Spaces:
Build error
Build error
File size: 4,517 Bytes
52c1998 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
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_payslip_for_display(analysis_results_for_id):
data_to_display = {}
data_to_display["document_category"] = "income_document"
data_to_display["document_type"] = "payslip"
data_to_display["employee_name"] = analysis_results_for_id.get(
"employee_name", None)
data_to_display["employer_name"] = analysis_results_for_id.get(
"employer_name", None)
data_to_display["employee_id"] = analysis_results_for_id.get(
"employee_id", None)
data_to_display["employee_address"] = analysis_results_for_id.get(
"employee_address", None)
data_to_display["employer_address"] = analysis_results_for_id.get(
"employer_address", None)
data_to_display["tax_code"] = analysis_results_for_id.get(
"tax_code", None)
data_to_display["payslip_date"] = analysis_results_for_id.get(
"payslip_date", None)
data_to_display["pay_period_start"] = analysis_results_for_id.get(
"pay_period_start", None)
data_to_display["pay_period_end"] = analysis_results_for_id.get(
"pay_period_end", None)
data_to_display["payment_frequency"] = analysis_results_for_id.get(
"payment_frequency", None)
data_to_display["basic_pay"] = analysis_results_for_id.get(
"basic_pay", None)
data_to_display["net_pay"] = analysis_results_for_id.get(
"net_pay", None)
data_to_display["gross_pay"] = analysis_results_for_id.get(
"gross_pay", None)
data_to_display["salary_components"] = analysis_results_for_id.get(
"salary_components", None)
data_to_display["ni_contribution"] = analysis_results_for_id.get(
"ni_contribution", None)
data_to_display["tax_deduction"] = analysis_results_for_id.get(
"tax_deduction", None)
data_to_display["other_deductions"] = analysis_results_for_id.get(
"other_deductions", None)
return data_to_display
def display_payslip(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 not in ['other_deductions', 'salary_components']:
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("Other Deductions")
other_deductions_dict = analysis_results_pruned['other_deductions']
logger.info(f"other_deductions_dict : {other_deductions_dict}")
try:
# Flatten the nested list structure
flat_list = []
for sublist in other_deductions_dict:
for item in sublist:
if isinstance(item, list):
flat_list.extend(item)
elif isinstance(item, dict):
flat_list.append(item)
# Filter and format into dataframe if valid
if flat_list and isinstance(flat_list[0], dict) and 'name' in flat_list[0] and 'amount' in flat_list[0]:
df = pd.DataFrame(flat_list)
df.columns = ['Key', 'Value']
df = df.fillna(value="Missing")
df.index += 1
st.dataframe(df, use_container_width=True)
else:
raise ValueError("Data is not in expected dictionary format.")
except Exception as e:
logger.info(f"Different format for other deductions: {e}")
st.dataframe(other_deductions_dict)
logger.info(f"simple_df: {simple_df}")
|