app.py

import base64
import os
import re
from io import BytesIO
from pathlib import Path

import gradio as gr
import pandas as pd
import json
from langchain.schema.output_parser import OutputParserException
from PIL import Image

from openpyxl import load_workbook
from openpyxl.utils import get_column_letter


import categories
from categories import Category
from main import process_image, process_pdf

HF_TOKEN = os.getenv("HF_TOKEN")
PDF_IFRAME = """
<div style="border-radius: 10px; width: 100%; overflow: hidden;">
    <iframe
        src="data:application/pdf;base64,{0}"
        width="100%"
        height="400"
        type="application/pdf">
    </iframe>
</div>"""

hf_writer_normal = gr.HuggingFaceDatasetSaver(
    HF_TOKEN, "automatic-reimbursement-tool-demo", separate_dirs=False
)
hf_writer_incorrect = gr.HuggingFaceDatasetSaver(
    HF_TOKEN, "automatic-reimbursement-tool-demo-incorrect", separate_dirs=False
)
# with open("examples/example1.pdf", "rb") as pdf_file:
#     base64_pdf = base64.b64encode(pdf_file.read())


# example_paths = []
# current_file_path = None

# def ignore_examples(function):
#     def new_function(*args, **kwargs):
#         global example_paths, current_file_path
#         if current_file_path not in example_paths:
#             return function(*args, **kwargs)


def display_file(input_files):
    global current_file_paths

    # Initialize the list of current file paths
    current_file_paths = [file.name for file in input_files]

    if not input_files:
        return gr.HTML.update(visible=False), gr.Image.update(visible=False)
    
    # Check if there's any PDF file among the uploaded files
    pdf_base64 = None
    for input_file in input_files:
        if input_file.name.endswith(".pdf"):
            with open(input_file.name, "rb") as pdf_file:
                pdf_base64 = base64.b64encode(pdf_file.read()).decode()
                break  # Assuming only one PDF is present

    if pdf_base64:
        return gr.HTML.update(PDF_IFRAME.format(pdf_base64), visible=True), gr.Image.update(visible=False)
    else:
        # You can choose to display the first image in the list or handle multiple images differently
        image = Image.open(input_files[0].name)
        return gr.HTML.update(visible=False), gr.Image.update(image, visible=True)




def show_intermediate_outputs(show_intermediate):
    if show_intermediate:
        return gr.Accordion.update(visible=True)
    else:
        return gr.Accordion.update(visible=False)


def show_share_contact(share_result):
    return gr.Textbox.update(visible=share_result)


def clear_inputs():
    return gr.File.update(value=None)


def clear_outputs(input_file):
    if input_file:
        return None, None, None, None


def extract_text(input_file):
    """Takes the input file and updates the extracted text"""
    if not input_file:
        gr.Error("Please upload a file to continue!")
        return gr.Textbox.update()
    # Send change to preprocessed image or to extracted text
    if input_file.name.endswith(".pdf"):
        text = process_pdf(Path(input_file.name), extract_only=True)
    else:
        text = process_image(Path(input_file.name), extract_only=True)
    return text


def categorize_text(text):
    """Takes the extracted text and updates the category"""
    category = categories.categorize_text(text)
    return category


def query(category, text):
    """Takes the extracted text and category and updates the chatbot in two steps:
    1. Construct a prompt
    2. Generate a response
    """
    
    #category = Category[category]
    
    chain = categories.category_modules[category].chain
    formatted_prompt = chain.prompt.format_prompt(
        text=text,
        format_instructions=chain.output_parser.get_format_instructions(),
    )
    question = f""
    if len(formatted_prompt.messages) > 1:
        question += f"**System:**\n{formatted_prompt.messages[0].content}"
    question += f"\n\n**Human:**\n{formatted_prompt.messages[-1].content}"
    yield gr.Chatbot.update([[question, "Generating..."]])

    result = chain.generate(
        input_list=[
            {
                "text": text,
                "format_instructions": chain.output_parser.get_format_instructions(),
            }
        ]
    )
    answer = result.generations[0][0].text
    yield gr.Chatbot.update([[question, answer]])


PARSING_REGEXP = r"\*\*System:\*\*\n([\s\S]+)\n\n\*\*Human:\*\*\n([\s\S]+)"


def parse(category, chatbot):
    """Takes the chatbot prompt and response and updates the extracted information"""
    global PARSING_REGEXP

    
    chatbot_responses = []

    for response in chatbot:
        chatbot_responses.append(response[1])
    
    if not chatbot_responses:
        # Handle the case when there are no chatbot responses
        return {"status": "No responses available"}

    answer = chatbot_responses[-1] 
    
    # try:
    #     answer = next(chatbot)[1]  
    # except StopIteration:
    #     answer = "" 
    
    if category not in Category.__members__:
        # Handle the case when an invalid category is provided
        answer="test"  
        
