Update app.py

app.py

@@ -5,29 +5,32 @@ from langchain.agents.agent_types import AgentType
 from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
 from langchain_openai import ChatOpenAI
 
+# Usa tu clave API de OpenAI aquí
+openai_api_key = 'sk-proj-8bJsop8rpnJkkMYhmOzwT3BlbkFJmwOmcQLI0dznDLpQMmuY'
+
 st.title("Excel ChatBot")
 st.subheader("Stack used: LangChain Agent, Streamlit, OpenAI LLM - by https://github.com/jaglinux", divider='rainbow')
 
-uploaded_file = st.file_uploader("Choose a file", type=['csv','xlsx'])
+uploaded_file = st.file_uploader("Choose a file", type=['csv', 'xlsx'])
 if uploaded_file is None:
     df = pd.read_csv("titanic.csv")
     st.write("Default file uploaded, titanic.csv")
 else:
-    # Can be used wherever a "file-like" object is accepted:
     if uploaded_file.name.endswith(".csv"):
         df = pd.read_csv(uploaded_file)
     elif uploaded_file.name.endswith(".xlsx"):
         df = pd.read_excel(uploaded_file)
 st.dataframe(df, height=5)
 
+# Crear el agente con el DataFrame cargado
 agent = create_pandas_dataframe_agent(
-    ChatOpenAI(temperature=0),
+    ChatOpenAI(temperature=0, openai_api_key=openai_api_key),
     df,
     verbose=True,
     agent_type=AgentType.OPENAI_FUNCTIONS,
 )
+
 if question := st.chat_input("Ask Question to the csv/xlsx provided"):
     response = agent.invoke(question)
-    print(response['output'])
     st.chat_message("user").markdown(question)
-    st.chat_message("assistant").markdown(response['output'])
+    st.chat_message("assistant").markdown(response['output'])