Update app.py

app.py

@@ -147,62 +147,41 @@ if input_type == 'URL':
     if base_url:
         urls = get_page_urls(base_url)
         retriever = get_retriever(urls)
+        llm_chain = create_chain(retriever)
 elif input_type == 'Upload PDF':
     uploaded_file = st.file_uploader("Upload your PDF here:", type="pdf")
     if uploaded_file:
         pdf_text = process_pdf(uploaded_file)
-        # Assume we process the PDF text into a format that can be used by your LLM
-        urls = [pdf_text]  # This should be adjusted to match your system's needs
-        retriever = get_retriever(urls)  # Make sure your retriever can handle raw text if not, adapt it.
+        # Process the PDF text into a format that can be used by your LLM
+        urls = [pdf_text]  # Adapt as needed for your system
+        retriever = get_retriever(urls)  # Ensure your retriever can handle raw text; if not, adapt it.
         llm_chain = create_chain(retriever)
 
     # We store the conversation in the session state.
     # This will be used to render the chat conversation.
     # We initialize it with the first message we want to be greeted with
     
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-            {"role": "assistant", "content": "How may I help you today?"}
-        ]
-
-    if "current_response" not in st.session_state:
-        st.session_state.current_response = ""
-
-    # We loop through each message in the session state and render it as
-    # a chat message.
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    
-    # 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"):
-    
-        # 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)
-    
-        # 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}
-        )
+# Initialize chat session state for storing messages and responses
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}]
+
+if "current_response" not in st.session_state:
+    st.session_state.current_response = ""
+
+# Render the chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Input and response handling
+if llm_chain and (user_prompt := st.chat_input("Your message here", key="user_input")):
+    # Add user input to the session state and chat window
+    st.session_state.messages.append({"role": "user", "content": user_prompt})
+    with st.chat_message("user"):
+        st.markdown(user_prompt)
     
-        # Add the response to the chat window
-        with st.chat_message("assistant"):
-            st.markdown(response)
+    # Generate and display the response using the LLM chain
+    response = llm_chain.run(user_prompt)
+    st.session_state.messages.append({"role": "assistant", "content": response})
+    with st.chat_message("assistant"):
+        st.markdown(response)