| 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 str(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 str(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 str(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 str(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_marker = 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_marker = gr.Textbox( |
| label="Extractor Output", |
| elem_id="model_output_text_box_marker", |
| ) |
|
|
| 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_marker], |
| outputs = [model_output_text_box_marker] |
| ) |
|
|
| 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_pdf = 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_pdf = gr.Textbox( |
| label="Extractor Output", |
| elem_id="model_output_text_box_pdf", |
| ) |
|
|
| 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_pdf], |
| outputs = [model_output_text_box_pdf] |
| ) |
|
|
| 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_gemini = gr.File(type="filepath") |
| |
| gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>") |
| |
| key_gemini = 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_gemini = gr.Textbox( |
| label="Extractor Output", |
| elem_id="model_output_text_box_gemini", |
| ) |
|
|
| 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_gemini, key_gemini], |
| outputs = [model_output_text_box_gemini] |
| ) |
|
|
| 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_oai = gr.File(type="filepath") |
| |
| gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>") |
|
|
| key_oai = 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_oai = gr.Textbox( |
| label="Extractor Output", |
| elem_id="model_output_text_box_oai", |
| ) |
|
|
| 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_oai, key_oai], |
| outputs = [model_output_text_box_oai] |
| ) |
|
|
| 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() |
