import math import streamlit as st import pandas as pd import numpy as np #import torch #from transformers import AlbertTokenizer, AlbertModel from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity from io import BytesIO # base is smaller, vs large #model_size='base' #tokenizer = AlbertTokenizer.from_pretrained('albert-' + model_size + '-v2') #model = AlbertModel.from_pretrained('albert-' + model_size + '-v2') # For baseline 'sentence-transformers/paraphrase-albert-base-v2' model_name = 'output/training_OnlineConstrativeLoss-2023-03-14_01-24-44' model_name = 'output/training_OnlineConstrativeLoss-2023-03-17_23-15-52' model_name = 'output/training_OnlineConstrativeLoss-2023-03-17_23-50-15' model_name = 'output/training_OnlineConstrativeLoss-2023-03-25_23-48-35' # had 89% accur on eval set model_name = 'sentence-transformers/paraphrase-albert-base-v2' similarity_threshold = 0.9 # for regular burt 0.98 model_sbert = SentenceTransformer(model_name) def get_sbert_embedding(input_text): embedding = model_sbert.encode(input_text) return embedding.tolist() #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 # if you need to format with 00000-0000 # lambda x: x[:5] + '-' + x[5:] if len(x) > 5 and not '-' in x else x data_caqh['postalcode'] = data_caqh['postalcode'].astype(str).apply(lambda x: x[:5]) 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_caqh['full-addr'] = data_caqh['full-addr'].str.upper() st.write(f"CAQH before duplicate removal {len(data_caqh)}") data_caqh.drop_duplicates(subset='full-addr',inplace=True) data_caqh = data_caqh.reset_index(drop=True) # reset the index. st.write(f"CAQH after duplicate removal {len(data_caqh)}") # 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).str.strip() + ', ' \ + data_ndb['st_cd'].astype(str) + ' ' + data_ndb['zip_cd'].astype(str) data_ndb['full-addr'] = data_ndb['full-addr'].str.upper() # Calculate similarity For CAQH num_items = len(data_caqh) progress_bar = st.progress(0) total_steps = 100 step_size = math.ceil(num_items / total_steps) data_caqh['embedding'] = 0 embedding_col_index = data_caqh.columns.get_loc('embedding') full_addr_col_index = data_caqh.columns.get_loc('full-addr') for i in range(total_steps): # Update progress bar progress = (i + 1) / total_steps # Process a batch of rows start = i * step_size end = start + step_size stop_iter = False if end >= num_items: end = num_items stop_iter = True data_caqh.iloc[start:end, embedding_col_index] = data_caqh.iloc[start:end, full_addr_col_index].apply(get_sbert_embedding) progress_bar.progress(value=progress, text=f"CAQH embeddings: {(i + 1) * step_size} processed out of {num_items}") if stop_iter: break st.write(f"Embeddings for CAQH calculated") # Calculate similarity For NDB num_items = len(data_ndb) progress_bar = st.progress(0) total_steps = 100 step_size = math.ceil(num_items / total_steps) data_ndb['embedding'] = 0 embedding_col_index = data_ndb.columns.get_loc('embedding') full_addr_col_index = data_ndb.columns.get_loc('full-addr') for i in range(total_steps): # Update progress bar progress = (i + 1) / total_steps # Process a batch of rows start = i * step_size end = start + step_size stop_iter = False if end >= num_items: end = num_items stop_iter = True # or get_embedding data_ndb.iloc[start:end, embedding_col_index] = data_ndb.iloc[start:end, full_addr_col_index].apply(get_sbert_embedding) progress_bar.progress(value=progress, text=f"NDB embeddings: {(i + 1) * step_size} processed out of {num_items}") if stop_iter: break st.write(f"Embeddings for NDB calculated... matching") progress_bar = st.progress(0) num_items = len(data_caqh) 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 >= similarity_threshold: data_caqh.at[i, 'matched-addr'] = matched_row['full-addr'] data_caqh.at[i, 'similarity-score'] = max_similarity else: print(f"max similarity was {max_similarity}") data_caqh.at[i, 'matched-addr'] = 'No Matches' progress = i / num_items if progress > 1.0: progress = 1.0 progress_bar.progress(value=progress, text=f"matching similarities - {i} done out of {num_items}") # 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) # Calculate stats. total_items = len(data_caqh) item_without_matches = data_caqh['matched-addr'].value_counts().get('No Matches', 0) items_with_matches = total_items - item_without_matches; percent_matched = (items_with_matches/total_items)*100.0 st.write(f"From total matches {total_items}, {items_with_matches} items matched, {item_without_matches} items did not match, {percent_matched:.2f}% matched") # Create an in-memory binary stream output = BytesIO() # Save the DataFrame to the binary stream as an Excel file with pd.ExcelWriter(output, engine='xlsxwriter') as writer: data_caqh.to_excel(writer, sheet_name='Sheet1', index=False) writer.save() # Get the binary data from the stream data = output.getvalue() # Add a download button for the Excel file st.download_button( label='Download CAQH matches as Excel file', data=data, file_name='data.xlsx', mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' ) st.header('NDB data') st.dataframe(data_ndb, use_container_width=True)