Update app.py

app.py

@@ -1,55 +1,96 @@
-# from dotenv import load_dotenv
 import streamlit as st
+from dotenv import load_dotenv
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
 from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-from langchain.callbacks import get_openai_callback
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
 
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
 
-def main():
-    # load_dotenv()
-    st.set_page_config(page_title="Ask your PDF")
-    st.header("Ask your PDF 💬")
-    
-    # upload file
-    pdf = st.file_uploader("Upload your PDF", type="pdf")
-    
-    # extract the text
-    if pdf is not None:
-      pdf_reader = PdfReader(pdf)
-      text = ""
-      for page in pdf_reader.pages:
-        text += page.extract_text()
-        
-      # split into chunks
-      text_splitter = CharacterTextSplitter(
+
+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)
-      
-      # create embeddings
-      embeddings = OpenAIEmbeddings()
-      knowledge_base = FAISS.from_texts(chunks, embeddings)
-      
-      # show user input
-      user_question = st.text_input("Ask a question about your PDF:")
-      if user_question:
-        docs = knowledge_base.similarity_search(user_question)
-        
-        llm = OpenAI()
-        chain = load_qa_chain(llm, chain_type="stuff")
-        with get_openai_callback() as cb:
-          response = chain.run(input_documents=docs, question=user_question)
-          print(cb)
-           
-        st.write(response)
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vectorstore(text_chunks):
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    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
+
+
+def handle_userinput(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)
+
+
+def main():
     
+    st.set_page_config(page_title="Get Questions Related To Your PDF Answered",
+                       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("Get Questions Related To Your PDF Answered :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Your PDFs")
+        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"):
+
+                raw_text = get_pdf_text(pdf_docs)
+
+                text_chunks = get_text_chunks(raw_text)
+                vectorstore = get_vectorstore(text_chunks)
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
+
 
 if __name__ == '__main__':
     main()