Update app.py

app.py

@@ -9,12 +9,53 @@ from langchain.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 import re
 def main():
-    st.title('Simple Streamlit App')
+# 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)
 
-    st.write("Welcome to our simple static Streamlit application!")
+        # 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]])
 
-    if st.button('Click me!'):
-        st.write('Thanks for clicking!')
 
 if __name__ == "__main__":
     main()