Update app.py

app.py

@@ -21,14 +21,14 @@ embedding_function = HuggingFaceBgeEmbeddings(
 )
 
 # Set the OpenAI API key
-openai.api_key = os.getenv("OPENAI_API_KEY")
+openai.api_key = os.getenv("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")
 
 # Load the FAISS index using LangChain's FAISS implementation
 db = FAISS.load_local("Faiss", embedding_function, allow_dangerous_deserialization=True)
 parser = StrOutputParser()
 
 # Load your data (e.g., a DataFrame)
-df = pd.read_pickle('df_news.pkl')
+df = pd.read_pickle('df_news (1).pkl')
 
 # Search function to retrieve relevant documents
 def search(query):