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from typing import List, Tuple | |
import pandas as pd | |
from sentence_transformers import SentenceTransformer, util | |
import streamlit as st | |
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode | |
st.set_page_config(layout='wide') | |
def load_model(): | |
return SentenceTransformer('all-MiniLM-L6-v2') | |
def semantic_search(model, sentence, corpus_embeddings): | |
query_embeddings = model.encode(sentence, | |
convert_to_tensor=True, | |
normalize_embeddings=True) | |
hits = util.semantic_search(query_embeddings, | |
corpus_embeddings, | |
top_k=len(corpus_embeddings), | |
score_function=util.dot_score) | |
return pd.DataFrame(hits[0]) | |
def top_k_similarity(model, df, query, corpus_embeddings): | |
hits = semantic_search(model, [query], corpus_embeddings) | |
result = pd.merge(df, hits, left_on='ID', right_on='corpus_id') | |
result.sort_values(by='score', ascending=False, inplace=True) | |
return result | |
def create_embedding(model: SentenceTransformer, data: pd.DataFrame, key: str) -> Tuple[list, list]: | |
"""Create vector embeddings from the dataset""" | |
corpus_sentences = data[key].astype(str).tolist() | |
corpus_embeddings = model.encode(sentences=corpus_sentences, | |
show_progress_bar=True, | |
convert_to_tensor=True, | |
normalize_embeddings=True) | |
return corpus_embeddings | |
def load_dataset(columns: List) -> pd.DataFrame: | |
"""Load real-time dataset from google sheets""" | |
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}' | |
data = pd.read_csv(url) | |
data = data.iloc[: , :7] | |
data.columns = columns | |
data.insert(0, 'ID', range(len(data))) | |
return data | |
def show_aggrid_table(result: pd.DataFrame): | |
gb = GridOptionsBuilder.from_dataframe(result) | |
gb.configure_pagination(paginationAutoPageSize=True) | |
gb.configure_side_bar() | |
gb.configure_default_column(min_column_width=200) | |
gb.configure_selection('multiple', use_checkbox=True, groupSelectsChildren="Group checkbox select children") | |
gb.configure_column(field="LinkedIn Profile", | |
headerName="LinkedIn Profile", | |
cellRenderer=JsCode('''function(params) {return `<a href=${params.value} target="_blank">${params.value}</a>`}''')) | |
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, | |
) | |
def main(): | |
st.title('Job Posting Similarity') | |
st.write('This app will help you find similar job titles real-time from ecommurz google sheets.') | |
columns = ['Timestamp', 'Full Name', 'Company', 'Previous Role', | |
'Experience', 'Last Day', 'LinkedIn Profile'] | |
data = load_dataset(columns) | |
model = load_model() | |
corpus_embeddings = create_embedding(model, data, 'Previous Role') | |
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 = top_k_similarity(model, data, job_title, corpus_embeddings) | |
result = result[columns] | |
st.download_button( | |
"Download Table", | |
result.to_csv().encode('utf-8'), | |
"result.csv", | |
"text/csv", | |
key='download-csv' | |
) | |
show_aggrid_table(result) | |
if __name__ == '__main__': | |
main() | |