Update app.py

app.py

@@ -2,7 +2,7 @@
 
 import streamlit as st
 from dotenv import load_dotenv
-import pickle
+#import pickle
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
@@ -11,7 +11,10 @@ from langchain.llms import OpenAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
 import os
-load_dotenv()
+#load_dotenv()
+
+
+api_key = os.getenv("OpenAI_API_KEY")
 
 ## Reading the PDF
 
@@ -41,7 +44,7 @@ if pdf is not None:
 ## Create Embeddings of each chunk of data and store them in the Vector DB
     
     store_name = pdf.name[:-4] # extract the pdf name
-    embeddings = OpenAIEmbeddings(openai_api_key = os.environ["OpenAI_API_KEY"]) # using OpenAI to create embeddings
+    embeddings = OpenAIEmbeddings(openai_api_key = api_key) # using OpenAI to create embeddings
 
     if os.path.exists(f"{store_name}"): # if already the vector db is present then load it
         #path = f"{store_name}\index.pkl"
@@ -63,7 +66,7 @@ if pdf is not None:
     if query:
         docs = VectorStore.similarity_search(query=query, k=3)
 
-        llm = OpenAI()
+        llm = OpenAI(openai_api_key = api_key)
         chain = load_qa_chain(llm=llm, chain_type="stuff")
         with get_openai_callback() as cb:
             response = chain.run(input_documents=docs, question=query)