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import torch |
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from torch.utils.data import Dataset,DataLoader |
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import torch.nn as nn |
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import nltk |
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from nltk.stem.porter import PorterStemmer |
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import json |
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import numpy as np |
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import random |
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nltk.download('punkt') |
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def ExecuteQuery(query): |
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class NeuralNet(nn.Module): |
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def __init__(self,input_size,hidden_size,num_classes): |
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super(NeuralNet,self).__init__() |
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self.l1 = nn.Linear(input_size,hidden_size) |
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self.l2 = nn.Linear(hidden_size,hidden_size) |
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self.l3 = nn.Linear(hidden_size,num_classes) |
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self.relu = nn.ReLU() |
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def forward(self,x): |
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out = self.l1(x) |
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out = self.relu(out) |
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out = self.l2(out) |
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out = self.relu(out) |
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out = self.l3(out) |
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return out |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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with open('intents.json', 'r') as json_data: |
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intents = json.load(json_data) |
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FILE = "intents.pth" |
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data = torch.load(FILE) |
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input_size = data["input_size"] |
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hidden_size = data["hidden_size"] |
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output_size = data["output_size"] |
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all_words = data["all_words"] |
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tags = data["tags"] |
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model_state = data["model_state"] |
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model = NeuralNet(input_size,hidden_size,output_size).to(device) |
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model.load_state_dict(model_state) |
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model.eval() |
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Stemmer = PorterStemmer() |
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def tokenize(sentence): |
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return nltk.word_tokenize(sentence) |
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def stem(word): |
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return Stemmer.stem(word.lower()) |
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def bag_of_words(tokenized_sentence,words): |
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sentence_word = [stem(word) for word in tokenized_sentence] |
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bag = np.zeros(len(words),dtype=np.float32) |
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for idx , w in enumerate(words): |
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if w in sentence_word: |
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bag[idx] = 1 |
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return bag |
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sentence = str(query) |
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sentence = tokenize(sentence) |
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X = bag_of_words(sentence,all_words) |
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X = X.reshape(1,X.shape[0]) |
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X = torch.from_numpy(X).to(device) |
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output = model(X) |
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_ , predicted = torch.max(output,dim=1) |
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tag = tags[predicted.item()] |
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probs = torch.softmax(output,dim=1) |
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prob = probs[0][predicted.item()] |
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if prob.item() >= 0.96: |
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for intent in intents['intents']: |
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if tag == intent["tag"]: |
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reply = random.choice(intent["responses"]) |
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return reply, tag, prob.item() |
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if prob.item() <= 0.95: |
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reply = "opencosmo" |
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tag = "opencosmo" |
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return reply, tag, prob.item() |
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