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deploy.py
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import json
from json.tool import main
import jsonlines
import numpy as np
import math
import os
import multiprocessing
import argparse
import statistics
import codecs
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModel
import operator
from datetime import datetime
import streamlit as st
import pandas as pd
import numpy as np
st.set_page_config(
page_title="sciatica",
page_icon="🧊",
layout="wide",
initial_sidebar_state='collapsed'
)
def dot_product2(v1, v2):
return sum(map(operator.mul, v1, v2))
def cosine_sim(v1, v2):
prod = dot_product2(v1, v2)
len1 = math.sqrt(dot_product2(v1, v1))
len2 = math.sqrt(dot_product2(v2, v2))
return prod / (len1 * len2)
model_list = [['alberta', 2.5], ['all_mpnet_base_v2', 5], ['bert_nli', 0.5], ['bert_pp', 0.5], ['distilbert_nli', 5], ['allenai_specter', 3.5]]
@st.cache_resource
def load_model():
model_sent_bert_nli = SentenceTransformer('nli-roberta-base-v2')
model_sent_bert_pp = SentenceTransformer('paraphrase-TinyBERT-L6-v2')
model_all_mpnet_base_v2 = SentenceTransformer('all-mpnet-base-v2')
model_sent_distbert_nli = SentenceTransformer('all-distilroberta-v1')
model_alberta = SentenceTransformer('paraphrase-albert-small-v2')
specter_tokenize = AutoTokenizer.from_pretrained('allenai/specter')
specter_model = AutoModel.from_pretrained('allenai/specter')
return model_sent_bert_nli, model_sent_bert_pp, model_all_mpnet_base_v2, model_sent_distbert_nli, model_alberta, specter_tokenize, specter_model
model_sent_bert_nli, model_sent_bert_pp, model_all_mpnet_base_v2, model_sent_distbert_nli, model_alberta, specter_tokenize, specter_model = load_model()
def get_bert_nli_embedding(sentence):
return model_sent_bert_nli.encode(sentence)
def get_bert_pp_embedding(sentence):
return model_sent_bert_pp.encode(sentence)
def get_all_mpnet_base_v2_embedding(sentence):
return model_all_mpnet_base_v2.encode(sentence)
def get_distilbert_base_v2_embedding(sentence):
return model_sent_distbert_nli.encode(sentence)
def get_alberta_embedding(sentence):
return model_alberta.encode(sentence)
def get_allenai_specter_embedding(sentence):
inputs = specter_tokenize(sentence, padding=True, truncation=True, return_tensors="pt", max_length=5000)
return specter_model(**inputs).last_hidden_state[:, 0, :]
@st.cache_resource
def load_data():
data_all = dict()
for model in model_list:
data_all[model[0]] = dict()
data_all[model[0]]['all'] = json.load(open(f'./Results/{model[0]}/all.json'))
data_all[model[0]]['background'] = json.load(open(f'./Results/{model[0]}/background.json'))
data_all[model[0]]['method'] = json.load(open(f'./Results/{model[0]}/method.json'))
data_all[model[0]]['result'] = json.load(open(f'./Results/{model[0]}/result.json'))
return data_all
data_all = load_data()
# for normal execution
@st.cache_data
def custom_docs_normal(facet, ATK, user_query):
print("computing")
print(type(model_sent_bert_nli))
ens = dict()
for model in model_list:
try:
method = model[0]
weight = model[1]
query_embedding = []
if method == 'bert_nli':
query_embedding = np.array(get_bert_nli_embedding(user_query)).tolist()
elif method == 'bert_pp':
query_embedding = np.array(get_bert_pp_embedding(user_query)).tolist()
elif method == 'all_mpnet_base_v2':
query_embedding = np.array(get_all_mpnet_base_v2_embedding(user_query)).tolist()
elif method == 'distilbert_nli':
query_embedding = np.array(get_distilbert_base_v2_embedding(user_query)).tolist()
elif method == 'alberta':
query_embedding = np.array(get_alberta_embedding(user_query)).tolist()
else:
query_embedding = get_allenai_specter_embedding(" ".join(user_query)).detach().numpy().tolist()[0]
