Update app.py

app.py

@@ -1,17 +1,16 @@
 import streamlit as st
+# from streamlit_extras.add_vertical_space import add_vertical_space
 import os
-# import pickle
+import pickle
 from PyPDF2 import PdfReader
-# from langchain import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
-
-from langchain.vectorstores import Pinecone
-import pinecone
+from langchain.vectorstores import FAISS
 from langchain.llms import OpenAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
 
+
 # Sidebar contents
 with st.sidebar:
     st.title('🤗💬 LLM Chat App')
@@ -51,28 +50,34 @@ def main():
         store_name = pdf.name[:-4]
         st.write(f'{store_name}')
 
-        # if os.path.exists(f"{store_name}.pkl"):
-        #     with open(f"{store_name}.pkl", "rb") as f:
-        #         VectorStore = pickle.load(f)
-        #     st.write('Embeddings Loaded from the Disk')
-        # else:
-        #     st.write('Embeddings calculate to the Pinecone')
-        #     embeddings = OpenAIEmbeddings()
-        #     VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-        #     with open(f"{store_name}.pkl", "wb") as f:
-        #         pickle.dump(VectorStore, f)
-
-        PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '894d5f1f-df46-4b01-8407-d9977eaee2eb')
-        PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV',
-                                          'asia-southeast1-gcp-free')  # You may need to switch with your env
-        embeddings = OpenAIEmbeddings()
-        # initialize pinecone
-        pinecone.init(
-            api_key=PINECONE_API_KEY,  # find at app.pinecone.io
-            environment=PINECONE_API_ENV  # next to api key in console
-        )
-        index_name = "indexer"  # put in the name of your pinecone index here
-        VectorStore = Pinecone.from_texts(chunks, embeddings, index_name=index_name)
+        if os.path.exists(f"{store_name}.pkl"):
+            with open(f"{store_name}.pkl", "rb") as f:
+                VectorStore = pickle.load(f)
+            st.write('Embeddings Loaded from the Disk')
+        else:
+            st.write('Embeddings calculate to the Pinecone')
+            embeddings = OpenAIEmbeddings()
+            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+            with open(f"{store_name}.pkl", "wb") as f:
+                pickle.dump(VectorStore, f)
+                st.download_button(
+                    label="Download data as pkl",
+                    data=f,
+                    file_name='{store_name}.pkl',
+                    mime='text/text',
+                )
+
+        # PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '894d5f1f-df46-4b01-8407-d9977eaee2eb')
+        # PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV',
+        #                                   'asia-southeast1-gcp-free')  # You may need to switch with your env
+        # embeddings = OpenAIEmbeddings()
+        # # initialize pinecone
+        # pinecone.init(
+        #     api_key=PINECONE_API_KEY,  # find at app.pinecone.io
+        #     environment=PINECONE_API_ENV  # next to api key in console
+        # )
+        # index_name = "indexer"  # put in the name of your pinecone index here
+        # VectorStore = Pinecone.from_texts(chunks, embeddings, index_name=index_name)
 
         # Accept user questions/query
         query = st.text_input("Ask questions about your PDF file:")