Update app.py

app.py

@@ -173,35 +173,34 @@ elif input_type == 'Upload PDF':
             st.markdown(message["content"])
 
     
-    if retriever:    
-        # We initialize the quantized LLM from a local path.
-        # Currently most parameters are fixed but we can make them
-        # configurable.
-        llm_chain = create_chain(retriever)
+    # We initialize the quantized LLM from a local path.
+    # Currently most parameters are fixed but we can make them
+    # configurable.
+    llm_chain = create_chain(retriever)
     
-        # We take questions/instructions from the chat input to pass to the LLM
-        if user_prompt := st.chat_input("Your message here", key="user_input"):
+    # We take questions/instructions from the chat input to pass to the LLM
+    if user_prompt := st.chat_input("Your message here", key="user_input"):
     
-            # Add our input to the session state
-            st.session_state.messages.append(
-                {"role": "user", "content": user_prompt}
-            )
+        # Add our input to the session state
+        st.session_state.messages.append(
+            {"role": "user", "content": user_prompt}
+        )
     
-            # Add our input to the chat window
-            with st.chat_message("user"):
-                st.markdown(user_prompt)
+        # Add our input to the chat window
+        with st.chat_message("user"):
+            st.markdown(user_prompt)
     
-            # Pass our input to the llm chain and capture the final responses.
-            # It is worth noting that the Stream Handler is already receiving the
-            # streaming response as the llm is generating. We get our response
-            # here once the llm has finished generating the complete response.
-            response = llm_chain.run(user_prompt)
+        # Pass our input to the llm chain and capture the final responses.
+        # It is worth noting that the Stream Handler is already receiving the
+        # streaming response as the llm is generating. We get our response
+        # here once the llm has finished generating the complete response.
+        response = llm_chain.run(user_prompt)
     
-            # Add the response to the session state
-            st.session_state.messages.append(
-                {"role": "assistant", "content": response}
-            )
+        # Add the response to the session state
+        st.session_state.messages.append(
+            {"role": "assistant", "content": response}
+        )
     
-            # Add the response to the chat window
-            with st.chat_message("assistant"):
-                st.markdown(response)
+        # Add the response to the chat window
+        with st.chat_message("assistant"):
+            st.markdown(response)