Spaces:
Running
on
Zero
Running
on
Zero
| 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() | |
| 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 | |
| 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 | |
| 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 | |
| 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() |