Update app.py

app.py

@@ -18,31 +18,6 @@ def use_marker(pdf_filepath):
 	result = markdown_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 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_1 = gr.File(type="filepath")
-		with gr.Column():
-			gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
-			go_button_1 = gr.Button(value="Run Marker extractor", variant="primary")
-			model_output_text_box_1 = gr.Textbox(label="Extractor Output", elem_id="model_output_text_box_1")
-
-	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_1.click(fn=use_marker, inputs=[pdf_file_1], outputs=[model_output_text_box_1])
-
 @spaces.GPU
 def use_pdf_extractor(pdf_filepath):
 	if pdf_filepath is None:
@@ -54,32 +29,56 @@ def use_pdf_extractor(pdf_filepath):
 	result = pdf_extractor.extract(content, config)
 	return result
 
-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 continuous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/SEC_10_K_docs.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_2 = gr.File(type="filepath")
-		with gr.Column():
-			gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
-			go_button_2 = gr.Button(value="Run PDF extractor", variant="primary")
-			model_output_text_box_2 = gr.Textbox(label="Extractor Output", elem_id="model_output_text_box_2")
-
-	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_2.click(fn=use_pdf_extractor, inputs=[pdf_file_2], outputs=[model_output_text_box_2])
+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>")
+    	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_1 = gr.File(type="filepath")
+    		with gr.Column():
+    			gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
+    			go_button_1 = gr.Button(value="Run Marker extractor", variant="primary")
+    			model_output_text_box_1 = gr.Textbox(label="Extractor Output", elem_id="model_output_text_box_1")
+    
+    	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_1.click(fn=use_marker, inputs=[pdf_file_1], outputs=[model_output_text_box_1])
 
-demo = gr.TabbedInterface([marker_demo, pdf_demo], ["Marker Extractor", "PDF Extractor"], theme=gr.themes.Soft())
+    with gr.Tab("PDF data extraction with PDF Extractor & Indexify"):
+    	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 continuous <a href='https://github.com/tensorlakeai/indexify/blob/main/docs/docs/examples/SEC_10_K_docs.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_2 = gr.File(type="filepath")
+    		with gr.Column():
+    			gr.HTML("<p><b>Step 2:</b> Run the extractor.</p>")
+    			go_button_2 = gr.Button(value="Run PDF extractor", variant="primary")
+    			model_output_text_box_2 = gr.Textbox(label="Extractor Output", elem_id="model_output_text_box_2")
+    
+    	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_2.click(fn=use_pdf_extractor, inputs=[pdf_file_2], outputs=[model_output_text_box_2])
 
 demo.queue()
 demo.launch()