app.py

import os
import gradio as gr
import pandas as pd
import plotly.express as px
from lexoid.api import parse

parser_options = ["LLM_PARSE", "STATIC_PARSE", "AUTO"]


# Function to set the environment variables and parse the document
def run_parser(
    file,
    parser_type,
    model,
    pages_per_split,
    max_processes,
    as_pdf,
    x_tolerance,
    y_tolerance,
    save_dir,
    page_nums,
    router_priority,
    framework,
    temperature,
    depth,
    google_api_key,
    openai_api_key,
    huggingfacehub_api_token,
    together_api_key,
    openrouter_api_key,
    anthropic_api_key,
    fireworks_api_key,
    mistral_api_key,
):
    # Set environment variables
    os.environ["GOOGLE_API_KEY"] = google_api_key
    os.environ["OPENAI_API_KEY"] = openai_api_key
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingfacehub_api_token
    os.environ["TOGETHER_API_KEY"] = together_api_key
    os.environ["OPENROUTER_API_KEY"] = openrouter_api_key
    os.environ["ANTHROPIC_API_KEY"] = anthropic_api_key
    os.environ["FIREWORKS_API_KEY"] = fireworks_api_key
    os.environ["MISTRAL_API_KEY"] = mistral_api_key

    if file is None:
        return "Please upload a file to parse."

    kwargs = {
        "model": model,
        "pages_per_split": pages_per_split,
        "max_processes": max_processes,
        "as_pdf": as_pdf,
        "x_tolerance": x_tolerance,
        "y_tolerance": y_tolerance,
        "save_dir": save_dir,
        "page_nums": (
            [int(num.strip()) for num in page_nums.split(",")] if page_nums else None
        ),
        "router_priority": router_priority,
        "framework": framework,
        "temperature": temperature,
        "depth": depth,
    }

    # Clean None values
    kwargs = {k: v for k, v in kwargs.items() if v is not None}

    result = parse(path=file.name, parser_type=parser_type, **kwargs)

    if "raw" in result:
        return result["raw"]
    elif "segments" in result:
        return "\n\n".join([seg.get("content", "") for seg in result["segments"]])
    else:
        return str(result)


with gr.Blocks(title="Lexoid Document Parser") as app:
    gr.Markdown(
        "## 📄 Lexoid Document Parser\nUpload a document and customize how you'd like to parse it."
    )

    with gr.Row():
        file_input = gr.File(
            label="Upload Document",
            file_types=[".pdf", ".docx", ".html", ".txt"],
            type="filepath",
        )
        parser_type = gr.Dropdown(
            choices=parser_options, value="AUTO", label="Parser Type"
        )
        model_input = gr.Textbox(value="gemini-2.0-flash", label="LLM ID")
        framework = gr.Textbox(
            value="pdfplumber",
            label="Static Framework",
            placeholder="e.g., pdfplumber, slate",
        )

    with gr.Accordion("Advanced Options", open=False):
        pages_per_split = gr.Slider(
            minimum=1, maximum=20, value=4, step=1, label="Pages per Split"
        )
        max_processes = gr.Slider(
            minimum=1, maximum=16, value=4, step=1, label="Max Parallel Processes"
        )
        as_pdf = gr.Checkbox(label="Convert to PDF before parsing")
        x_tolerance = gr.Number(label="X-axis Tolerance", value=None)
        y_tolerance = gr.Number(label="Y-axis Tolerance", value=None)
        save_dir = gr.Textbox(
            label="Save Directory",
            placeholder="Path to save intermediate files (optional)",
        )
        page_nums = gr.Textbox(
            label="Page Numbers",
            placeholder="Comma-separated page numbers (e.g., 1,3,5)",
        )
        router_priority = gr.Dropdown(
            choices=["speed", "accuracy"], value="accuracy", label="Router Priority"
        )
        temperature = gr.Number(label="LLM Temperature", value=None)
        depth = gr.Number(label="Recursive Depth", value=None)

    # Adding the text boxes for the environment variables
    with gr.Row():
        google_api_key = gr.Textbox(
            label="Google API Key", placeholder="Enter Google API Key"
        )
        openai_api_key = gr.Textbox(
            label="OpenAI API Key", placeholder="Enter OpenAI API Key"
        )
        huggingfacehub_api_token = gr.Textbox(
            label="HuggingFaceHub API Token",
            placeholder="Enter HuggingFaceHub API Token",
        )
        together_api_key = gr.Textbox(
            label="Together API Key", placeholder="Enter Together API Key"
        )
        openrouter_api_key = gr.Textbox(
            label="OpenRouter API Key", placeholder="Enter OpenRouter API Key"
        )
        anthropic_api_key = gr.Textbox(
            label="Anthropic API Key", placeholder="Enter Anthropic API Key"
        )
        fireworks_api_key = gr.Textbox(
            label="Fireworks API Key", placeholder="Enter Fireworks API Key"
        )
        mistral_api_key = gr.Textbox(
            label="Mistral API Key", placeholder="Enter Mistral API Key"
        )

    output = gr.Markdown(label="Parsed Output")

    parse_button = gr.Button("Parse Document")
    parse_button.click(
        fn=run_parser,
        inputs=[
            file_input,
            parser_type,
            model_input,
            pages_per_split,
            max_processes,
            as_pdf,
            x_tolerance,
            y_tolerance,
            save_dir,
            page_nums,
            router_priority,
            framework,
            temperature,
            depth,
            google_api_key,
            openai_api_key,
            huggingfacehub_api_token,
            together_api_key,
            openrouter_api_key,
            anthropic_api_key,
            fireworks_api_key,
            mistral_api_key,
        ],
        outputs=output,
    )

    # Leaderboard loaded from leaderboard.csv
    df = pd.read_csv("leaderboard.csv")
    # Sort df by `sequence_matcher` in descending order and use new index as "Rank"
    df = df.sort_values(by="sequence_matcher", ascending=False).reset_index(drop=True)
    df.index += 1
    df.index.name = "Rank"
    leaderboard = gr.Dataframe(
        value=df,
        label="Leaderboard",
    )

    df = pd.read_csv("document_results.csv")
    fig = px.bar(
        df,
        x="Input File",
        y="sequence_matcher",
        color="model",
        labels={
            "Input File": "Document",
            "sequence_matcher": "Sequence Matcher Score",
            "model": "Model",
        },
        barmode="group",
        title="Sequence Matcher Scores by Document and Model",
    )

    gr.Plot(value=fig)


app.launch()