from nltk.stem import WordNetLemmatizer
 lemmatizer = WordNetLemmatizer()
 import pickle
 import numpy as np
+import spacy
 
 from keras.models import load_model
 model = load_model('chatbot_model.h5')
@@ -12,7 +15,6 @@ intents = json.loads(open('intents.json').read())
 words = pickle.load(open('words.pkl','rb'))
 classes = pickle.load(open('classes.pkl','rb'))
 
-
 def clean_up_sentence(sentence):
     # tokenize the pattern - splitting words into array
     sentence_words = nltk.word_tokenize(sentence)
@@ -26,10 +28,10 @@ def bag_of_words(sentence, words, show_details=True):
     # tokenizing patterns
     sentence_words = clean_up_sentence(sentence)
     # bag of words - vocabulary matrix
-    bag = [0]*len(words)  
+    bag = [0]*len(words)
     for s in sentence_words:
         for i,word in enumerate(words):
-            if word == s: 
+            if word == s:
                 # assign 1 if current word is in the vocabulary position
                 bag[i] = 1
                 if show_details:
@@ -52,40 +54,78 @@ def predict_class(sentence):
 def getResponse(ints, intents_json):
     tag = ints[0]['intent']
     list_of_intents = intents_json['intents']
+    print("ints")
+    print(ints)
     for i in list_of_intents:
         if(i['tag']== tag):
             result = random.choice(i['responses'])
             break
     return result
 
+def getInfo(sentence):
+    doc = nlp(sentence)
+    start = 0
+    end = 0
+    startBuilding = "random location"
+    stopBuilding = "random location"
+    for token in doc:
+        if token.pos_ == "PROPN" and start == 1:
+            startBuilding = token.text
+        elif token.pos_ == "PROPN" and end == 1:
+            stopBuilding = token.text
+        elif token.text == "to":
+            start = 0
+            end = 1
+        elif token.text == "from":
+            start = 1
+            end = 0
+        else:
+            pass
+            # print(token.text)
+    return [startBuilding, stopBuilding]
+
 
 #Creating tkinter GUI
 import tkinter
 from tkinter import *
 
 def send():
-    msg = EntryBox.get("1.0",'end-1c').strip()
+    msgClean = EntryBox.get("1.0",'end-1c')
+    msg = msgClean.strip()
     EntryBox.delete("0.0",END)
 
     if msg != '':
         ChatBox.config(state=NORMAL)
         ChatBox.insert(END, "You: " + msg + '\n\n')
         ChatBox.config(foreground="#446665", font=("Verdana", 12 ))
-    
+
         ints = predict_class(msg)
-        res = getResponse(ints, intents)
-        
-        ChatBox.insert(END, "Bot: " + res + '\n\n')
-            
+        if ints[0]['intent'] == "navigation":
+            building = getInfo(msgClean)
+            #TODO: Check if buildings are available
+            res = "Now navigating to " + building[1] + " from " + building[0]
+            #TODO: START CONVERSION TO GPS COORDINATES
+        elif ints[0]['intent'] == "exit":
+            res = getResponse(ints, intents)
+            #TODO: STOP EVERYTHING
+        else:
+            res = getResponse(ints, intents)
+        ChatBox.insert(END, "Belatrix: " + res + '\n\n')
+
         ChatBox.config(state=DISABLED)
         ChatBox.yview(END)
- 
+
 
 root = Tk()
 root.title("Chatbot")
 root.geometry("400x500")
 root.resizable(width=FALSE, height=FALSE)
 
+#import nlp dictionary
+nlp = spacy.load("en_core_web_sm")
+nltk.download('punkt')
+nltk.download('wordnet')
+
 #Create Chat window
 ChatBox = Text(root, bd=0, bg="white", height="8", width="50", font="Arial",)
 

