Update app.py

app.py

@@ -14,14 +14,11 @@ 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")
-	
+		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 = MarkdownExtractorConfig(batch_multiplier=2)
-	
 	result = markdown_extractor.extract(content, config)
 	return result
 
@@ -29,59 +26,35 @@ with gr.Blocks(title="PDF data extraction with Marker & Indexify") as marker_dem
 	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>")
+	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 = 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",
-			)
+			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>")
 
-		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]
-	)
+	go_button.click(fn=use_marker, inputs=[pdf_file], outputs=[model_output_text_box])
 
 @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")
-	
+		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 = PDFExtractorConfig(output_types=["text", "table"])
-	
 	result = pdf_extractor.extract(content, config)
 	return result
 
@@ -89,59 +62,35 @@ with gr.Blocks(title="PDF data extraction with PDF Extractor & Indexify") as pdf
 	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>")
+	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 = 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",
-			)
+			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>")
 
-		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]
-	)
+	go_button.click(fn=use_pdf_extractor, inputs=[pdf_file], outputs=[model_output_text_box])
 
 @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")
-	
+		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 = GeminiExtractorConfig(prompt="Extract all text from the document.", model_name="gemini-1.5-flash", key=key)
-	
 	result = gemini_extractor.extract(content, config)
 	return result
 
@@ -149,66 +98,37 @@ with gr.Blocks(title="PDF data extraction with Gemini & Indexify") as gemini_dem
 	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>")
+	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/multimodal_gemini.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 = gr.File(type="filepath")
-			
 			gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>")
-			
-			key = gr.Textbox(
-                info="Please enter your GEMINI_API_KEY",
-				label="Key:"
-			)
-			
+			key = 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 = gr.Textbox(
-				label="Extractor Output",
-				elem_id="model_output_text_box",
-			)
+			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>")
 
-		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, key],
-		outputs = [model_output_text_box]
-	)
+	go_button.click(fn=use_gemini, inputs=[pdf_file, key], outputs=[model_output_text_box])
 
 @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")
-	
+		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 = OAIExtractorConfig(prompt="Extract all text from the document.", model_name="gpt-4o", key=key)
-	
 	result = oai_extractor.extract(content, config)
 	return result
 
@@ -216,56 +136,30 @@ with gr.Blocks(title="PDF data extraction with OpenAI & Indexify") as openai_dem
 	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>")
+	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/multimodal_openai.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 = gr.File(type="filepath")
-			
 			gr.HTML("<p><b>Step 2:</b> Enter your API key.</p>")
-
-			key = gr.Textbox(
-                info="Please enter your OPENAI_API_KEY",
-				label="Key:"
-			)
-
+			key = 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 = gr.Textbox(
-				label="Extractor Output",
-				elem_id="model_output_text_box",
-			)
+			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>")
 
-		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, key],
-		outputs = [model_output_text_box]
-	)
+	go_button.click(fn=use_openai, inputs=[pdf_file, key], 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()
+demo.launch()