variables added in environment

app.py

@@ -10,8 +10,12 @@ from langchain.embeddings.openai import OpenAIEmbeddings
 
 embeddings = OpenAIEmbeddings()
 
-PINECONE_API_KEY = '6af52b8a-a3df-4189-899b-b21163027bb8'
-PINECONE_API_ENV = 'asia-southeast1-gcp'
+# PINECONE_API_KEY = '6af52b8a-a3df-4189-899b-b21163027bb8'
+# PINECONE_API_ENV = 'asia-southeast1-gcp'
+
+PINECONE_API_KEY = os.environ["PINECONE_API_KEY"]
+PINECONE_API_ENV = os.environ["PINECONE_API_ENV"]
+PINECONE_INDEX = os.environ["PINECONE_INDEX"]
 
 # initialize pinecone
 pinecone.init(
@@ -19,10 +23,10 @@ pinecone.init(
     environment=PINECONE_API_ENV  # next to api key in console
 )
 
-index_name = "twimbit-answer"
+index_name = PINECONE_INDEX
 vectorstore = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings)
 
-api_key = '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'
+api_key = os.environ["OPENAI_API_KEY"]
 
 
 class ChatWrapper:
@@ -49,7 +53,7 @@ class ChatWrapper:
                 os.environ["OPENAI_API_KEY"] = ""
 
             import openai
-            openai.api_key = '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'
+            openai.api_key = os.environ["OPENAI_API_KEY"]
             # Run chain and append input.
             output = chain({"question": inp, "chat_history": history})["answer"]
             history.append((inp, output))