app.py

import os

# By default TLDExtract will try to pull files from the internet. I have instead downloaded this file locally to avoid the requirement for an internet connection.
os.environ['TLDEXTRACT_CACHE'] = 'tld/.tld_set_snapshot'

from tools.helper_functions import ensure_output_folder_exists, add_folder_to_path, put_columns_in_df, get_connection_params, output_folder, get_or_create_env_var
from tools.file_redaction import choose_and_run_redactor
from tools.file_conversion import prepare_image_or_text_pdf
from tools.data_anonymise import do_anonymise
from tools.auth import authenticate_user
#from tools.aws_functions import load_data_from_aws
import gradio as gr

add_folder_to_path("tesseract/")
add_folder_to_path("poppler/poppler-24.02.0/Library/bin/")

ensure_output_folder_exists()

chosen_redact_entities = ["TITLES", "PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "STREETNAME", "UKPOSTCODE"] 
full_entity_list = ["TITLES", "PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "STREETNAME", "UKPOSTCODE", 'CREDIT_CARD', 'CRYPTO', 'DATE_TIME', 'IBAN_CODE', 'IP_ADDRESS', 'NRP', 'LOCATION', 'MEDICAL_LICENSE', 'URL', 'UK_NHS']
language = 'en'

# Create the gradio interface
app = gr.Blocks(theme = gr.themes.Base())

with app:

    prepared_pdf_state = gr.State([])
    output_image_files_state = gr.State([])
    output_file_list_state = gr.State([])

    session_hash_state = gr.State()
    s3_output_folder_state = gr.State()

    gr.Markdown(
    """
    # Document redaction

    Redact personal information from documents, open text, or xlsx/csv tabular data. See the 'Redaction settings' to change various settings such as which types of information to redact (e.g. people, places), or terms to exclude from redaction.

    WARNING: This is a beta product. It is not 100% accurate, and it will miss some personal information. It is essential that all outputs are checked **by a human** to ensure that all personal information has been removed.

    Other redaction entities are possible to include in this app easily, especially country-specific entities. If you want to use these, clone the repo locally and add entity names from [this link](https://microsoft.github.io/presidio/supported_entities/) to the 'full_entity_list' variable in app.py.
    """)

    with gr.Tab("PDFs/images"):

        with gr.Accordion("Redact document", open = True):
            in_file = gr.File(label="Choose document/image files (PDF, JPG, PNG)", file_count= "multiple", file_types=['.pdf', '.jpg', '.png'])
            redact_btn = gr.Button("Redact document(s)", variant="primary")
        
        with gr.Row():
            output_summary = gr.Textbox(label="Output summary")
            output_file = gr.File(label="Output file")

        with gr.Row():
            convert_text_pdf_to_img_btn = gr.Button(value="Convert pdf to image-based pdf to apply redactions", variant="secondary", visible=False)
    
    with gr.Tab(label="Open text or Excel/csv files"):
        gr.Markdown(
    """
    ### Choose open text or a tabular data file (xlsx or csv) to redact.
    """
        )    
        with gr.Accordion("Paste open text", open = False):
            in_text = gr.Textbox(label="Enter open text", lines=10)
        with gr.Accordion("Upload xlsx (first sheet read only) or csv file(s)", open = False):
            in_file_text = gr.File(label="Choose an xlsx (first sheet read only) or csv files", file_count= "multiple", file_types=['.xlsx', '.csv', '.parquet', '.csv.gz'])

        in_colnames = gr.Dropdown(choices=["Choose a column"], multiselect = True, label="Select columns that you want to anonymise. Ensure that at least one named column exists in all files.")
        
