File size: 4,801 Bytes
6b50a07 41761b0 6b50a07 | 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 | import streamlit as st
import pandas as pd
import os
from glob import glob
# Function to get list of document IDs
def get_document_ids():
# Assuming 'abc' is the folder containing documents
document_ids = [file.split('_')[0] for file in os.listdir('abc') if file.endswith('.png')]
return list(set(document_ids))
# Function to load image based on selected document ID and page number
def load_image(image_path, document_id, page_number):
im_path = f"{image_path}{document_id}-{page_number-1}.png"
if os.path.exists(im_path):
return im_path
else:
return None
# Function to load dataframe based on selected document ID
def load_dataframe(auto_csv_path, document_id, page_number, cols):
csv_path = glob(f'{auto_csv_path}*{document_id}*auto.csv')
print(csv_path)
if len(csv_path)>0:
auto_df = pd.read_csv(csv_path[0])
auto_df_page = auto_df[auto_df['Page#']==page_number][cols]
return auto_df_page
else:
return None
def path_setting(inbound_df_path):
auto_csv_path = 'PhaseData/Batch1/NLP_batch/'
image_path = 'PhaseData/Data/output/images/'
inbound_df = pd.read_csv(inbound_df_path)
pif_list = list(inbound_df.pif_key.values)
return pif_list, image_path, auto_csv_path
def main():
inbound_df_path = 'inbound_issues_tempus_2_q2.csv'
display_cols = ['Biomarker Name Source', 'Biomarker Test Type', 'NLP Result','NLP Value', 'NLP Variant',
'Biomarker Test Result Value Numeric 1', 'Biomarker Test Result Value Unit 1',
'Biomarker Test Result Value Numeric 2', 'Biomarker Test Result Value Unit 2',
'Biomarker Test Threshold Value Numeric 1', 'Biomarker Test Threshold Value Unit1',
'Biomarker Test Threshold Value Numeric 2', 'Biomarker Test Threshold Value Unit2']
pif_list, image_path, auto_csv_path = path_setting(inbound_df_path)
st.set_page_config(layout="wide") # Set layout to wide
# Slider to adjust the width of the columns
col1_width = st.sidebar.slider("Width of First Column", 0.1, 10.0, 1.0, 0.1)
col2_width = st.sidebar.slider("Width of Second Column", 0.1, 10.0, 6.5, 0.1)
col3_width = st.sidebar.slider("Width of Third Column", 0.1, 10.0, 5.0, 0.1)
# Divide the screen into three vertical panels with specified widths
col1, col2, col3 = st.columns([col1_width, col2_width, col3_width])
# Document Selection Panel
with col1:
st.write("### Document Selection")
document_id = st.selectbox("Select Document ID", options=pif_list)
pages = [int(i.split('-')[-1].split('.')[0]) for i in glob(f"{image_path}{document_id}*.png")]
page_number = st.number_input("Page Number", min_value=1, max_value=len(pages), step=1, value=1)
# Display Image Panel
with col2:
st.write("### Display Image")
im_path = load_image(image_path, document_id, page_number)
if im_path:
# Wrap the image into a vertical slider
st.slider("", 0, 1, 0, 1, key="image_slider", value=0, orientation='vertical', format="")
st.image(im_path, use_column_width=True)
else:
st.write("Image not found")
# Display DataFrame Panel
with col3:
st.write("### Display DataFrame")
df = load_dataframe(auto_csv_path, document_id, page_number, display_cols)
if df is not None:
columns_to_display = st.multiselect("Select Columns to Display", df.columns)
if len(columns_to_display) > 0:
st.write(df[columns_to_display])
# Input area for comments and suggestions
st.subheader("Comments and Suggestions")
comment = st.text_area("Add your comment here")
suggestions = st.text_area("Add your suggestions here")
# Save comments and suggestions to CSV when submitted
if st.button("Submit"):
data = {
"Document ID": [document_id],
"Page": [page_number],
"Comment": [comment],
"Suggestions": [suggestions]
}
comments_df = pd.DataFrame(data)
comments_csv = "comments.csv"
if os.path.exists(comments_csv):
comments_df.to_csv(comments_csv, mode="a", header=False, index=False)
else:
comments_df.to_csv(comments_csv, index=False)
else:
st.write("No columns selected")
else:
st.write("DataFrame not found")
if __name__ == "__main__":
main()
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