Update app.py

app.py

@@ -123,6 +123,13 @@ def load_pdfs(selected_regulations, additional_pdfs):
     return "PDFs loaded and RAG system updated successfully!"
 
 def process_query(user_query):
+    global rag_chain, full_pdf_content
+    
+    if rag_chain is None or not full_pdf_content:
+        return ("Please load PDFs before asking questions.", 
+                "Please load PDFs before asking questions.", 
+                "Please load PDFs and initialize the system before asking questions.")
+    
     preprocessed_query = preprocess_query(user_query)
     
     # Get RAG response using Groq 
@@ -144,7 +151,7 @@ rag_chain = None
 # Gradio interface
 with gr.Blocks() as iface:
     gr.Markdown("# Data Protection Team")
-    gr.Markdown("Get responses combining advanced RAG, Long Context, and SOTA models to data protection related questions.")
+    gr.Markdown("**Get responses combining advanced RAG, Long Context, and SOTA models to data protection related questions**")
     
     with gr.Row():
         gdpr_checkbox = gr.Checkbox(label="GDPR (EU)")
@@ -156,7 +163,7 @@ with gr.Blocks() as iface:
     overflow-y: auto;
 }
 """
-    gr.Markdown("Optional: upload additional PDFs if needed (national regulation, school policy)")
+    gr.Markdown("**Optional: upload additional PDFs if needed (national regulation, school policy)**")
     additional_pdfs = gr.File(
         file_count="multiple",
         label="Upload additional PDFs",
@@ -167,11 +174,11 @@ with gr.Blocks() as iface:
     load_button = gr.Button("Load PDFs")
     load_output = gr.Textbox(label="Load Status")
     
-    gr.Markdown("Ask your data protection related question")
+    gr.Markdown("**Ask your data protection related question**")
     query_input = gr.Textbox(label="Your Question", placeholder="Ask your question here...")
     query_button = gr.Button("Submit Query")
     
-    gr.Markdown("Results")
+    gr.Markdown("**Results**")
     rag_output = gr.Textbox(label="RAG Pipeline (Llama3.1) Response")
     gemini_output = gr.Textbox(label="Long Context (Gemini 1.5 Pro) Response")
     final_output = gr.HTML(label="Final (GPT-4o) Response")