Update app.py

app.py

@@ -2,10 +2,12 @@ import spaces
 import gradio as gr
 from marker.markdown_extractor import MarkdownExtractorConfig, MarkdownExtractor
 from pdf.pdf_extractor import PDFExtractorConfig, PDFExtractor
+from unstructuredio.unstructured_pdf import UnstructuredIOConfig, UnstructuredIOExtractor
 from indexify_extractor_sdk import Content
 
 markdown_extractor = MarkdownExtractor()
 pdf_extractor = PDFExtractor()
+unstructured_extractor = UnstructuredIOExtractor()
 
 @spaces.GPU
 def use_marker(pdf_filepath):
@@ -29,6 +31,17 @@ def use_pdf_extractor(pdf_filepath):
 	result = pdf_extractor.extract(content, config)
 	return result
 
+@spaces.GPU
+def use_unstructured(pdf_filepath):
+	if pdf_filepath is None:
+		raise gr.Error("Please provide some input PDF: upload a PDF file")
+	with open(pdf_filepath, "rb") as f:
+		pdf_data = f.read()
+	content = Content(content_type="application/pdf", data=pdf_data)
+	config = UnstructuredIOConfig(strategy="hi_res")
+	result = unstructured_extractor.extract(content, config)
+	return result
+
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Tab("PDF data extraction with Marker & Indexify"):
     	gr.HTML("<h1 style='text-align: center'>PDF data extraction with Marker & <a href='https://getindexify.ai/'>Indexify</a></h1>")
@@ -80,5 +93,30 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     	go_button_2.click(fn=use_pdf_extractor, inputs=[pdf_file_2], outputs=[model_output_text_box_2])
 
+    with gr.Tab("PDF data extraction with Unstructured IO & Indexify"):
+    	gr.HTML("<h1 style='text-align: center'>PDF data extraction with Unstructured IO & <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 continuous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/efficient_rag.ipynb' target='_blank'>extraction pipeline</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_3 = gr.File(type="filepath")
+    		with gr.Column():
+    			gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
+    			go_button_3 = gr.Button(value="Run Unstructured extractor", variant="primary")
+    			model_output_text_box_3 = gr.Textbox(label="Extractor Output", elem_id="model_output_text_box_3")
+    
+    	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_3.click(fn=use_unstructured, inputs=[pdf_file_3], outputs=[model_output_text_box_3])
+
 demo.queue()
 demo.launch()