app.py

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)