NLP{ class for modeling

nlp.py

from transformers import (
    pipeline,
    GPT2LMHeadModel,
    GPT2Tokenizer
)

class NLP:
    def __init__(self):
        self.gen_model = GPT2LMHeadModel.from_pretrained('gpt2')
        self.gen_tokenizer = GPT2Tokenizer.from_pretrained('gpt2') 
         
    def generate(self, prompt="The epistemelogical limit"):
        
        inputs = self.gen_tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt")
        prompt_length = len(self.gen_tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
        outputs = self.gen_model.generate(inputs, max_length=200, do_sample=True, top_p=0.95, top_k=60)
        generated = prompt + self.gen_tokenizer.decode(outputs[0])[prompt_length:]
        return generated

    def sentiments(self, text: str):
        nlp = pipeline("sentiment-analysis")
        result = nlp(text)[0]
        return f"label: {result['label']}, with score: {round(result['score'], 4)}"