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import random
import json
import torch
from nltk_utils import bag_of_words, tokenize
from run_segbot import get_model
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)
#with open('model.pickle', 'rb') as f:
# model = pickle.load(f)
model = get_model()
model.eval()
bot_name = "Sam"
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)
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