app.py

from transformers import AutoModel, AutoTokenizer
import faiss
import numpy as np
import pandas as pd
import streamlit as st
import torch

import math
import os 

os.environ['KMP_DUPLICATE_LIB_OK']='True'


@st.cache(allow_output_mutation=True)
def load_model_and_tokenizer():
    tokenizer = AutoTokenizer.from_pretrained("kaisugi/scitoricsbert")
    model = AutoModel.from_pretrained("kaisugi/scitoricsbert")
    model.eval()
    
    return model, tokenizer


@st.cache(allow_output_mutation=True)
def load_sentence_data():
    sentence_df = pd.read_csv("sentence_data_858k.csv.gz")
    
    return sentence_df


@st.cache(allow_output_mutation=True)
def load_sentence_embeddings_and_index():
    npz_comp = np.load("sentence_embeddings_858k.npz")
    sentence_embeddings = npz_comp["arr_0"]

    faiss.normalize_L2(sentence_embeddings)
    D = 768
    N = 857610
    Xt = sentence_embeddings[:100000]
    X = sentence_embeddings

    # Param of PQ
    M = 16  # The number of sub-vector. Typically this is 8, 16, 32, etc.
    nbits = 8 # bits per sub-vector. This is typically 8, so that each sub-vec is encoded by 1 byte
    # Param of IVF
    nlist = int(math.sqrt(N))  # The number of cells (space partition). Typical value is sqrt(N)
    # Param of HNSW
    hnsw_m = 32  # The number of neighbors for HNSW. This is typically 32

    # Setup
    quantizer = faiss.IndexHNSWFlat(D, hnsw_m)
    index = faiss.IndexIVFPQ(quantizer, D, nlist, M, nbits)

    # Train
    index.train(Xt)

    # Add
    index.add(X)

    # Search
    index.nprobe = 8  # Runtime param. The number of cells that are visited for search.

    return sentence_embeddings, index


@st.cache(allow_output_mutation=True)
def get_retrieval_results(index, input_text, top_k, model, tokenizer, sentence_df, exclude_word_list):
    with torch.no_grad():
        inputs = tokenizer.encode_plus(
            input_text,
            padding=True,
            truncation=True,
            max_length=512,
            return_tensors='pt'
        )
        outputs = model(**inputs)
        query_embeddings = outputs.last_hidden_state[:, 0, :][0]
        query_embeddings = query_embeddings.detach().cpu().numpy()
        query_embeddings = query_embeddings / np.linalg.norm(query_embeddings, ord=2)

    _, ids = index.search(x=np.array([query_embeddings]), k=top_k)
    retrieved_sentences = []
    retrieved_paper_id = []

    for id in ids[0]:
        retrieved_sentences.append(sentence_df.loc[id, "sentence"])
        retrieved_paper_id.append(f"https://aclanthology.org/{sentence_df.loc[id, 'file_id']}")

    all_df = pd.DataFrame({"sentence": retrieved_sentences, "source link": retrieved_paper_id})

    if len(exclude_word_list) == 0:
        return all_df
    else:
        exclude_word_list_regex = '|'.join(exclude_word_list)
        return all_df[~all_df["sentence"].str.contains(exclude_word_list_regex)]


if __name__ == "__main__":
    model, tokenizer = load_model_and_tokenizer()
    sentence_df = load_sentence_data()
    sentence_embeddings, index = load_sentence_embeddings_and_index()


    st.markdown("## AI-based Paraphrasing for Academic Writing")

    input_text = st.text_area("text input", "We saw difference in the results between A and B.", placeholder="Write something here...")
    top_k = st.number_input('top_k (upperbound)', min_value=1, value=200, step=1)
    input_words = st.text_input("exclude words (comma separated)", "see, saw")

    if st.button('search'):
        exclude_word_list = [s.strip() for s in input_words.split(",") if s.strip() != ""]
        df = get_retrieval_results(index, input_text, top_k, model, tokenizer, sentence_df, exclude_word_list)
        st.table(df)