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chat.py
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chat.py
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import random
import json
import torch
import numpy as np
from model import NeuralNet
import nltk
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
with open("intents.json","r") as f:
intents = json.load(f)
def tokenize(sentence):
return nltk.word_tokenize(sentence)
def stem(word):
return stemmer.stem(word.lower())
def bag_of_words(sentence,all_words):
sentence = [stem(w) for w in sentence]
bag = np.zeros(len(all_words))
#print(sentence)
#print(all_words)
for index,w in enumerate(all_words):
if w in sentence:
#print(w)
bag[index] = 1.0
return bag
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
FILE = "data.pth"
data = torch.load(FILE)
input_size = data["input_size"]
output_size = data["output_size"]
hidden_size = data["hidden_size"]
all_words = data["all_words"]
tags = data["tags"]
model_state = data["model_state"]
model = NeuralNet(input_size,hidden_size,output_size).to(device)
model.load_state_dict(model_state)
model.eval()
bot_name = "BOT"
while True:
sentence = input("You: ")
if(sentence == "quit"):
break
sentence = tokenize(sentence)
x = bag_of_words(sentence,all_words)
x = x.reshape(1,x.shape[0])
x = torch.from_numpy(x).to(device)
output = model(x.float())
_, predicted = torch.max(output, dim = 1)
tag = tags[predicted.item()]
probs = torch.softmax(output,dim = 1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents['intents']:
if tag == intent["tag"]:
print(f"{bot_name}: {random.choice(intent['responses'])}")
else:
print(f"{bot_name}: Sorry! I do not understand.\nIf you wish to talk to our customer executive please click here and we will contact you shortly.\nThank You!")