Update app.py

app.py

@@ -73,8 +73,8 @@ PQC_rules="""
 
 #===========================
 #retriever = retrieve_chunks(repo_id)
-retriever=retrieve_chunks_GPC()
-def create_qa_chain():    
+#retriever=retrieve_chunks_GPC()
+def create_qa_chain(retriever):    
     prompt = ChatPromptTemplate.from_template(
         "Use context to answer: {context}\n\nQ: {input}"
     )
@@ -87,7 +87,7 @@ def create_qa_chain():
     )
     return chain
 
-qa_chain = create_qa_chain()
+
 #=======================
 def chat(message, history):
     answer = qa_chain.invoke(message)
@@ -98,7 +98,8 @@ def chat(message, history):
     history.append([message, full])
     return history, ""
 #============starting extract_docx_text
-def respond(message, history):
+def respond(message, history, doc_choice):
+    
     word_count = len(message.strip().split())
     
     # If less than 3 words, do not call LLM, just ask user to clarify
@@ -110,12 +111,23 @@ def respond(message, history):
         ]
         return "", new_history
     else:
+        if doc_choice == "gpc_goods":        
+            retriever=retrieve_chunks_GPC()
+        else:
+            retriever = retrieve_chunks(repo_id)        
+        qa_chain = create_qa_chain(retriever)
         with get_openai_callback() as cb:
             answer = qa_chain.invoke(message)
         #answer = qa_chain.invoke(message)
         docs = retriever.invoke(message)
-        refs = [f"Page {d.metadata.get('page', 'N/A')}" for d in docs]
-        full_answer = f"Input tokens: {cb.prompt_tokens}, Ouput tokens: {cb.completion_tokens}, Total tokens: {cb.total_tokens}, Cost: ${cb.total_cost}\n{answer}\n\n**References:**\n" + "\n".join(refs)
+        refs=[]
+        if doc_choice == "gpc_goods":
+            refs= [f"NRL GPC point No: {d.metadata.get('condition_number', 'N/A')} / Heading: {d.metadata.get('condition_heading', 'N/A')}" for d in docs]
+        else:            
+            refs = [f"Page {d.metadata.get('page', 'N/A')}" for d in docs]
+        full_answer = f"""Input tokens: {cb.prompt_tokens}, 
+        Ouput tokens: {cb.completion_tokens}, Total tokens: {cb.total_tokens}, 
+        Cost: ${cb.total_cost}\n{answer}\n\n**References:**\n""" + "\n".join(refs)
         
         # CRITICAL: Append ONLY pure dicts - no metadata, tuples, or extras
         new_history = history + [  # Or history.append() then return history
@@ -388,15 +400,24 @@ with gr.Blocks(css=css) as demo:
             run_btn.click(check_compliance, inputs=inp, outputs=out)
         
         with gr.TabItem("NRL ChatBot"):
-                gr.Markdown("""# RAG Chatbot - Ask question and answer will be generated by AI from Only MANUAL FOR PROCUREMENT OF
-                            GOODS - NRL revised on 16.03.23""")
+                gr.Markdown("""# RAG Chatbot - NRL Documents""")
+                # RADIO BUTTON for document selection
+                doc_selector = gr.Radio(
+                    choices=[
+                        ("GPC Goods", "gpc_goods"),
+                        ("Procurement Manual", "manual")
+                    ],
+                    value="gpc_goods",  # Default
+                    label="Select Document:",
+                    info="Choose which document to query"
+                )
                 chatbot = gr.Chatbot(height=500)  # Defaults to messages
                 msg = gr.Textbox(placeholder="Ask a question...", label="Query")
                 submit_btn = gr.Button("Submit")
                 
                 # Events
-                submit_btn.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
-                msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+                submit_btn.click(respond, inputs=[msg, chatbot, doc_selector], outputs=[msg, chatbot])
+                msg.submit(respond, inputs=[msg, chatbot, doc_selector], outputs=[msg, chatbot])
         with gr.TabItem("Compliance Check of user technical doc"):
             with gr.Row():
                 inp_tech = gr.File(