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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| from transformers import AlbertTokenizer, AlbertModel | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| # base is smaller, vs large | |
| model_size='base' | |
| tokenizer = AlbertTokenizer.from_pretrained('albert-' + model_size + '-v2') | |
| model = AlbertModel.from_pretrained('albert-' + model_size + '-v2') | |
| def get_embedding(input_text): | |
| encoded_input = tokenizer(input_text, return_tensors='pt') | |
| input_ids = encoded_input.input_ids | |
| #input_num_tokens = input_ids.shape[1] | |
| #print( "Number of input tokens: " + str(input_num_tokens)) | |
| #print("Length of input: " + str(len(input_text))) | |
| list_of_tokens = tokenizer.convert_ids_to_tokens(input_ids.view(-1).tolist()) | |
| print( "Tokens : " + ' '.join(list_of_tokens)) | |
| with torch.no_grad(): | |
| outputs = model(**encoded_input) | |
| last_hidden_states = outputs[0] | |
| sentence_embedding = torch.mean(last_hidden_states[0], dim=0) | |
| #sentence_embedding = output.last_hidden_state[0][0] | |
| return sentence_embedding.tolist() | |
| st. set_page_config(layout="wide") | |
| st.title('Upload the Address Dataset') | |
| st.markdown('Upload an Excel file to view the data in a table.') | |
| uploaded_file = st.file_uploader('Choose a file', type='xlsx') | |
| if uploaded_file is not None: | |
| data_caqh = pd.read_excel(uploaded_file, sheet_name='CAQH', dtype=str) | |
| data_ndb = pd.read_excel(uploaded_file, sheet_name='NDB', dtype=str) | |
| # Data cleaning CAQH | |
| data_caqh['postalcode'] = data_caqh['postalcode'].astype(str).apply(lambda x: x[:5] + '-' + x[5:] if len(x) > 5 and not '-' in x else x) | |
| data_caqh['full-addr'] = data_caqh['address1'].astype(str) + ', ' \ | |
| + np.where(data_caqh['address2'].isnull(), '' , data_caqh['address2'].astype(str)) \ | |
| + data_caqh['city'].astype(str) + ', '\ | |
| + data_caqh['state'].astype(str) + ', ' \ | |
| + data_caqh['postalcode'].astype(str) | |
| # Data cleaning NDB | |
| data_ndb['zip_pls_4_cd'] = data_ndb['zip_pls_4_cd'].astype(str).apply(lambda x: x if (x[-1] != '0' and x[-1] != '1') else '') | |
| data_ndb['zip_cd_zip_pls_4_cd'] = data_ndb['zip_cd'].astype(str) +\ | |
| np.where( data_ndb['zip_pls_4_cd'] == '', '', '-' \ | |
| + data_ndb['zip_pls_4_cd'].astype(str)) | |
| data_ndb['full-addr'] = data_ndb['adr_ln_1_txt'].astype(str).str.strip() + ', ' \ | |
| + data_ndb['cty_nm'].astype(str) + ', ' \ | |
| + data_ndb['st_cd'].astype(str) + ', ' + data_ndb['zip_cd_zip_pls_4_cd'] | |
| # App | |
| data_caqh['embedding'] = data_caqh['full-addr'].apply(get_embedding) | |
| data_ndb['embedding'] = data_ndb['full-addr'].apply(get_embedding) | |
| data_caqh['matched-addr'] = '' | |
| for i, row in data_caqh.iterrows(): | |
| max_similarity = 0 | |
| matched_row = None | |
| for j, ndb_row in data_ndb.iterrows(): | |
| sim = cosine_similarity([row['embedding']], [ndb_row['embedding']]) | |
| if sim > max_similarity: | |
| max_similarity = sim | |
| matched_row = ndb_row | |
| if max_similarity >= 0.98: | |
| data_caqh.at[i, 'matched-addr'] = matched_row['full-addr'] | |
| data_caqh.at[i, 'similarity-score'] = max_similarity | |
| else: | |
| data_caqh.at[i, 'matched-addr'] = 'No Matches' | |
| # Drop columns not needed for display | |
| data_caqh.drop(columns=['embedding'], inplace=True) | |
| data_ndb.drop(columns=['embedding'], inplace=True) | |
| st.header('CAQH addresses and matches') | |
| st.dataframe(data_caqh, use_container_width=True) | |
| st.header('NDB data') | |
| st.dataframe(data_ndb, use_container_width=True) | |
| # calculate the embedding of each item. | |
| #st.dataframe(data_caqh) | |
| # Do some matching | |
| #data_caqh.loc[data_caqh['full-addr'] == '1000 Vale Terrace, Vista, CA, 92084', 'matched-addr'] = '456 Main St' | |
| #time.sleep(10) | |
| #st.dataframe(data_caqh) | |