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import spaces
import gradio as gr
from marker.markdown_extractor import MarkdownExtractorConfig, MarkdownExtractor
from pdf.pdf_extractor import PDFExtractorConfig, PDFExtractor
from gemini.gemini_extractor import GeminiExtractorConfig, GeminiExtractor
from oai.oai_extractor import OAIExtractorConfig, OAIExtractor
from indexify_extractor_sdk import Content
markdown_extractor = MarkdownExtractor()
pdf_extractor = PDFExtractor()
gemini_extractor = GeminiExtractor()
oai_extractor = OAIExtractor()
@spaces.GPU
def use_marker(pdf_filepath):
if pdf_filepath is None:
raise gr.Error("Please provide some input PDF: upload an PDF file")
with open(pdf_filepath, "rb") as f:
pdf_data = f.read()
content = Content(content_type="application/pdf", data=pdf_data)
config = MarkdownExtractorConfig(batch_multiplier=2)
result = markdown_extractor.extract(content, config)
return result
@spaces.GPU
def use_pdf_extractor(pdf_filepath):
if pdf_filepath is None:
raise gr.Error("Please provide some input PDF: upload an PDF file")
with open(pdf_filepath, "rb") as f:
pdf_data = f.read()
content = Content(content_type="application/pdf", data=pdf_data)
config = PDFExtractorConfig(output_types=["text", "table"])
result = pdf_extractor.extract(content, config)
return result
@spaces.GPU
def use_gemini(pdf_filepath, key):
if pdf_filepath is None:
raise gr.Error("Please provide some input PDF: upload an PDF file")
with open(pdf_filepath, "rb") as f:
pdf_data = f.read()
content = Content(content_type="application/pdf", data=pdf_data)
config = GeminiExtractorConfig(prompt="Extract all text from the document.", model_name="gemini-1.5-flash", key=key)
result = gemini_extractor.extract(content, config)
return result
@spaces.GPU
def use_openai(pdf_filepath, key):
if pdf_filepath is None:
raise gr.Error("Please provide some input PDF: upload an PDF file")
with open(pdf_filepath, "rb") as f:
pdf_data = f.read()
content = Content(content_type="application/pdf", data=pdf_data)
config = OAIExtractorConfig(prompt="Extract all text from the document.", model_name="gpt-4o", key=key)
result = oai_extractor.extract(content, config)
return result
with gr.Blocks(title="PDF data extraction with Marker & Indexify") as marker_demo:
gr.HTML("<h1 style='text-align: center'>PDF data extraction with Marker & <a href='https://getindexify.ai/'>Indexify</a></h1>")
gr.HTML("<p style='text-align: center'>Indexify is a scalable realtime and continuous indexing and structured extraction engine for unstructured data to build generative AI applications</p>")
gr.HTML("<h3 style='text-align: center'>If you like this demo, please ⭐ Star us on <a href='https://github.com/tensorlakeai/indexify' target='_blank'>GitHub</a>!</h3>")
gr.HTML("<h4 style='text-align: center'>Here's an example notebook that demonstrates how to build a continous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/efficient_rag.ipynb' target='_blank'>extraction pipleine</a> with Indexify</h4>")
with gr.Row():
with gr.Column():
gr.HTML(
"<p><b>Step 1:</b> Upload a PDF file from local storage.</p>"
"<p style='color: #A0A0A0;'>Use this demo for single PDF file only. "
"You can extract from PDF files continuously and try various other extractors locally with "
"<a href='https://getindexify.ai/'>Indexify</a>.</p>"
)
pdf_file = gr.File(type="filepath")
with gr.Column():
gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
go_button = gr.Button(
value="Run extractor",
variant="primary",
)
model_output_text_box = gr.Textbox(
label="Extractor Output",
elem_id="model_output_text_box",
)
with gr.Row():
gr.HTML(
"<p style='text-align: center'>"
"Developed with 🫶 by <a href='https://getindexify.ai/' target='_blank'>Indexify</a> | "
"a <a href='https://www.tensorlake.ai/' target='_blank'>Tensorlake</a> product"
"</p>"
)
go_button.click(
fn=use_marker,
inputs = [pdf_file],
outputs = [model_output_text_box]
)
with gr.Blocks(title="PDF data extraction with PDF Extractor & Indexify") as pdf_demo:
gr.HTML("<h1 style='text-align: center'>PDF data extraction with PDF Extractor & <a href='https://getindexify.ai/'>Indexify</a></h1>")
gr.HTML("<p style='text-align: center'>Indexify is a scalable realtime and continuous indexing and structured extraction engine for unstructured data to build generative AI applications</p>")
gr.HTML("<h3 style='text-align: center'>If you like this demo, please ⭐ Star us on <a href='https://github.com/tensorlakeai/indexify' target='_blank'>GitHub</a>!</h3>")
gr.HTML("<h4 style='text-align: center'>Here's an example notebook that demonstrates how to build a continous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/SEC_10_K_docs.ipynb' target='_blank'>extraction pipleine</a> with Indexify</h4>")
with gr.Row():
with gr.Column():
gr.HTML(
