32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
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import requests
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class MachineLearningNLG:
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def __init__(self):
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self.model_name = "./nlg_model" # Ścieżka do wytrenowanego modelu
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
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self.generator = pipeline('text2text-generation', model=self.model, tokenizer=self.tokenizer)
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def translate_text(self, text, target_language='pl'):
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url = 'https://translate.googleapis.com/translate_a/single?client=gtx&sl=auto&tl={}&dt=t&q={}'.format(target_language, text)
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response = requests.get(url)
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if response.status_code == 200:
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translated_text = response.json()[0][0][0]
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return translated_text
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else:
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return text # Zwracamy oryginalny tekst w razie problemów z tłumaczeniem
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def nlg(self, system_act):
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input_text = f"generate text: {system_act}"
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result = self.generator(input_text)
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response = result[0]['generated_text']
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translated_response = self.translate_text(response, target_language='pl')
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return translated_response
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if __name__ == "__main__":
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nlg = MachineLearningNLG()
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system_act = "inform(date.from=15.07, date.to=22.07)"
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print(nlg.nlg(system_act))
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