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import streamlit as st
from utils.logger import setup_logger
import pandas as pd
from PIL import Image
import os
logger = setup_logger(__name__)
def prune_bank_statement_for_display(analysis_results_for_id):
data_to_display = {}
data_to_display["document_category"] = "bank_statement"
data_to_display["document_type"] = "bank_statement"
data_to_display["account_holder_name"] = analysis_results_for_id.get(
"account_holder_name", None)
data_to_display["account_holder_address"] = analysis_results_for_id.get(
"account_holder_address", None)
data_to_display["bank_name"] = analysis_results_for_id.get(
"bank_name", None)
data_to_display["account_number"] = analysis_results_for_id.get(
"account_number", None)
data_to_display["sort_code"] = analysis_results_for_id.get(
"sort_code", None)
data_to_display["statement_start_date"] = analysis_results_for_id.get(
"statement_start_date", None)
data_to_display["statement_end_date"] = analysis_results_for_id.get(
"statement_end_date", None)
data_to_display["salary_credits"] = analysis_results_for_id.get(
"salary_credits", None)
return data_to_display
def display_bank_statement(extracted_files, analysis_results_pruned):
col1, col2 = st.columns([2, 3])
logger.info(f"file_path while displaying: {extracted_files}")
st.markdown("---")
with col1:
if len(extracted_files) > 1:
st.image(extracted_files, caption=[os.path.basename(
img) for img in extracted_files], use_container_width=True)
else:
image = Image.open(extracted_files[0])
st.image(image, caption=os.path.basename(
extracted_files[0])) # ,
# use_container_width=True)
logger.info(
f"analysis_results_pruned : {analysis_results_pruned}")
with col2:
dict_str = {}
for key, value in analysis_results_pruned.items():
if key != 'salary_credits':
dict_str[key] = value
simple_df = pd.DataFrame.from_dict(
dict_str,
orient='index', columns=['Value']).reset_index()
simple_df.columns = ['Key', 'Value']
simple_df = simple_df.fillna(value="Missing")
simple_df.index += 1
st.dataframe(simple_df, use_container_width=True)
st.markdown("Salary Credits")
salary_dict = analysis_results_pruned['salary_credits']
logger.info(f"salary_dict : {salary_dict}")
for salary_details in salary_dict:
simple_df = pd.DataFrame.from_dict(
salary_details,
orient='index', columns=['Value']).reset_index()
simple_df.columns = ['Key', 'Value']
simple_df = simple_df.fillna(value="Missing")
simple_df.index += 1
st.dataframe(simple_df, use_container_width=True)
logger.info(f"simple_df: {simple_df}")
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