Update app.py

app.py

@@ -10,7 +10,12 @@ import os
 
 
 
-GOOGLE_API_KEY = os.environ['GOOGLE_API_KEY']
+load_dotenv()  # Load environment variables at the beginning
+GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY') #Use .get to handle if the variable is not present
+
+if not GOOGLE_API_KEY:
+    st.error("GOOGLE_API_KEY environment variable not set.")
+    st.stop()
 
 
 # Processing pdfs
@@ -44,30 +49,33 @@ def get_vectorstore(text_chunks):
 
 # Handling user questions
 def handle_userinput(question):
-    response = st.session_state.conversation({"question": question})
-    st.session_state.chat_history = response['chat_history']
-    st.write(response)  # Return only the answer from the response
+    if st.session_state.conversation: #Check if conversation is initialised
+        response = st.session_state.conversation({"question": question})
+        st.session_state.chat_history = response['chat_history']
+        answer = response['answer']  # Extract the 'answer' from the response
+        st.write(answer)
+    else:
+        st.write("Please process the documents first.") #Inform user to process documents
 
-# Storing converstations as chain of outputs
 def get_conversation_chain(vectorstore):
     llm = ChatGoogleGenerativeAI(model='gemini-2.0-flash-exp')
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
     conversation_chain = ConversationalRetrievalChain.from_llm(
         llm=llm,
         retriever=vectorstore.as_retriever(),
-        memory=memory
+        memory=memory,
     )
     return conversation_chain
 
-
 def main():
-    load_dotenv()
     st.set_page_config(page_title="Chat with multiple pdfs", page_icon=":books:")
 
     if "conversation" not in st.session_state:
         st.session_state.conversation = None
     if "chat_history" not in st.session_state:
         st.session_state.chat_history = None
+    if "vectorstore" not in st.session_state: #Store vectorstore in session state
+        st.session_state.vectorstore = None
 
     st.header("Chat with multiple PDFs :books:")
 
@@ -82,17 +90,10 @@ def main():
         if st.button("Process"):
             with st.spinner("Processing"):
                 raw_text = get_pdf_text(pdf_docs)
-
-                #convert to chunks
                 text_chunks = get_text_chunks(raw_text)
-                st.write(text_chunks)
-
-                #embeddings
-                vectorstore = get_vectorstore(text_chunks)
-
-
-                #create conversation chain
-                st.session_state.conversation = get_conversation_chain(vectorstore)
-
+                st.session_state.vectorstore = get_vectorstore(text_chunks) #Store in session state
+                st.session_state.conversation = get_conversation_chain(st.session_state.vectorstore) #Use stored vectorstore
 
 
+if __name__ == "__main__":
+    main()