data = data_all[model[0]][facet]
for id in data:
if id not in ens:
ens[id] = 0
ens[id] += cosine_sim(query_embedding, data[id]) * weight
except:
pass
print("model done")
sorted_results = sorted(ens.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
print("done")
print(sorted_results[:ATK])
return sorted_results[:ATK]
class get_doc:
def __init__(self, paper_id, metadata, title, abstract, pred_labels_truncated, pred_labels):
self.paper_id = paper_id
self.metadata = metadata
self.title = title
self.abstract = abstract
self.pred_labels_truncated = pred_labels_truncated
self.pred_labels = pred_labels
docs = {}
with jsonlines.open('./data/abstracts-csfcube-preds.jsonl') as doc:
for section in doc:
docs[section['paper_id']] = get_doc(section['paper_id'], section['metadata'], section['title'], section['abstract'], section['pred_labels_truncated'], section['pred_labels'])
mapping = {
'background':['background_label', 'objective_label'],
'method':['method_label'],
'result':['result_label']
}
def get_document(result):
docu = {}
docu['did'] = result[0]
docu['title'] = docs[result[0]].title,
docu['score'] = result[1]
docu['authors'] = []
for author in docs[result[0]].metadata['authors']:
name = []
name.append(author['first'])
name = name + author['middle']
name.append(author['last'])
auth = " ".join(name)
docu['authors'].append(auth)
docu['authors'] = " | ".join(docu['authors'])
docu['year'] = docs[result[0]].metadata['year']
docu['doi'] = docs[result[0]].metadata['doi']
docu['venue'] = docs[result[0]].metadata['venue']
abstract = []
if custom_facet == 'all':
for j in range(len(docs[result[0]].abstract)):
abstract.append(docs[result[0]].abstract[j])
# docu['abstract'] = " ".join(docs[result[0]].abstract)
else:
for j in range(len(docs[result[0]].abstract)):
for l in mapping[custom_facet]:
if docs[result[0]].pred_labels[j] == l:
abstract.append(docs[result[0]].abstract[j])
docu['abstract'] = " ".join(abstract)
return docu
def make_result(docu):
exp = st.expander(docu['title'][0] + " [ Ensembled score: " + str(docu['score']) + " ]")
with exp:
st.write("Title: ", docu['title'][0])
st.write("Authors: ", docu['authors'])
st.write("Abstract: ", docu['abstract'])
if docu['doi'] is not None:
st.write("URL (doi):", "https://doi.org/" + (docu['doi']))
if docu['year'] is not None:
st.write("Year: ", docu['year'])
if docu['venue'] is not None:
st.write("Venue: ", docu['venue'])
# st.title('SCIATICA')
st.markdown("<h1 style='text-align: center; color: cyan;'>SCIATICA</h1>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: center; color: white;'>Research For Research Papers</h3>", unsafe_allow_html=True)
qdf = pd.read_csv('./queries-release.csv', sep=',')
facets = ['all', 'background', 'result', 'method']
sb = st.sidebar
with sb:
st.subheader("Group 4 \n(CS60092: Information Retrieval, 2023 Spring)")
st.write("Ashwani Kumar Kamal - 20CS10011")
st.write("Hardik Pravin Soni - 20CS30023")
st.write("Shiladitya De - 20CS30061")
st.write("Sourabh Soumyakanta Das - 20CS30051")
custom_facet = st.selectbox("Select Facet", facets)
custom_query = st.text_input("Enter Query")
col1, col2 = st.columns(2)
with col1:
bsearch = st.button("search")
with col2:
no_results = st.slider("number of results", 1, 10, 5)
doc_result = st.empty()
if bsearch:
message = st.empty()
# message.write("Searching For Results...")
start = datetime.now()
result = custom_docs_normal(custom_facet, no_results, custom_query)
end = datetime.now()
diff = (end - start).total_seconds()
st.write("Fetched Results in", diff, "seconds")
for res in result:
docu = get_document(res)
make_result(docu)
# make_result(res[0], res[1], docs[res[0]].title, docs[res[0]].author, docs[res[0]].abstract, docs[res[0]].url)