src/semantic/intents.json

@@ -21,53 +21,18 @@
         },
         {"tag": "options",
          "patterns": ["How you could help me?", "What you can do?", "What help you provide?", "How you can be helpful?", "What support is offered"],
-         "responses": ["I can guide you through Adverse drug reaction list, Blood pressure tracking, Hospitals and Pharmacies", "Offering support for Adverse drug reaction, Blood pressure, Hospitals and Pharmacies"],
+         "responses": ["I can take you to multiple buildings including BBB, EECS, and more on north campus."],
          "context": [""]
         },
         {"tag": "navigation",
-         "patterns": ["How to check Adverse drug reaction?", "Open adverse drugs module", "Give me a list of drugs causing adverse behavior", "List all drugs suitable for patient with adverse reaction", "Which drugs dont have adverse reaction?" ],
+         "patterns": ["Can you take me to the ", "Open adverse drugs module", "Give me a list of drugs causing adverse behavior", "List all drugs suitable for patient with adverse reaction", "Which drugs dont have adverse reaction?" ],
          "responses": ["Navigating to Adverse drug reaction module"],
-         "context": [""]
+         "context": ["navigation_to_building"]
         },
         {"tag": "exit",
-         "patterns": ["Open blood pressure module", "Task related to blood pressure", "Blood pressure data entry", "I want to log blood pressure results", "Blood pressure data management" ],
-         "responses": ["Navigating to Blood Pressure module"],
-         "context": [""]
-        },
-        {"tag": "blood_pressure_search",
-         "patterns": ["I want to search for blood pressure result history", "Blood pressure for patient", "Load patient blood pressure result", "Show blood pressure results for patient", "Find blood pressure results by ID" ],
-         "responses": ["Please provide Patient ID", "Patient ID?"],
-         "context": ["search_blood_pressure_by_patient_id"]
-        },
-        {"tag": "search_blood_pressure_by_patient_id",
-         "patterns": [],
-         "responses": ["Loading Blood pressure result for Patient"],
-         "context": [""]
-        },
-        {"tag": "pharmacy_search",
-         "patterns": ["Find me a pharmacy", "Find pharmacy", "List of pharmacies nearby", "Locate pharmacy", "Search pharmacy" ],
-         "responses": ["Please provide pharmacy name"],
-         "context": ["search_pharmacy_by_name"]
-        },
-        {"tag": "search_pharmacy_by_name",
-         "patterns": [],
-         "responses": ["Loading pharmacy details"],
-         "context": [""]
-        },
-        {"tag": "hospital_search",
-         "patterns": ["Lookup for hospital", "Searching for hospital to transfer patient", "I want to search hospital data", "Hospital lookup for patient", "Looking up hospital details" ],
-         "responses": ["Please provide hospital name or location"],
-         "context": ["search_hospital_by_params"]
-        },
-        {"tag": "search_hospital_by_params",
-         "patterns": [],
-         "responses": ["Please provide hospital type"],
-         "context": ["search_hospital_by_type"]
-        },
-        {"tag": "search_hospital_by_type",
-         "patterns": [],
-         "responses": ["Loading hospital details"],
-         "context": [""]
+         "patterns": ["stop", "quit", "end", "I want to stop navigation"],
+         "responses": ["Ending current navigation"],
+         "context": ["navigation_to_building"]
         }
    ]
 }

src/semantic/pickleManage.py

@@ -21,12 +21,9 @@ def updatePickle(filename, pklList):
     pickle_in = open(filename + '.pkl',"rb")
     currDict = pickle.load(pickle_in)
     f = open(filename + '.pkl', 'wb')   # Pickle file is newly created where foo1.py is
-    pickle.dump(currDict|pklList, f)          # dump data to f
+    pickle.dump(currDict + pklList, f)          # dump data to f
     f.close()
 
-printPickle("classes")
-printPickle("words")
-
 # Example usage
 #   createPickle('test', {'Bart', 'Lisa', 'Milhouse', 'Nelson'})
 #   updatePickle('test', {'Theo'})

src/semantic/train_chatbot.py

@@ -17,6 +17,10 @@ ignore_letters = ['!', '?', ',', '.']
 intents_file = open('intents.json').read()
 intents = json.loads(intents_file)
 
+# download nltk resources
+nltk.download('punkt')
+nltk.download('wordnet')
+
 for intent in intents['intents']:
     for pattern in intent['patterns']:
         #tokenize each word

src/semantic/words.txt

@@ -85,3 +85,8 @@ what
 which
 with
 you
+navigation
+map
+locate
+navigate
+building