Update on.py

on.py

@@ -0,0 +1,110 @@
+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()