63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
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import os
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import torch
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from unidecode import unidecode
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import get_path_file
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from ia.model import NeuralNet
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from ia.nltk_utils import bag_of_words, tokenize
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path = os.path.dirname(get_path_file.__file__)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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file = path + "/ia/trained_model.pth"
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data = torch.load(file, map_location=device)
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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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def get_tag_for_sentence(input_sentence):
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"""
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Return the matching tag of the input_sentence given in parameter.
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It usually is what the STT engine recognise or what the user's type when using no-voice mode
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Parameters
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----------
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input_sentence is your sentence
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Returns tag from the intents.json file
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-------
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"""
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sentence = unidecode(input_sentence) # convert accent to better recognition
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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() > 0.75 and len(sentence) > 2:
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return "MATCHING INTENT : " + tag + " (" + str(prob.item()) + ")"
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# return intents.intents.get_matching_intent_for_tag(tag).get('tag')
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else:
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return 'dont_understand'
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if __name__ == '__main__':
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print(get_tag_for_sentence("Hey, est il"))
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