Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,179 Bytes
d0c9c37 ecddc77 d0c9c37 ecddc77 d0c9c37 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import spaces
import gradio as gr
from marker.markdown_extractor import MarkdownExtractorConfig, MarkdownExtractor
from pdf-extractor.pdf_extractor import PDFExtractorConfig, PDFExtractor
from gemini.gemini_extractor import GeminiExtractorConfig, GeminiExtractor
from openai.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]
)
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() |