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import os
import gradio as gr
import pandas as pd
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,
):
# 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
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"
)
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,
],
outputs=output,
)
# Leaderboard loaded from leaderboard.csv
df = pd.read_csv("leaderboard.csv")
leaderboard = gr.Dataframe(
value=df,
label="Leaderboard",
)
app.launch()
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