app.py

from flask import Flask, render_template, request
import numpy as np
import pandas as pd
import nltk.data
import pickle
from sentence_transformers import SentenceTransformer
from rank_bm25 import BM25L

def rank(query):
    PM_Articles = pd.read_csv("data/PM classified data/Final_PM.csv", encoding='latin1')

    embedder = SentenceTransformer('bert-base-nli-mean-tokens')

    corpus = []
    tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')

    #Title = PM_Articles['Title']
    #Abstract = PM_Articles['Abstract']
    NCT_ID = PM_Articles['NCT ID']

    gene = "BRAF (V600E)"


    for article in PM_Articles.Abstract.apply(lambda row: row.lower()):
        corpus.extend(tokenizer.tokenize(article))



    embedding_file = "data/models/PM_Articles.emb"
    with open(embedding_file, mode='rb') as emb_f:
        corpus_embeddings = pickle.load(emb_f)

    bm25 = BM25L(corpus)
    tokenized_gene = gene.split(" ")
    BM25_Score = bm25.get_scores(tokenized_gene) * 2
    query_embeddings = embedder.encode(query)


    topk=10
    score_corpus = np.sum(query_embeddings * corpus_embeddings, axis=1) / np.linalg.norm(corpus_embeddings, axis=1)
    results=[]
    score_list=[]
    topk_idx = np.argsort(score_corpus)[::-1][:topk]

    i = 0
    for idx in topk_idx:
        i = i + 1
        score = score_corpus[idx] + BM25_Score[idx]
        results.append('https://clinicaltrials.gov/ct2/show/' + NCT_ID[idx] + '?term=' + NCT_ID[idx] + '&draw=2&rank=1 ')
        score_list.append(score)
    return results ,score_list

app = Flask(__name__)

@app.route('/')
def home():
    return render_template('search.html')

@app.route('/search/results', methods=['GET', 'POST'])
def search_request():
    search_term = request.form["input"]
    articlelink,score=rank(search_term)
    return render_template('results.html', res=articlelink ,score=score)

if __name__ == '__main__':
    app.run(debug=True)