Update app.py

app.py

@@ -1,17 +1,16 @@
+import os
 import streamlit as st
 from dotenv import load_dotenv
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub
-import os
 
-def get_pdf_text(pdf_docs):
+def extract_text_from_pdfs(pdf_docs):
     text = ""
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
@@ -19,91 +18,47 @@ def get_pdf_text(pdf_docs):
             text += page.extract_text()
     return text
 
+def split_text_into_chunks(text):
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
+    return text_splitter.split_text(text)
 
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vectorstore(text_chunks):
-    #embeddings = OpenAIEmbeddings()
-    
+def create_vector_store_from_text_chunks(text_chunks):
     key = os.getenv('OPENAI_KEY')
     embeddings = OpenAIEmbeddings(openai_api_key=key)
-    
-    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
+    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
 
-
-def get_conversation_chain(vectorstore):
+def create_conversation_chain(vectorstore):
     llm = ChatOpenAI()
-    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
-
-    memory = ConversationBufferMemory(
-        memory_key='chat_history', return_messages=True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=vectorstore.as_retriever(),
-        memory=memory
-    )
-    return conversation_chain
-
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
 
-def handle_userinput(user_question):
+def process_user_input(user_question):
     response = st.session_state.conversation({'question': user_question})
     st.session_state.chat_history = response['chat_history']
 
     for i, message in enumerate(st.session_state.chat_history):
-        if i % 2 == 0:
-            st.write(user_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-        else:
-            st.write(bot_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-
+        template = user_template if i % 2 == 0 else bot_template
+        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
 
 def main():
     load_dotenv()
-    st.set_page_config(page_title="Chat with multiple PDFs",
-                       page_icon=":books:")
+    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
     st.write(css, unsafe_allow_html=True)
 
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = None
-
     st.header("Chat with multiple PDFs :books:")
     user_question = st.text_input("Ask a question about your documents:")
     if user_question:
-        handle_userinput(user_question)
+        process_user_input(user_question)
 
     with st.sidebar:
         st.subheader("Your documents")
-        pdf_docs = st.file_uploader(
-            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
         if st.button("Process"):
             with st.spinner("Processing"):
-                # get pdf text
-                raw_text = get_pdf_text(pdf_docs)
-
-                # get the text chunks
-                text_chunks = get_text_chunks(raw_text)
-
-                # create vector store
-                vectorstore = get_vectorstore(text_chunks)
-
-                # create conversation chain
-                st.session_state.conversation = get_conversation_chain(
-                    vectorstore)
-
+                raw_text = extract_text_from_pdfs(pdf_docs)
+                text_chunks = split_text_into_chunks(raw_text)
+                vectorstore = create_vector_store_from_text_chunks(text_chunks)
+                st.session_state.conversation = create_conversation_chain(vectorstore)
 
 if __name__ == '__main__':
-    main()
+    main()