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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(): | |
| """Load pretrained model from SentenceTransformer""" | |
| return SentenceTransformer('minilm_sbert') | |
| def semantic_search(model, sentence, corpus_embeddings): | |
| """Perform semantic search on the corpus""" | |
| 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 get_similarity_score(model, data, query, corpus_embeddings): | |
| """Get similarity score for each data point and sort by similarity score and day""" | |
| hits = semantic_search(model, [query], corpus_embeddings) | |
| result = pd.merge(data, hits, left_on='ID', right_on='corpus_id') | |
| result['Last Day'] = pd.to_datetime(result['Last Day'], format='%d/%m/%Y') | |
| result.sort_values(by=['score', 'Last Day'], ascending=[False, True], 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): | |
| """Show interactive table from similarity result""" | |
| 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>`}''')) | |
| grid_options = gb.build() | |
| grid_response = AgGrid( | |
| dataframe=result, | |
| gridOptions=grid_options, | |
| 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(): | |
| """Main Function""" | |
| st.title('@ecommurz Talent Search Engine') | |
| st.write('This app lets you search and sort talent by job title or relevant job descriptions from ecommurz talent list in real-time.') | |
| 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 = get_similarity_score(model, data, job_title, corpus_embeddings) | |
| result = result[columns] | |
| show_aggrid_table(result) | |
| if __name__ == '__main__': | |
| main() | |