    #category = Category[category]
        
    chain = categories.category_modules[category].chain
    yield {"status": "Parsing response..."}
    try:
        information = chain.output_parser.parse(answer)


        information = information.json() if information else {}
    except OutputParserException as e:
        information = {
            "details": str(e),
            "output": e.llm_output,
        }
    yield information



def activate_flags():
    return gr.Button.update(interactive=True), gr.Button.update(interactive=True)


def deactivate_flags():
    return gr.Button.update(interactive=False), gr.Button.update(interactive=False)


def flag_if_shared(flag_method):
    def proxy(share_result, request: gr.Request, *args, **kwargs):
        if share_result:
            return flag_method(request, *args, **kwargs)
    return proxy

def save_df_to_excel_with_autowidth(df, filename):
    # Save DataFrame to Excel without any formatting
    df.to_excel(filename, index=False, engine='openpyxl')
    
    # Open the Excel file with openpyxl to adjust column widths
    book = load_workbook(filename)
    sheet = book.active
    
    # Loop through columns and adjust the width based on max length in each column
    for column in sheet.columns:
        max_length = 0
        column = [cell for cell in column]  
        for cell in column:
            try:
                if len(str(cell.value)) > max_length:
                    max_length = len(cell.value)
            except:
                pass
        adjusted_width = (max_length + 2)  # adding a little extra space
        sheet.column_dimensions[get_column_letter(column[0].column)].width = adjusted_width

    # Save the changes back to the Excel file
    book.save(filename)
    


def process_and_output_files(input_files):
    data = []

    total_amount = 0
    item_no = 1
    
    for file in input_files:
        # Extract and categorize text for each file
        text = extract_text(file)
        category = categorize_text(text)

        chatbot_response = query(category, text)  # Convert the generator to a list
        #parsed_info = parse(category, chatbot_response)
        chats=list(chatbot_response)
        # Append the relevant data for this file to the output_data list
        # data.append(
        #     #"File Name": file.name,
        #     #"Extracted Text": text,
        #     #"Category": category,
        #     #"Chatbot Response": chatbot_response,  # Access the first element as a list
        #     #"trial" : chats,
        #     chats[1]["value"][0][1] ,
        # )

        response_dict = json.loads(chats[1]["value"][0][1])
        if category.name == "TRAVEL_CAB" : 
        # Extract the relevant data
        
            extracted_data = {
                "S.No.": item_no,
                "Nature of Expenditure": response_dict.get("summary"),
                "Billing Date": response_dict.get("issue_date"),
                "Bill/Invoice No.": "NA",
                "Amount(Rs.)": response_dict.get("total"),
    
            }
        else:
            extracted_data = {
                "S.No.": item_no,
                "Nature of Expenditure": response_dict.get("summary"),
                "Billing Date": response_dict.get("issue_date"),
                "Bill/Invoice No.": response_dict.get("uids"),
                "Amount(Rs.)": response_dict.get("total")
                
            }
            

        total_amount+=response_dict.get("total")

        # Append the relevant data for this file to the data list
        data.append(extracted_data)

        item_no=item_no+1
        
    total_data = {
        "S.No.": "",
        "Nature of Expenditure": "Total Amount",
        "Billing Date": "",
        "Bill/Invoice No.": "",
        "Amount(Rs.)": total_amount
    }
    data.append(total_data)
    
    string_data = []
    for item in data:
        string_item = {key: str(value) for key, value in item.items()}
        string_data.append(string_item)

        
    df = pd.DataFrame(string_data)
    
    filename = "output.xlsx"
    save_df_to_excel_with_autowidth(df, filename)
    
    table_html = df.to_html(classes="table table-bordered", index=True)
    scrollable_table = f'<div style="overflow-x: auto;">{table_html}</div>'
    
    return scrollable_table, filename   

    #return data





with gr.Blocks(title="Automatic Reimbursement Tool Demo") as page:
    gr.Markdown("<center><h1>Automatic Reimbursement Tool Demo</h1></center>")
    gr.Markdown("<h2>Description</h2>")
    gr.Markdown(
        "The reimbursement filing process can be time-consuming and cumbersome, causing "
        "frustration for faculty members and finance departments. Our project aims to "
        "automate the information extraction involved in the process by feeding "
        "extracted text to language models such as ChatGPT. This demo showcases the "
        "categorization and extraction parts of the pipeline. Categorization is done "
        "to identify the relevant details associated with the text, after which "
        "extraction is done for those details using a language model."
    )
    gr.Markdown("<h2>Try it out!</h2>")
    with gr.Box() as demo:
        with gr.Row():
            with gr.Column(variant="panel"):
                gr.HTML(
                    '<div><center style="color:rgb(200, 200, 200);">Input</center></div>'
                )
                pdf_preview = gr.HTML(label="Preview", show_label=True, visible=False)
                image_preview = gr.Image(
                    label="Preview", show_label=True, visible=False, height=350
                )
                input_file = gr.File(
                    label="Input receipt",
                    show_label=True,
                    type="file",
                    file_count="multiple",
                    file_types=["image", ".pdf"],
                )
                input_file.change(
                    display_file, input_file, [pdf_preview, image_preview]
                )

