Update app.py

app.py

@@ -2,14 +2,10 @@ 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):
@@ -36,16 +32,16 @@ with gr.Blocks(title="PDF data extraction with Marker & Indexify") as marker_dem
 				"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")
+			pdf_file_1 = 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")
+			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.click(fn=use_marker, inputs=[pdf_file], outputs=[model_output_text_box])
+	go_button.click(fn=use_marker, inputs=[pdf_file_1], outputs=[model_output_text_box_1])
 
 @spaces.GPU
 def use_pdf_extractor(pdf_filepath):
@@ -72,94 +68,18 @@ with gr.Blocks(title="PDF data extraction with PDF Extractor & Indexify") as pdf
 				"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")
+			pdf_file_2 = 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")
+			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.click(fn=use_pdf_extractor, inputs=[pdf_file], outputs=[model_output_text_box])
+	go_button.click(fn=use_pdf_extractor, inputs=[pdf_file_2], outputs=[model_output_text_box_2])
 
-@spaces.GPU
-def use_gemini(pdf_filepath, key):
-	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 = GeminiExtractorConfig(prompt="Extract all text from the document.", model_name="gemini-1.5-flash", key=key)
-	result = gemini_extractor.extract(content, config)
-	return result
-
-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 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:")
-		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")
-
-	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, 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 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
-
-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 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:")
-		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")
-
-	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, 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 = gr.TabbedInterface([marker_demo, pdf_demo], ["Marker Extractor", "PDF Extractor"], theme=gr.themes.Soft())
 
 demo.queue()
 demo.launch()