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import random
import json
import torch
from model import NeuralNet
from nltk_utils import bag_of_words, tokenize
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with open('intents.json', 'r') as json_data:
intents = json.load(json_data)
FILE = 'data.pth'
data = torch.load(FILE)
input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_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 = "Anis"
# print("Let's chat! (type 'quit' to exit)")
# 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)
# _, 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}: I do not understand...")
def get_response(msg):
sentence = tokenize(msg)
X = bag_of_words(sentence, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X).to(device)
output = model(X)
_, 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"]:
return random.choice(intent['responses'])
return "I do not understand..."
if __name__ == "__main__":
print("Let's chat! (type 'quit' to exit)")
while True:
# sentence = "do you use credit cards?"
sentence = input("You: ")
if sentence == "quit":
break
resp = get_response(sentence)
print(resp) |