        match_btn = gr.Button("Anonymise text", variant="primary")
        
        with gr.Row():
            text_output_summary = gr.Textbox(label="Output result")
            text_output_file = gr.File(label="Output file")

    with gr.Tab(label="Redaction settings"):
        gr.Markdown(
    """
    Define redaction settings that affect both document and open text redaction.
    """)
        with gr.Accordion("Settings for documents", open = True):
            in_redaction_method = gr.Radio(label="Default document redaction method - text analysis is faster is not useful for image-based PDFs. Imaged-based is slightly less accurate in general.", value = "Text analysis", choices=["Text analysis", "Image analysis"])
        with gr.Accordion("Settings for open text or xlsx/csv files", open = True):
            anon_strat = gr.Radio(choices=["replace", "redact", "hash", "mask", "encrypt", "fake_first_name"], label="Select an anonymisation method.", value = "replace") 

        with gr.Accordion("Settings for documents and open text/xlsx/csv files", open = True):
            in_redact_entities = gr.Dropdown(value=chosen_redact_entities, choices=full_entity_list, multiselect=True, label="Entities to redact (click close to down arrow for full list)")
            with gr.Row():
                in_redact_language = gr.Dropdown(value = "en", choices = ["en"], label="Redaction language (only English currently supported)", multiselect=False)
                in_allow_list = gr.Dataframe(label="Allow list - enter a new term to ignore for redaction on each row e.g. Lambeth -> add new row -> Lambeth 2030", headers=["Allow list"], row_count=1, col_count=(1, 'fixed'), value=[[""]], type="array", column_widths=["100px"], datatype='str')
            
    # AWS options - not yet implemented
    # with gr.Tab(label="Advanced options"):
    #     with gr.Accordion(label = "AWS data access", open = True):
    #         aws_password_box = gr.Textbox(label="Password for AWS data access (ask the Data team if you don't have this)")
    #         with gr.Row():
    #             in_aws_file = gr.Dropdown(label="Choose file to load from AWS (only valid for API Gateway app)", choices=["None", "Lambeth borough plan"])
    #             load_aws_data_button = gr.Button(value="Load data from AWS", variant="secondary")
                
    #         aws_log_box = gr.Textbox(label="AWS data load status")
    
    # ### Loading AWS data ###
    # load_aws_data_button.click(fn=load_data_from_aws, inputs=[in_aws_file, aws_password_box], outputs=[in_file, aws_log_box])

   
    # Document redaction
    redact_btn.click(fn = prepare_image_or_text_pdf, inputs=[in_file, in_redaction_method, in_allow_list],
                    outputs=[output_summary, prepared_pdf_state], api_name="prepare").\
    then(fn = choose_and_run_redactor, inputs=[in_file, prepared_pdf_state, in_redact_language, in_redact_entities, in_redaction_method, in_allow_list],
                    outputs=[output_summary, output_file, output_file_list_state], api_name="redact_doc")#.\
                    #then(fn = convert_text_pdf_to_img_pdf, inputs=[in_file, output_file_list_state],
                    #outputs=[output_summary, output_file])
    
    #convert_text_pdf_to_img_btn.click(fn = convert_text_pdf_to_img_pdf, inputs=[in_file, output_file_list_state],
    #                outputs=[output_summary, output_file], api_name="convert_to_img")

     # Open text interaction            
    in_file_text.upload(fn=put_columns_in_df, inputs=[in_file_text], outputs=[in_colnames])    
    match_btn.click(fn=do_anonymise, inputs=[in_file_text, in_text, anon_strat, in_colnames, in_redact_language, in_redact_entities, in_allow_list], outputs=[text_output_summary, text_output_file], api_name="redact_text")

    app.load(get_connection_params, inputs=None, outputs=[session_hash_state, s3_output_folder_state])

# Launch the Gradio app
COGNITO_AUTH = get_or_create_env_var('COGNITO_AUTH', '0')
print(f'The value of COGNITO_AUTH is {COGNITO_AUTH}')

if __name__ == "__main__":

    if os.environ['COGNITO_AUTH'] == "1":
        app.queue().launch(show_error=True, auth=authenticate_user)
    else:
        app.queue().launch(show_error=True, inbrowser=True)