Update app.py

app.py

@@ -1,5 +1,83 @@
 import streamlit as st
-from src.models.predict_model import *
+# from src.models.predict_model import *
+
+from src.models.pairwise_model import *
+from src.features.text_utils import *
+import regex as re
+from src.models.bm25_utils import BM25Gensim
+from src.models.qa_model import *
+from tqdm.auto import tqdm
+tqdm.pandas()
+from datasets import load_dataset
+
+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", ["models/qa_model_robust.bin"], entity_dict)
+pairwise_model_stage1 = PairwiseModel("nguyenvulebinh/vi-mrc-base")#.half()
+pairwise_model_stage1.load_state_dict(torch.load("models/pairwise_v2.bin", map_location=torch.device('cpu')))
+pairwise_model_stage1.eval()
+
+pairwise_model_stage2 = PairwiseModel("nguyenvulebinh/vi-mrc-base")#.half()
+pairwise_model_stage2.load_state_dict(torch.load("models/pairwise_stage2_seed0.bin", map_location=torch.device('cpu')))
+
+bm25_model_stage1 = BM25Gensim("models/bm25_stage1/", entity_dict, title2idx)
+bm25_model_stage2_full = BM25Gensim("models/bm25_stage2/full_text/", entity_dict, title2idx)
+bm25_model_stage2_title = BM25Gensim("models/bm25_stage2/title/", entity_dict, title2idx)
+
+def get_answer_e2e(question):
+    #Bm25 retrieval for top200 candidates
+    query = preprocess(question).lower()
+    top_n, bm25_scores = 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]
+    
+    #Reranking with pairwise model for top10
+    question = preprocess(question)
+    ranking_preds = pairwise_model_stage1.stage1_ranking(question, texts)
+    ranking_scores = ranking_preds * bm25_scores
+    
+    #Question answering
+    best_idxs = np.argsort(ranking_scores)[-10:]
+    ranking_scores = np.array(ranking_scores)[best_idxs]
+    texts = np.array(texts)[best_idxs]
+    best_answer = 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)
+    
+    #Entity mapping
+    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 = 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 = 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 = 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(
+                [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
+
 
 with st.sidebar:
     st.write("# 🤖 Language Models")