models/handler.py

from models.pairwise_model import *
from features.text_utils import *
import regex as re
from models.bm25_utils import BM25Gensim
from models.qa_model import *
from tqdm.auto import tqdm
tqdm.pandas()
from datasets import load_dataset
from transformers import pipeline

class EndpointHandler():
    def __init__(self, path=""):
        df_wiki_windows = load_dataset("foxxy-hm/e2eqa-wiki",  data_files="processed/wikipedia_20220620_cleaned_v2.csv")["train"].to_pandas()
        df_wiki = load_dataset("foxxy-hm/e2eqa-wiki",  data_files="wikipedia_20220620_short.csv")["train"].to_pandas()
        df_wiki.title = df_wiki.title.apply(str)

        entity_dict = load_dataset("foxxy-hm/e2eqa-wiki",  data_files="processed/entities.json")["train"].to_dict()
        new_dict = dict()
        for key, val in entity_dict.items():
            val = val[0].replace("wiki/", "").replace("_", " ")
            entity_dict[key] = val
            key = preprocess(key)
            new_dict[key.lower()] = val
        entity_dict.update(new_dict)
        title2idx = dict([(x.strip(), y) for x, y in zip(df_wiki.title, df_wiki.index.values)])

        qa_model = QAEnsembleModel("nguyenvulebinh/vi-mrc-large", ["qa_model_robust.bin"], entity_dict)
        pairwise_model_stage1 = PairwiseModel("nguyenvulebinh/vi-mrc-base")
        pairwise_model_stage1.load_state_dict(torch.load("pairwise_v2.bin", map_location=torch.device('cpu')))
        pairwise_model_stage1.eval()

        pairwise_model_stage2 = PairwiseModel("nguyenvulebinh/vi-mrc-base")
        pairwise_model_stage2.load_state_dict(torch.load("pairwise_stage2_seed0.bin", map_location=torch.device('cpu')))

        bm25_model_stage1 = BM25Gensim("bm25_stage1/", entity_dict, title2idx)
        bm25_model_stage2_full = BM25Gensim("bm25_stage2/full_text/", entity_dict, title2idx)
        bm25_model_stage2_title = BM25Gensim("bm25_stage2/title/", entity_dict, title2idx)

        self.qa_model = qa_model
        self.pairwise_model_stage1 = pairwise_model_stage1
        self.pairwise_model_stage2 = pairwise_model_stage2
        self.bm25_model_stage1 = bm25_model_stage1
        self.bm25_model_stage2_full = bm25_model_stage2_full
        self.bm25_model_stage2_title = bm25_model_stage2_title
    
    def get_answer_e2e(self, question):
        query = preprocess(question).lower()
        top_n, bm25_scores = self.bm25_model_stage1.get_topk_stage1(query, topk=200)
        titles = [preprocess(df_wiki_windows.title.values[i]) for i in top_n]
        texts = [preprocess(df_wiki_windows.text.values[i]) for i in top_n]

        question = preprocess(question)
        ranking_preds = self.pairwise_model_stage1.stage1_ranking(question, texts)
        ranking_scores = ranking_preds * bm25_scores

        best_idxs = np.argsort(ranking_scores)[-10:]
        ranking_scores = np.array(ranking_scores)[best_idxs]
        texts = np.array(texts)[best_idxs]
        best_answer = self.qa_model(question, texts, ranking_scores)
        if best_answer is None:
            return "Chịu"
        bm25_answer = preprocess(str(best_answer).lower(), max_length=128, remove_puncts=True)

        if not check_number(bm25_answer):
            bm25_question = preprocess(str(question).lower(), max_length=128, remove_puncts=True)
            bm25_question_answer = bm25_question + " " + bm25_answer
            candidates, scores = self.bm25_model_stage2_title.get_topk_stage2(bm25_answer, raw_answer=best_answer)
            titles = [df_wiki.title.values[i] for i in candidates]
            texts = [df_wiki.text.values[i] for i in candidates]
            ranking_preds = self.pairwise_model_stage2.stage2_ranking(question, best_answer, titles, texts)
            if ranking_preds.max() >= 0.1:
                final_answer = titles[ranking_preds.argmax()]
            else:
                candidates, scores = self.bm25_model_stage2_full.get_topk_stage2(bm25_question_answer)
                titles = [df_wiki.title.values[i] for i in candidates] + titles
                texts = [df_wiki.text.values[i] for i in candidates] + texts
                ranking_preds = np.concatenate(
                    [self.pairwise_model_stage2.stage2_ranking(question, best_answer, titles, texts), ranking_preds])
            final_answer = "wiki/"+titles[ranking_preds.argmax()].replace(" ","_")
        else:
            final_answer = bm25_answer.lower()
        return final_answer

    
class InferencePipeline:
    def __init__(self):
        self.endpoint_handler = EndpointHandler()  # Instantiate the EndpointHandler class
    
    def __call__(self, question):
        answer = self.endpoint_handler.get_answer_e2e(question)  # Call the get_answer_e2e method from EndpointHandler
        return {"answer": answer}  # Return the answer as a dictionary


inference_pipeline = InferencePipeline()
pipeline = pipeline("qa-model", model=inference_pipeline, tokenizer=None)