import torch from torch.utils.data import Dataset,DataLoader import torch.nn as nn import nltk from nltk.stem.porter import PorterStemmer import json import numpy as np import random def ExecuteQuery(query): class NeuralNet(nn.Module): def __init__(self,input_size,hidden_size,num_classes): super(NeuralNet,self).__init__() self.l1 = nn.Linear(input_size,hidden_size) self.l2 = nn.Linear(hidden_size,hidden_size) self.l3 = nn.Linear(hidden_size,num_classes) self.relu = nn.ReLU() def forward(self,x): out = self.l1(x) out = self.relu(out) out = self.l2(out) out = self.relu(out) out = self.l3(out) return out device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') with open('files/intents.json', 'r') as json_data: intents = json.load(json_data) FILE = "files/intents.pth" data = torch.load(FILE) # with open('Data/Tasks.pth') as f: # data = torch.load(f) 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() Stemmer = PorterStemmer() def tokenize(sentence): return nltk.word_tokenize(sentence) def stem(word): return Stemmer.stem(word.lower()) def bag_of_words(tokenized_sentence,words): sentence_word = [stem(word) for word in tokenized_sentence] bag = np.zeros(len(words),dtype=np.float32) for idx , w in enumerate(words): if w in sentence_word: bag[idx] = 1 return bag sentence = str(query) 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() >= 0.96: for intent in intents['intents']: if tag == intent["tag"]: reply = random.choice(intent["responses"]) return reply, tag, prob.item() if prob.item() <= 0.95: reply = "opencosmo" tag = "opencosmo" return reply, tag, prob.item() def test(): query = input("Enter your query: ") reply = ExecuteQuery(query) print(f"Cosmo: {reply[0]}" ) print(f"Tag: {reply[1]}") print(f"Prob: {reply[2]}") while True: test()