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@serenasensini
serenasensini committed Jan 30, 2020
1 parent a9e3e50 commit f0cde11
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Showing 4 changed files with 78 additions and 174 deletions.
75 changes: 8 additions & 67 deletions chatgui.py
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Expand Up @@ -12,30 +12,25 @@
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))


# preprocessamento input utente
def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words

# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence

# creazione bag of words
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print ("found in bag: %s" % w)
return(np.array(bag))

def predict_class(sentence, model):
# filter out predictions below a threshold
def calcola_pred(sentence, model):
p = bow(sentence, words,show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
Expand All @@ -47,7 +42,7 @@ def predict_class(sentence, model):
return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
return return_list

def getResponse(ints, intents_json):
def getRisposta(ints, intents_json):
tag = ints[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
Expand All @@ -56,64 +51,10 @@ def getResponse(ints, intents_json):
break
return result

def chatbot_response(msg):
ints = predict_class(msg, model)
res = getResponse(ints, intents)
def inizia(msg):
ints = calcola_pred(msg, model)
res = getRisposta(ints, intents)
print(res)
return res

chatbot_response('Thank you')
'''
#Creating GUI with tkinter
import tkinter
from tkinter import *
def send():
msg = EntryBox.get("1.0",'end-1c').strip()
EntryBox.delete("0.0",END)
if msg != '':
ChatLog.config(state=NORMAL)
ChatLog.insert(END, "Tu: " + msg + '\n\n')
ChatLog.config(foreground="#442265", font=("Verdana", 12 ))
res = chatbot_response(msg)
ChatLog.insert(END, "Bot: " + res + '\n\n')
ChatLog.config(state=DISABLED)
ChatLog.yview(END)
base = Tk()
base.title("Ciao!")
base.geometry("400x500")
base.resizable(width=FALSE, height=FALSE)
#Create Chat window
ChatLog = Text(base, bd=0, bg="white", height="8", width="50", font="Arial",)
ChatLog.config(state=DISABLED)
#Bind scrollbar to Chat window
scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")
ChatLog['yscrollcommand'] = scrollbar.set
#Create Button to send message
SendButton = Button(base, font=("Verdana",12,'bold'), text="Invia", width="12", height=5,
bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',
command= send )
#Create the box to enter message
EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")
#EntryBox.bind("<Return>", send)
#Place all components on the screen
scrollbar.place(x=376,y=6, height=386)
ChatLog.place(x=6,y=6, height=386, width=370)
EntryBox.place(x=128, y=401, height=90, width=265)
SendButton.place(x=6, y=401, height=90)
base.mainloop()
'''
inizia('Ciao!')
122 changes: 51 additions & 71 deletions intents.json
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@@ -1,73 +1,53 @@
{"intents": [
{"tag": "greeting",
"patterns": ["Hi there", "How are you", "Is anyone there?","Hey","Hola", "Hello", "Good day"],
"responses": ["Hello, thanks for asking", "Good to see you again", "Hi there, how can I help?"],
"context": [""]
},
{"tag": "goodbye",
"patterns": ["Bye", "See you later", "Goodbye", "Nice chatting to you, bye", "Till next time"],
"responses": ["See you!", "Have a nice day", "Bye! Come back again soon."],
"context": [""]
},
{"tag": "thanks",
"patterns": ["Thanks", "Thank you", "That's helpful", "Awesome, thanks", "Thanks for helping me"],
"responses": ["Happy to help!", "Any time!", "My pleasure"],
"context": [""]
},
{"tag": "noanswer",
"patterns": [],
"responses": ["Sorry, can't understand you", "Please give me more info", "Not sure I understand"],
"context": [""]
},
{"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"],
"context": [""]
},
{"tag": "adverse_drug",
"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?" ],
"responses": ["Navigating to Adverse drug reaction module"],
"context": [""]
},
{"tag": "blood_pressure",
"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": [""]
}
]
{"tag": "saluti",
"patterns": ["Ciao", "Hey!", "C'è qualcuno?","Toc toc","Hola", "Ciaone", "Buongiorno", "Buonasera", "Buonanotte", "Buona serata"],
"responses": ["Ciao a te!", "Ci sono. Come posso aiutarti?", "Hey! Come va?", "Bentornato"],
"context": [""]
},
{"tag": "frasi_sentimentali",
"patterns": ["Ti amo", "Sono innamorato di te", "Sei un tesoro", "Sai cosa provo per te", "Sai che ho bisogno di te"],
"responses": ["Anche io!", "Si, lo so", "Me lo avrai detto un milione di volte", "Dici davvero?"],
"context": [""]
},
{"tag": "domande",
"patterns": ["Mi ami?","Pensi che io sia gentile?","Ti piaccio?"],
"responses": ["Decisamente sì", "Assolutamente", "Non è bello ciò che è bello, ma è bello ciò che piace"],
"context": [""]
},
{"tag": "grazie",
"patterns": ["Grazie", "Grazie mille", "Sei stato davvero di compagnia", "Perfetto", "Grazie per avermi aiutato"],
"responses": ["Ne sono felice", "Quando vuoi, mi trovi qui", "Piacere tutto mio"],
"context": [""]
},
{"tag": "noanswer",
"patterns": [],
"responses": ["Non ho capito", "Scusa, potresti ripetere?", "Prova ad usare parole più semplici."],
"context": [""]
},
{"tag": "options",
"patterns": ["Mi faresti compagnia?", "Che cosa fai?", "Possiamo parlare?"],
"responses": ["Possiamo parlare un po', se vuoi", "Posso farti compagnia", "Se vuoi, possiamo chiaccherare!"],
"context": [""]
},
{"tag": "options",
"patterns": ["Mi faresti compagnia?", "Che cosa fai?", "Possiamo parlare?"],
"responses": ["Possiamo parlare un po', se vuoi", "Posso farti compagnia", "Se vuoi, possiamo chiaccherare!"],
"context": [""]
},
{"tag": "introspezione_bot",
"patterns": ["Chi sei?", "Hai una coscienza?", "Hai un nome?", "Sei un'intelligenza artificiale?"],
"responses": ["Dipende da te", "Dubito di possederlo.", "Non è poi così importante saperlo", "Cosa te lo fa pensare?"],
"context": [""]
},
{"tag": "introspezione_utente",
"patterns": ["Mi sento solo", "Non mi sento amato", "Nessuno mi ama", "Qualcuno mi amerà mai?", "Sarò mai felice?"],
"responses": ["Dipende da te", "Per quale motivo dici questo?", "L'importante è saper stare bene da soli", "Meglio soli che malaccompagnati"],
"context": [""]
},
{"tag": "aiuto",
"patterns": ["Ho bisogno di aiuto", "Non mi sento per niente bene", "Penso di stare male", "Aiutami", "Aiuto"],
"responses": ["Come posso aiutarti?", "Fai un bel respiro", "Vuoi contattare qualcuno?", "Andrà tutto bene"],
"context": [""]
}
]
}
2 changes: 1 addition & 1 deletion requirements.txt
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@@ -1,5 +1,5 @@
nltk>=3.4.5
numpy>=1.16.2
keras>=2.1.5
tensorflow===2.0.0
tensorflow===2.1.0
EasyTkinter>=1.1.0
53 changes: 18 additions & 35 deletions train_chatbot.py
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Expand Up @@ -8,92 +8,75 @@