                with gr.Row():
                    clear = gr.Button("Clear", variant="secondary")
                    submit_button = gr.Button("Submit", variant="primary")

                show_intermediate = gr.Checkbox(
                    False,
                    label="Show intermediate outputs",
                    info="There are several intermediate steps in the process such as "
                         "preprocessing, OCR, chatbot interaction. You can choose to "
                         "show their results here.",
                    visible=False,  # Shortcut for removal
                )
                share_result = gr.Checkbox(
                    True,
                    label="Share results",
                    info="Sharing your result with us will help us improve this tool.",
                    interactive=True,
                )
                contact = gr.Textbox(
                    type="email",
                    label="Contact",
                    interactive=True,
                    placeholder="Enter your email address",
                    info="Optionally, enter your email address to allow us to contact "
                         "you regarding your result.",
                    visible=True,
                )
                share_result.change(show_share_contact, share_result, [contact])

            with gr.Column(variant="panel"):
                gr.HTML(
                    '<div><center style="color:rgb(200, 200, 200);">Output</center></div>'
                )
                category = gr.Dropdown(
                    value=None,
                    choices=Category.__members__.keys(),
                    label=f"Recognized category ({', '.join(Category.__members__.keys())})",
                    show_label=True,
                    interactive=False,
                )
                intermediate_outputs = gr.Accordion(
                    "Intermediate outputs", open=True, visible=False
                )
                with intermediate_outputs:
                    extracted_text = gr.Textbox(
                        label="Extracted text",
                        show_label=True,
                        max_lines=5,
                        show_copy_button=True,
                        lines=5,
                        interactive=False,
                    )
                    chatbot = gr.Chatbot(
                        None,
                        label="Chatbot interaction",
                        show_label=True,
                        interactive=False,
                        height=240,
                    )
                #information = gr.JSON(label="Extracted information")
                table_display = gr.HTML(label="Table Display")
                excel_download = gr.File(label="Download Excel", type="file")

                
                with gr.Row():
                    flag_incorrect_button = gr.Button(
                        "Flag as incorrect", variant="stop", interactive=True
                    )
                    flag_irrelevant_button = gr.Button(
                        "Flag as irrelevant", variant="stop", interactive=True
                    )
            show_intermediate.change(
                show_intermediate_outputs, show_intermediate, [intermediate_outputs]
            )

            clear.click(clear_inputs, None, [input_file]).then(
                deactivate_flags,
                None,
                [flag_incorrect_button, flag_irrelevant_button],
            )

            hf_writer_normal.setup(
                [input_file, extracted_text, category, chatbot, table_display, contact],
                flagging_dir="flagged",
            )
            flag_method = gr.flagging.FlagMethod(
                hf_writer_normal, "", "", visual_feedback=False
            )

            submit_button.click(
                clear_outputs,
                [input_file],
                [extracted_text, category, chatbot, table_display],
            ).then(
                process_and_output_files,
                [input_file],
                [table_display, excel_download],  # Adding excel_download here
            ).then(
                flag_if_shared(flag_method),
                [
                    share_result,
                    input_file,
                    extracted_text,
                    category,
                    chatbot,
                    table_display,
                    contact,
                ],
                None,
                preprocess=False,
            )

            hf_writer_incorrect.setup(
                [input_file, extracted_text, category, chatbot, table_display, contact],
                flagging_dir="flagged_incorrect",
            )
            flag_incorrect_method = gr.flagging.FlagMethod(
                hf_writer_incorrect,
                "Flag as incorrect",
                "Incorrect",
                visual_feedback=True,
            )
            flag_incorrect_button.click(
                lambda: gr.Button.update(value="Saving...", interactive=False),
                None,
                flag_incorrect_button,
                queue=False,
            )
            flag_incorrect_button.click(
                flag_incorrect_method,
                inputs=[
                    input_file,
                    extracted_text,
                    category,
                    chatbot,
                    table_display,
                    contact,
                ],
                outputs=[flag_incorrect_button],
                preprocess=False,
                queue=False,
            )

            flag_irrelevant_method = gr.flagging.FlagMethod(
                hf_writer_incorrect,
                "Flag as irrelevant",
                "Irrelevant",
                visual_feedback=True,
            )
            flag_irrelevant_button.click(
                lambda: gr.Button.update(value="Saving...", interactive=False),
                None,
                flag_irrelevant_button,
                queue=False,
            )
            flag_irrelevant_button.click(
                flag_irrelevant_method,
                inputs=[
                    input_file,
                    extracted_text,
                    category,
                    chatbot,
                    table_display,
                    contact,
                ],
                outputs=[flag_irrelevant_button],
                preprocess=False,
                queue=False,
            )

page.queue(
    concurrency_count=20,
    max_size=1,
)
page.launch(show_api=True, show_error=True, debug=True)