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import streamlit as st
import pandas as pd
import numpy as np
import re
import json
import requests
from io import StringIO
from perplexity import Perplexity
import codecs
st.set_page_config(layout="wide")
def dataread(kk, pasteduse,perplex_use):
if pasteduse != '':
data = pd.read_csv(StringIO(pasteduse),sep='\t',header=0)
data['Gene']=data['Gene_name']
data['Protein Change']=data['Protein Change'].apply(lambda x: str(x).replace('p.',''))
st.write('The data you input is the following:')
st.write(data)
all_onc=[]
all_perplex=[]
all_query=[]
querygeneprotein=[]
for index,row in data.iterrows():
if str(row['Gene']) != 'nan' and str(row['Protein Change']) != 'nan':
if str(row['Protein Change']) != 'nan':
d1=requests.get("https://www.oncokb.org/api/v1/annotate/mutations/byProteinChange?hugoSymbol="+row['Gene']+"&alteration="+row['Protein Change']+"&tumorType="+kk, headers={'Accept': 'application/json',"Authorization": 'Bearer 64f4aa64-2509-4500-994b-1f2a38422d44'})
if perplex_use:
query="what drugs are used to treat "+ row['Gene']+" "+row['Protein Change']+" in "+kk+"?"
perplexity = Perplexity()
answer=perplexity.search(query)
all_perplex.append(answer)
all_query.append(query)
all_onc.append(d1.content)
querygeneprotein.append(row['Gene']+"&alteration="+row['Protein Change'])
#if perplex_use:
# perplexity.close()
return all_onc,all_perplex,all_query,querygeneprotein
with st.form(key='parameters'):
texttomatch=st.text_input('text to match',value='')
pasteduse=st.text_area('paste text to search',value='')
perplex_use=st.checkbox('Use Perplexity',value=False)
abbrev_perplex=st.checkbox('Abbreviate Perplexity',value=True)
submit_button = st.form_submit_button(label='Submit')
if submit_button:
if pasteduse != '':
kk,perplexity, query,querygeneprotein=dataread(texttomatch,pasteduse,perplex_use)
dictionary_of_json_fda={}
dictionary_of_json_text={}
dictionary_of_json_text_perplexity={}
dictionary_of_json_query={}
dictionary_of_drugs={}
for i in np.arange(0,len(kk)):
output = codecs.decode(kk[i])
dictionary_of_json_fda[querygeneprotein[i]]=json.loads(output).get('highestFdaLevel')
#dictionary_of_drugs[querygeneprotein[i]]=json.loads(output).get('treatments').get('drugs').get('DrugName')
dictionary_of_json_text[querygeneprotein[i]]=json.loads(output)
dictionary_of_json_text_perplexity[querygeneprotein[i]]=perplexity[i]
dictionary_of_json_query[querygeneprotein[i]]=query[i]
ord_dict=sorted(dictionary_of_json_fda.items(), key=lambda item: str(item[1]))
tt=st.tabs(pd.DataFrame(ord_dict).apply(lambda n: str(n[0])+' '+str(n[1]),axis=1).to_list())
counter=0
for i in ord_dict:
with tt[counter]:
st.write('The query was '+i[0])
st.write('The results were:')
if dictionary_of_json_text[i[0]]['highestFdaLevel'] != '':
st.write('Drugs in this result')
drugnames=[]
for d in dictionary_of_json_text[i[0]]['treatments']:
for dd in d['drugs']:
drugnames.append(dd['drugName'])
for m in (set(drugnames)):
st.write(m)
st.write(str(len(set(drugnames)))+' drugs in this result')
if perplex_use:
st.write('The perplexity query was '+dictionary_of_json_query[i[0]])
for jj in dictionary_of_json_text_perplexity[i[0]]:
forout=jj
st.write('answer from perplexity')
st.write(forout["answer"])
for m in (set(drugnames)):
if(re.findall(m,forout["answer"])) !=[]:
st.write('YES BOTH '+m+' is in the answer of perplexity')
else:
st.write('NO JUST ONCOKB '+m+' is not in the answer of perplexity')
st.write('all oncokb results')
st.json(dictionary_of_json_text[i[0]])
if perplex_use and abbrev_perplex ==False:
st.write('All results from perplexity:')
st.write(forout)
#breakpoint()
counter=counter+1
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