addon.py

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()