Update app.py

app.py

@@ -1567,6 +1567,10 @@ def fetch_google_flights(departure_id="JFK", arrival_id="BHM", outbound_date=cur
 # def insert_prompt(current_text, prompt):
 #     return prompt[0] if prompt else current_text
 
+# Create a global list to store uploaded document records
+uploaded_documents = []
+from datetime import datetime
+
 
 from langchain_core.documents import Document
 # Function to process PDF, extract text, split it into chunks, and upload to the vector DB
@@ -1587,6 +1591,17 @@ def process_pdf(pdf_file):
         chunk_id = vectorstore.add_documents([document])
         chunk_ids.append(chunk_id)
 
+    # Update the upload history
+    document_record = {
+        "Document Name": pdf_file.name,
+        "Upload Time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "Chunks": len(chunks),
+        "Pinecone Index": index_name
+    }
+    
+    # Add the record to the global list
+    uploaded_documents.append(document_record)    
+
     return f"Uploaded {len(chunks)} chunks to the vector database."
 
 
@@ -1700,11 +1715,17 @@ with gr.Blocks(theme='gradio/soft') as demo:
                 file_input = gr.File(label="Upload PDF", file_types=[".pdf"])
                 # Button to trigger processing
                 process_button = gr.Button("Process PDF and Upload")
+
+                # Dataframe to display uploaded document records
+                document_table = gr.Dataframe(headers=["Document Name", "Upload Time", "Chunks", "Pinecone Index"], interactive=False)
+
+            
                 # Output textbox for results
                 output_textbox = gr.Textbox(label="Result")
 
                 # Define button click action
-                process_button.click(fn=process_pdf, inputs=file_input, outputs=output_textbox)
+                # process_button.click(fn=process_pdf, inputs=file_input, outputs=output_textbox)
+                process_button.click(fn=process_pdf, inputs=[file_input, gr.State(uploaded_documents)], outputs=[document_table, output_textbox])