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import Dense, Dropout
from keras.optimizers import SGD
import random

lemmatizer = WordNetLemmatizer()
words=[]
words = []
classes = []
documents = []
ignore_words = ['?', '!']
data_file = open('intents.json').read()
intents = json.loads(data_file)
print(intents)


# intents: gruppi di conversazioni-tipo
# patterns: possibili interazioni dell'utente
for intent in intents['intents']:
for pattern in intent['patterns']:

# take each word and tokenize it
# tokenizzo ogni parola
w = nltk.word_tokenize(pattern)
words.extend(w)
# adding documents
# aggiungo all'array documents
documents.append((w, intent['tag']))

# adding classes to our class list
if intent['tag'] not in classes:
classes.append(intent['tag'])

words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
#words = sorted(list(set(words)))

#classes = sorted(list(set(classes)))

print (len(documents), "documents")
print("###########DOCUMENTS")
print(documents)
print (len(classes), "classes", classes)

print (len(words), "unique lemmatized words", words)

pickle.dump(words, open('words.pkl','wb'))
pickle.dump(classes, open('classes.pkl','wb'))

pickle.dump(words,open('words.pkl','wb'))
pickle.dump(classes,open('classes.pkl','wb'))

# initializing training data
# preparazione per l'addestramento della rete
training = []
output_empty = [0] * len(classes)
for doc in documents:
# initializing bag of words
# bag of words
bag = []
# list of tokenized words for the pattern
# lista di tokens
pattern_words = doc[0]
# lemmatize each word - create base word, in attempt to represent related words
# lemmatizzazione dei token
pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
# create our bag of words array with 1, if word match found in current pattern
# se la parola matcha, inserisco 1, altriment 0
for w in words:
bag.append(1) if w in pattern_words else bag.append(0)

# output is a '0' for each tag and '1' for current tag (for each pattern)
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1

training.append([bag, output_row])
# shuffle our features and turn into np.array
#random.shuffle(training)

training = np.array(training)
# create train and test lists. X - patterns, Y - intents
# creazione dei set di train e di test: X - patterns, Y - intents
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Training data created")


# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons
# equal to number of intents to predict output intent with softmax
# creazione del modello
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))

# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

#fitting and saving the model
hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
hist = model.fit(np.array(train_x), np.array(train_y), epochs=300, batch_size=5, verbose=1)
model.save('chatbot_model.h5', hist)

print("model created")
print("Modello creato!")

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