Update app.py

app.py

@@ -247,28 +247,28 @@ with gr.Blocks() as app:
     gr.Markdown("# VisRAG Pipeline: Vision-based Retrieval-augmented Generation on Multi-modality Documents")
     
     gr.Markdown("""
-- A Vision Language Model Dense Retriever ([VisRAG-Ret](https://huggingface.co/openbmb/VisRAG-Ret)) **directly reads** your PDFs **without need of OCR**, produce **multimodal dense representations** and build your personal library. 
+- A Vision Language Model Dense Retriever ([VisRAG-Ret](https://huggingface.co/openbmb/VisRAG-Ret)) **directly reads** your PDFs **without need for OCR**, generates **multimodal dense representations** and assists in building your personal library. 
 
-- **Ask a question**, it retrieve most relavant pages, then [MiniCPM-V-2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) will answer your question based on pages recalled, with strong multi-image understanding capability. 
+- **Ask a question**, and it will retrieve the most relevant pages. Then, [MiniCPM-V-2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) will answer your question based on the recalled pages, utilizing its strong multi-image understanding capabilities.
 
-    - It helps you read a long **visually-intensive** or **text-oriented** PDF document and find the pages that answer your question.
+    - It assists you in reading **lengthy**, **visually-intensive** or **text-oriented** PDF documents, helping you locate pages that answer your questions.
 
-    - It helps you build a personal library and retireve book pages from a large collection of books.
+    - It enables you to build a personal library and retrieve book pages from a large collection of books.
 
-    - It works like a human: read, store, retrieve, and answer with full vision.
+    - It works like a human: reading, storing, retrieving, and answering with full visual comprehension.
 """)
     
-    gr.Markdown("- Currently online demo support PDF document with less than 50 pages due to GPU time limit. Deploy on your own machine for longer PDFs and books.")
+    gr.Markdown("- The current online demo supports PDF documents with fewer than 50 pages due to GPU time limitations. For longer PDFs and books, consider deploying it on your own machine.")
     
     with gr.Row():
         file_input = gr.File(file_types=["pdf"], label="Step 1: Upload PDF")
         file_result = gr.Text(label="Knowledge Base ID (remember it, it is re-usable!)")
-        process_button = gr.Button("Process PDF (Don't click until PDF upload success)")
+        process_button = gr.Button("Process PDF (Don't click until PDF uploaded successfully)")
     
     process_button.click(add_pdf_gradio, inputs=[file_input], outputs=file_result)
 
     with gr.Row():
-        kb_id_input = gr.Text(label="Your Knowledge Base ID (paste your Knowledge Base ID here, it is re-usable:)")
+        kb_id_input = gr.Text(label="Your Knowledge Base ID (paste your Knowledge Base ID here, it is re-usable):")
         query_input = gr.Text(label="Your Queston")
         topk_input = inputs=gr.Number(value=5, minimum=1, maximum=10, step=1, label="Number of pages to retrieve")
         retrieve_button = gr.Button("Step2: Retrieve Pages")
@@ -276,10 +276,10 @@ with gr.Blocks() as app:
     with gr.Row():
         gr.Examples(
             examples=[
-                ["main_figure.pdf", "What is RAG-V?"],
+                ["main_figure.pdf", """What is RAG-V?"],
                 ["main_figure.pdf", "How does RAG-V perform?"]
             ],
-            inputs=[file_input, query_input],
+            inputs=[file_input, file_result, query_input],
         )
 
     with gr.Row():
@@ -301,7 +301,7 @@ with gr.Blocks() as app:
     upvote_button.click(upvote, inputs=[kb_id_input, query_input], outputs=None)
     downvote_button.click(downvote, inputs=[kb_id_input, query_input], outputs=None)
 
-    gr.Markdown("By using this demo, you agree to share your use data with us for research purpose, to help improve user experience.")
+    gr.Markdown("By using this demo, you agree to share your usage data with us for research purposes, helping us improve the user experience.")
 
 
 app.launch()