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import streamlit as st
import torch
import pickle
import time
import pandas as pd
from iteration_utilities import unique_everseen
from sentence_transformers import util
from loader import bi_encoder, cross_encoder, df, job_corpus_ecoded, job_corpus
def jobsearch(query,df, top_k=100):
#print("Answer by NinjaBot : ")
ans = []
question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
hits = util.semantic_search(question_embedding, job_corpus_ecoded, top_k=top_k)
hits = hits[0]
cross_inp = [[query, job_corpus[hit['corpus_id']]] for hit in hits]
cross_scores = cross_encoder.predict(cross_inp)
for idx in range(len(cross_scores)):
hits[idx]['cross-score'] = cross_scores[idx]
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
#indexes = []
search_result = []
for idx, hit in enumerate(hits[0:10]):
obj = {}
ans.append(job_corpus[hit['corpus_id']])
#indexes.append(job_corpus.index(job_corpus[hit['corpus_id']]))
obj['title'] = df.at[job_corpus.index(job_corpus[hit['corpus_id']]),'title']
obj['link'] = df.at[job_corpus.index(job_corpus[hit['corpus_id']]),'url']
search_result.append(obj)
final_search_result = list(unique_everseen(search_result))
return final_search_result
#return df.at[indexes[0],'title'],df.at[indexes[1],'title'],df.at[indexes[2],'title'],df.at[indexes[3],'title'],df.at[indexes[4],'title']
#return ans[0],ans[1],ans[2],ans[3],ans[4]
def main():
if 'submitted' not in st.session_state:
st.session_state.submitted = False
def callback():
st.session_state.submitted = True
st.title('Job Search Engine 💼')
st.text("")
st.text("")
query = st.text_input('Enter your job query here ! ')
if (st.button("Search", on_click=callback) and query) :
with st.spinner('Fetching the best jobs for you!...'):
#time.sleep(10)
result = jobsearch(query, df)
#result = jobsearch(query, df)
st.success('NinjaBot : Here are a few suggestions')
#st.write(f"This is the query : {query}")
st.write(result)
main()
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