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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') | |
| 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 | |
| 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() | |