Update app.py

app.py

@@ -1,66 +1,103 @@
 import streamlit as st
 from dotenv import load_dotenv
-from pypdf import PdfReader
-from langchain.text_splitter import CharacterTextSplitter as CSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
 from langchain.vectorstores import FAISS
-from langchain.memory import ConversationalBufferMemory()
+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
 
-def get_pdf_text(docs):
+def get_pdf_text(pdf_docs):
     text = ""
-    for pdf in docs:
-        reader = PdfReader(pdf)
-        for page in reader.pages:
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
             text += page.extract_text()
+    return text
+
 
 def get_text_chunks(text):
-    splitter = CSplitter(
+    text_splitter = CharacterTextSplitter(
         separator="\n",
         chunk_size=1000,
         chunk_overlap=200,
         length_function=len
     )
-    chunks = splitter.split_text(text)
+    chunks = text_splitter.split_text(text)
     return chunks
 
-def get_embeddings():
-    model_name = "sentence-transformers/all-mpnet-base-v2"
-    model_kwargs = {'device': 'cpu'}
-    encode_kwargs = {'normalize_embeddings': False}
-    
-    return HuggingFaceEmbeddings( 
-        model_name=model_name,
-        model_kwargs=model_kwargs,
-        encode_kwargs=encode_kwargs
+
+def get_vectorstore(text_chunks):
+    # embeddings = OpenAIEmbeddings()
+    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 get_vectorstore(chunks):
-    hf = get_embeddings()
-    vectorstore = FAISS.from_texts(text=chunks, embedding=hf)
-    conversation = get_conversation_chain(vectorstore)
+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():
     load_dotenv()
-    st.set_page_config(page_title="IDSR Chat", page_icon=":books:")
-    st.header("IntelSurv Chat")
-    st.text_input("Ask a question")
+    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)
 
     with st.sidebar:
-        st.subheader("TG for IDSR Booklet")
-        docs= st.file_uploader("Upload booklet here", accept_multiple_files=True)
-        
+        st.subheader("Your documents")
+        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(docs)
-
-                chunks = get_text_chunks()
-        st.write(chunks)
-
+                # 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)
 
 
 if __name__ == '__main__':
-    main()
+    main()