app.py

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

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

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_file):
    global current_file_path
    current_file_path = input_file.name if input_file else None
    if not input_file:
        return gr.HTML.update(visible=False), gr.Image.update(visible=False)
    if input_file.name.endswith(".pdf"):
        with open(input_file.name, "rb") as input_file:
            pdf_base64 = base64.b64encode(input_file.read()).decode()
        return gr.HTML.update(
            PDF_IFRAME.format(pdf_base64), visible=True
        ), gr.Image.update(visible=False)
    else:
        # image = Image.open(input_file.name)
        return gr.HTML.update(visible=False), gr.Image.update(
            input_file.name, 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 submit(input_file, old_text):
    if not input_file:
        gr.Error("Please upload a file to continue!")
        return gr.Textbox.update()
    print("-"*5)
    print("New input")
    # 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)
    print("Extracted text")
    return text


def categorize_extracted_text(extracted_text):
    category = categories.categorize_text(extracted_text)
    print("Recognized category:", category)
    # gr.Info(f"Recognized category: {category}")
    return category


def parse_from_category(category, extracted_text):
    if not category:
        print("Updated with no category:", category)
        return (
            gr.Chatbot.update(None),
            gr.JSON.update(None),
            gr.Button.update(interactive=False),
            gr.Button.update(interactive=False),
        )
    category = Category[category]
    print("Parsing text from", category)
    chain = categories.category_modules[category].chain
    formatted_prompt = chain.prompt.format_prompt(
        text=extracted_text,
        format_instructions=chain.output_parser.get_format_instructions(),
    )
    result = chain.generate(
        input_list=[
            {
                "text": extracted_text,
                "format_instructions": chain.output_parser.get_format_instructions(),
            }
        ]
    )
    question = f""
    if len(formatted_prompt.messages) > 1:
        question += f"**System:**\n{formatted_prompt.messages[1].content}"
    question += f"\n\n**Human:**\n{formatted_prompt.messages[0].content}"
    print("\tConstructed prompt")
    answer = result.generations[0][0].text
    print("\tProcessed text")
    try:
        information = chain.output_parser.parse_with_prompt(answer, formatted_prompt)
        information = information.json() if information else {}
    except OutputParserException as e:
        information = {
            "error": "Unable to parse chatbot output",
            "details": str(e),
            "output": e.llm_output,
        }
    return (
        gr.Chatbot.update([[question, answer]]),
        gr.JSON.update(information),
        gr.Button.update(interactive=True),
        gr.Button.update(interactive=True),
    )


def dynamic_auto_flag(flag_method):
    def modified_flag_method(share_result, *args, **kwargs):
        if share_result:
            flag_method(*args, **kwargs)

    return modified_flag_method


# def save_example_and_submit(input_file):
#     example_paths.append(input_file.name)
#     submit(input_file, "")


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="single",
                    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.",
                )
                share_result = gr.Checkbox(
                    True,
                    label="Share results",
                    info="Sharing your result with us will help us immensely in improving 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")
                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])
            submit_button.click(
                submit,
                [input_file, extracted_text],
                [extracted_text],
            )
            submit_button.click(
                lambda input_file, category, chatbot, information: (
                    gr.Dropdown.update(None),
                    gr.Chatbot.update(None),
                    gr.Textbox.update(None),
                ) if input_file else (category, chatbot, information),
                [input_file, category, chatbot, information],
                [category, chatbot, information],
            )
            extracted_text.change(
                categorize_extracted_text,
                [extracted_text],
                [category],
            )
            category.change(
                parse_from_category,
                [category, extracted_text],
                [chatbot, information, flag_incorrect_button, flag_irrelevant_button],
            )

            hf_writer_normal.setup(
                [input_file, extracted_text, category, chatbot, information, contact],
                flagging_dir="flagged",
            )
            flag_method = gr.flagging.FlagMethod(
                hf_writer_normal, "", "", visual_feedback=True
            )
            information.change(
                dynamic_auto_flag(flag_method),
                inputs=[
                    share_result,
                    input_file,
                    extracted_text,
                    category,
                    chatbot,
                    information,
                    contact,
                ],
                outputs=None,
                preprocess=False,
                queue=False,
            )

            hf_writer_incorrect.setup(
                [input_file, extracted_text, category, chatbot, information, 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,
                    information,
                    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,
                    information,
                    contact,
                ],
                outputs=[flag_irrelevant_button],
                preprocess=False,
                queue=False,
            )


page.launch(show_api=True, show_error=True, debug=True)