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import pandas as pd
from sentence_transformers import SentenceTransformer, util
import streamlit as st
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
from cpu_unpickler import cpu_unpickler
st.set_page_config(layout='wide')
@st.cache(allow_output_mutation=True)
def load_model():
return SentenceTransformer('all-MiniLM-L6-v2')
def find_top_similar(sentence, corpus_sentences, corpus_embeddings):
# preprocess query
model = load_model()
query_embeddings = model.encode(sentence, convert_to_tensor=True) # encode to tensor
# query_embeddings = query_embeddings.to('cuda') # put into gpu
query_embeddings = util.normalize_embeddings(query_embeddings) # normalize
# find the closest 5 sentences of the corpus for each query sentence based on cosine similarity
hits = util.semantic_search(query_embeddings,
corpus_embeddings,
top_k=len(corpus_embeddings),
score_function=util.dot_score)
hits = hits[0] # get the hits for the first query
# Create dataframe to store top searches
records = []
for hit in hits[0:len(corpus_embeddings)]:
records.append(corpus_sentences[hit['corpus_id']])
return records
def top_k_similarity(df, query, corpus_sentences, corpus_embeddings):
hits = find_top_similar([query], corpus_sentences, corpus_embeddings)
res = pd.DataFrame()
for h in hits:
s = df[df['Last job role'] == h]
res = pd.concat([res, s])
return res
def get_result(df, query, corpus_sentences, corpus_embeddings):
result = top_k_similarity(df, query, corpus_sentences, corpus_embeddings)
result.drop_duplicates(inplace=True)
return result
@st.cache(allow_output_mutation=True)
def load_embedding():
"""Loads the embeddings from the pickle file"""
with open('corpus_embeddings.pkl', 'rb') as file:
cache_data = cpu_unpickler(file).load()
corpus_sentences = cache_data['sentences']
corpus_embeddings = cache_data['embeddings']
return corpus_sentences, corpus_embeddings
def main():
# get dataset
sheet_id = '1KeuPPVw9gueNmMrQXk1uGFlY9H1vvhErMLiX_ZVRv_Y'
sheet_name = 'Form Response 3'.replace(' ', '%20')
url = f'https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}'
df = pd.read_csv(url)
df = df.iloc[: , :7]
# get embeddings
corpus_sentences, corpus_embeddings = load_embedding()
# streamlit form
st.title('Job Posting Similarity')
job_title = st.text_input('Insert the job title below:', '')
submitted = st.button('Submit')
if submitted:
st.info(f'Showing results for { job_title}')
result = get_result(df, job_title, corpus_sentences, corpus_embeddings)
result.reset_index(drop=True, inplace=True)
result.index += 1
st.download_button(
"Press to Download",
result.to_csv().encode('utf-8'),
"result.csv",
"text/csv",
key='download-csv'
)
gb = GridOptionsBuilder.from_dataframe(result)
gb.configure_pagination(paginationAutoPageSize=True) # Add pagination
# gb.configure_side_bar() #Add a sidebar
# gb.configure_selection('multiple', use_checkbox=True, groupSelectsChildren="Group checkbox select children") #Enable multi-row selection
gb.configure_column("LinkedIn Link",
headerName="LinkedIn Link",
# cellRenderer=JsCode('''function(params) {return '<a href=params.value + '" target="_blank">'+ params.value+'</a>'}'''),
cellRenderer=JsCode('''function(params) {return `<a href=${params.value} target="_blank">${params.value}</a>`}'''),
width=300)
gridOptions = gb.build()
grid_response = AgGrid(
dataframe=result,
gridOptions=gridOptions,
height=1100,
fit_columns_on_grid_load=True,
data_return_mode='AS_INPUT',
update_mode='VALUE_CHANGED',
theme='light',
enable_enterprise_modules=True,
allow_unsafe_jscode=True,
)
if __name__ == '__main__':
main()
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