"<p><b>Step 1:</b> Upload a PDF file from local storage.</p>"
"<p style='color: #A0A0A0;'>Use this demo for single PDF file only. "
"You can extract from PDF files continuously and try various other extractors locally with "
"<a href='https://getindexify.ai/'>Indexify</a>.</p>"
)
pdf_file = gr.File(type="filepath")
with gr.Column():
gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
go_button = gr.Button(
value="Run extractor",
variant="primary",
)
model_output_text_box = gr.Textbox(
label="Extractor Output",
elem_id="model_output_text_box",
)
with gr.Row():
gr.HTML(
"<p style='text-align: center'>"
"Developed with 🫶 by <a href='https://getindexify.ai/' target='_blank'>Indexify</a> | "
"a <a href='https://www.tensorlake.ai/' target='_blank'>Tensorlake</a> product"
"</p>"
)
go_button.click(
fn=use_pdf_extractor,
inputs = [pdf_file],
outputs = [model_output_text_box]
)
with gr.Blocks(title="PDF data extraction with Gemini & Indexify") as gemini_demo:
gr.HTML("<h1 style='text-align: center'>PDF data extraction with Gemini & <a href='https://getindexify.ai/'>Indexify</a></h1>")
gr.HTML("<p style='text-align: center'>Indexify is a scalable realtime and continuous indexing and structured extraction engine for unstructured data to build generative AI applications</p>")
gr.HTML("<h3 style='text-align: center'>If you like this demo, please ⭐ Star us on <a href='https://github.com/tensorlakeai/indexify' target='_blank'>GitHub</a>!</h3>")
gr.HTML("<h4 style='text-align: center'>Here's an example notebook that demonstrates how to build a continous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/multimodal_gemini.ipynb' target='_blank'>extraction pipleine</a> with Indexify</h4>")
with gr.Row():
with gr.Column():
gr.HTML(
"<p><b>Step 1:</b> Upload a PDF file from local storage.</p>"
"<p style='color: #A0A0A0;'>Use this demo for single PDF file only. "
"You can extract from PDF files continuously and try various other extractors locally with "
"<a href='https://getindexify.ai/'>Indexify</a>.</p>"
)
pdf_file = gr.File(type="filepath")
gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>")
key = gr.Textbox(
info="Please enter your GEMINI_API_KEY",
label="Key:"
)
with gr.Column():
gr.HTML("<p><b>Step 3:</b> Run the extractor.</p>")
go_button = gr.Button(
value="Run extractor",
variant="primary",
)
model_output_text_box = gr.Textbox(
label="Extractor Output",
elem_id="model_output_text_box",
)
with gr.Row():
gr.HTML(
"<p style='text-align: center'>"
"Developed with 🫶 by <a href='https://getindexify.ai/' target='_blank'>Indexify</a> | "
"a <a href='https://www.tensorlake.ai/' target='_blank'>Tensorlake</a> product"
"</p>"
)
go_button.click(
fn=use_gemini,
inputs = [pdf_file, key],
outputs = [model_output_text_box]
)
with gr.Blocks(title="PDF data extraction with OpenAI & Indexify") as openai_demo:
gr.HTML("<h1 style='text-align: center'>PDF data extraction with OpenAI & <a href='https://getindexify.ai/'>Indexify</a></h1>")
gr.HTML("<p style='text-align: center'>Indexify is a scalable realtime and continuous indexing and structured extraction engine for unstructured data to build generative AI applications</p>")
gr.HTML("<h3 style='text-align: center'>If you like this demo, please ⭐ Star us on <a href='https://github.com/tensorlakeai/indexify' target='_blank'>GitHub</a>!</h3>")
gr.HTML("<h4 style='text-align: center'>Here's an example notebook that demonstrates how to build a continous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/multimodal_openai.ipynb' target='_blank'>extraction pipleine</a> with Indexify</h4>")
with gr.Row():
with gr.Column():
gr.HTML(
"<p><b>Step 1:</b> Upload a PDF file from local storage.</p>"
"<p style='color: #A0A0A0;'>Use this demo for single PDF file only. "
"You can extract from PDF files continuously and try various other extractors locally with "
"<a href='https://getindexify.ai/'>Indexify</a>.</p>"
)
pdf_file = gr.File(type="filepath")
gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>")
key = gr.Textbox(
info="Please enter your OPENAI_API_KEY",
label="Key:"
)
with gr.Column():
gr.HTML("<p><b>Step 3:</b> Run the extractor.</p>")
go_button = gr.Button(
value="Run extractor",
variant="primary",
)
model_output_text_box = gr.Textbox(
label="Extractor Output",
elem_id="model_output_text_box",
)
with gr.Row():
gr.HTML(
"<p style='text-align: center'>"
"Developed with 🫶 by <a href='https://getindexify.ai/' target='_blank'>Indexify</a> | "
"a <a href='https://www.tensorlake.ai/' target='_blank'>Tensorlake</a> product"
"</p>"
)
go_button.click(
fn=use_openai,
inputs = [pdf_file, key],
outputs = [model_output_text_box]
)
demo = gr.TabbedInterface([marker_demo, pdf_demo, gemini_demo, openai_demo], ["Marker Extractor", "PDF Extractor", "Gemini Extractor", "OpenAI Extractor"], theme=gr.themes.Soft())
demo.queue()
demo.launch() |