mi_clase.py

class Persona:
    def __init__(self,nombre,edad):
        self.nombre = nombre
        self.edad = edad
        
    def get_nomber(self):
        return self.nombre

persona = Persona("josue",33)


# os.environ['CUDA_VISIBLE_DEVICES'] ='0'
import torch
from transformers import AutoModelForMultipleChoice
from transformers import AutoTokenizer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from transformers import AutoTokenizer, AutoModel


GENERATOR_MODEL = "JosueElias/pipeline_generator_model"
GENERATOR_TOKENIZER = "JosueElias/pipeline_generator_tokenizer"
QUERY_MODEL = "JosueElias/pipeline_query_model"
QUERY_TOKENIZER = "JosueElias/pipeline_query_tokenizer"

DEVICE = "cpu" # cpu or cuda

class Pipeline:
    
    #---- init class
    
    def __init__(self):
        self.model = AutoModelForMultipleChoice.from_pretrained(GENERATOR_MODEL)
        self.tokenizer = AutoTokenizer.from_pretrained(GENERATOR_TOKENIZER)
        self.semModel = AutoModel.from_pretrained(QUERY_MODEL)   
        self.semTokenizer = AutoTokenizer.from_pretrained(QUERY_TOKENIZER)
        self.device = torch.device(DEVICE)

        self.semModel.to(self.device)
        self.model.to(self.device)
    
    #---- utils functions
    
    def convert_to_letter(self,a):
        if a == 0:
            return "A"
        if a==1:
            return "B"
        if a==2:
            return "C"
        if a==3:
            return "D"
        if a==4:
            return "E"
    
    def filter_stopwords(self,example_sent):
        stop_words = set(stopwords.words('english'))
        word_tokens = word_tokenize(example_sent)
        filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]
        return " ".join(filtered_sentence)
    
    def cls_pooling(self,model_output):
        return model_output.pooler_output#last_hidden_state[:, 0]

    def get_embeddings(self,text_list):
        encoded_input = self.semTokenizer(
            text_list, padding=True, truncation=True, return_tensors="pt",max_length =512
        )
        encoded_input = {k: v.to(self.device) for k, v in encoded_input.items()}
        model_output = self.semModel(**encoded_input)
        return self.cls_pooling(model_output)
        
    #---- retriever
    
    def get_context_from_text(self,question):
        question_embedding = self.get_embeddings([question]).cpu().detach().numpy()
        scores, samples = datasetx.get_nearest_examples(
            "embeddings", question_embedding, k=5
        )
        samples_df = pd.DataFrame.from_dict(samples)
        samples_df["scores"] = scores
        samples_df.sort_values("scores", ascending=False, inplace=True)
        contexts = ""
#         aux_row = ""
        for _, row in samples_df.iterrows():
            contexts = contexts + f"=={row.section}== {row.text} " 

#             if aux_row =={row.title}:
#                 contexts = contexts + f"=={row.section}== {row.text}" 
#             else:
#                 contexts = contexts + f"==={row.title}=== =={row.section}== {row.text}" 
#             aux_row = {row.title}
        return contexts

    #---- generator
    
    # [CLS] context #### question? [SEP] answer [SEP]
    def create_tokens(self,quetion_and_options,context):
        question = quetion_and_options["prompt"]
        candidate1 = "#### "+question +  " [SEP] "+quetion_and_options["A"]+  " [SEP]"
        candidate2 = "#### "+question +  " [SEP] "+quetion_and_options["B"]+  " [SEP]"
        candidate3 = "#### "+question +  " [SEP] "+quetion_and_options["C"]+  " [SEP]"
        candidate4 = "#### "+question +  " [SEP] "+quetion_and_options["D"]+  " [SEP]"
        candidate5 = "#### "+question +  " [SEP] "+quetion_and_options["E"]+  " [SEP]"
        prompt = "[CLS]"+ context

        inputs = self.tokenizer([
            [prompt, candidate1], 
            [prompt, candidate2], 
            [prompt, candidate3], 
            [prompt, candidate4], 
            [prompt, candidate5]
        ], return_tensors="pt", padding=True,truncation="only_first",max_length =512,add_special_tokens=False)
        labels = torch.tensor(0).unsqueeze(0)
        return (inputs,labels)
        
    def infer_answer(self,mi_tupla):
        (inputs,labels) = mi_tupla
        
        inputs = {k: v.to(self.device) for k, v in inputs.items()}
        labels = labels.to(self.device)
        
        outputs = self.model(**{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels)
        logits = outputs.logits
        _, topk_indices = torch.topk(logits, k=3, dim=1)
        #predicted_class = logits.argmax().item()
        return topk_indices
    
    #---- retriever + generator

    def give_the_best_answer(self,dict_with_all_the_info):
        a = self.get_context_from_text(my_dict["prompt"])
        b = self.create_tokens(my_dict,a)
        c = self.infer_answer(b)
        d = self.convert_to_letter(int(c[0][0]))
        #print("\nThe answer is ",)
        return d
    
pipeline = Pipeline()