Update app.py

app.py

@@ -20,9 +20,8 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
 
 
-data_path = "data"
-
-def create_retriever_from_chroma(data_path, vectorstore_path="docs/chroma/", search_type='mmr', k=7, chunk_size=250, chunk_overlap=20):
+def create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type='mmr', k=7, chunk_size=250, chunk_overlap=20):
+    data_path = "data"
     model_name = "sentence-transformers/all-mpnet-base-v2"
     model_kwargs = {'device': 'cpu'}
     encode_kwargs = {'normalize_embeddings': True}
@@ -82,19 +81,19 @@ def main():
         st.session_state["messages"] = [
         {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
     ]
-
+    
 
     user_question = st.text_input("Ask a question about your documents:")
-    
+
+    retriever = create_retriever_from_chroma(data_path, vectorstore_path="docs/chroma/", search_type='mmr', k=7, chunk_size=250, chunk_overlap=20)
  
     if user_question:
         handle_userinput(user_question)
 
 
-def handle_userinput(user_question):
+def handle_userinput(user_question,retriever):
     st.session_state.messages.append({"role": "user", "content": user_question})
     st.chat_message("user").write(user_question)
-    retriever = create_retriever_from_chroma(data_path, vectorstore_path="docs/chroma/", search_type='mmr', k=7, chunk_size=250, chunk_overlap=20)
     docs = retriever.invoke(user_question)
     
     doc_txt = [doc.page_content for doc in docs]