Update app.py

app.py

@@ -26,12 +26,14 @@ def get_text_chunks(text):
     chunks = text_splitter.split_text(text)
     return chunks
 
+
 def get_vectorstore(text_chunks):
     metadatas = [{"source": f"{i}-pl"} for i in range(len(text_chunks))]
     embeddings = OpenAIEmbeddings()
     vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)
     return vectorstore
 
+
 def get_conversation_chain(vectorstore):
     llm = ChatOpenAI()
 
@@ -44,6 +46,7 @@ def get_conversation_chain(vectorstore):
     )
     return conversation_chain
 
+
 def handle_userinput(user_question):
     response = st.session_state.conversation({'question': user_question})
     st.session_state.chat_history = response['chat_history']
@@ -54,36 +57,38 @@ def handle_userinput(user_question):
         else:
             st.markdown(("AI: "+message.content))
 
+
 def main():
-    st.title("PDF Question Answering")
     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 st.session_state.conversation is not None:
-        st.header("Ask questions from your PDF")
-        user_question = st.text_input("Ask a question about your document:")
+        st.header("Ask questions from your PDF:books:")
+        user_question = st.chat_input("Ask a question about your documents:")
         if user_question:
             handle_userinput(user_question)
 
     if st.session_state.conversation is None:
         st.header("Upload your PDF here")
-        pdf_doc = st.file_uploader("Browse your file here", type="pdf")
+        pdf_doc = st.file_uploader("Browse your file here",type="pdf")
         if pdf_doc is not None:
             with st.spinner("Processing"):
                 # get pdf text
                 raw_text = extract_text_from_pdf(pdf_doc)
-
+    
                 # get the text chunks
                 text_chunks = get_text_chunks(raw_text)
-
+    
                 # create vector store
                 vectorstore = get_vectorstore(text_chunks)
-
+    
                 # create conversation chain
                 st.session_state.conversation = get_conversation_chain(
                     vectorstore)
 
+                st.rerun()
+
 if __name__ == '__main__':
-    main()
+    main()