Update app.py

app.py

@@ -12,46 +12,3 @@ import re
 # Initialize the Streamlit app
 st.title('Document-Based Q&A System')
 
-# API Key input securely
-api_key = st.text_input("Enter your OpenAI API key:", type="password")
-if api_key:
-    os.environ["OPENAI_API_KEY"] = api_key
-    st.success("API Key has been set!")
-
-# File uploader
-uploaded_file = st.file_uploader("Upload your document", type=['txt'])
-if uploaded_file is not None:
-    # Read and process the document
-    text_data = uploaded_file.getvalue().decode("utf-8")
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    data = text_splitter.split_documents(text_data)
-
-    # Create vector store
-    embeddings = OpenAIEmbeddings()
-    vectorstore = FAISS.from_documents(data, embedding=embeddings)
-
-    # Create conversation chain
-    llm = ChatOpenAI(temperature=0.3, model_name="gpt-4-turbo")
-    memory = ConversationBufferMemory(
-        memory_key='chat_history', return_messages=True, output_key='answer')
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        chain_type="stuff",
-        retriever=vectorstore.as_retriever(),
-        memory=memory,
-        return_source_documents=True
-    )
-
-    # Question input
-    query = st.text_input("Ask a question about the document:")
-    if query:
-        result = conversation_chain({"question": query})
-        answer = result["answer"]
-        st.write("Answer:", answer)
-
-        # Optionally display source text snippets
-        if st.checkbox("Show source text snippets"):
-            st.write("Source documents:")
-            for i in result["source_documents"]:
-                res = re.search(r'^[^\n]*', i.page_content)
-                st.write(i.page_content[res.span()[0]:res.span()[1]])