import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from langchain.llms.huggingface_hub import HuggingFaceHub

css = '''
<style>
.chat-message {
    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
}
.chat-message.user {
    background-color: #2b313e
}
.chat-message.bot {
    background-color: #475063
}
.chat-message .avatar {
  width: 20%;
}
.chat-message .avatar img {
  max-width: 78px;
  max-height: 78px;
  border-radius: 50%;
  object-fit: cover;
}
.chat-message .message {
  width: 80%;
  padding: 0 1.5rem;
  color: #fff;
}
'''

bot_template = '''
<div class="chat-message bot">
    <div class="avatar">
        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
    </div>
    <div class="message">{{MSG}}</div>
</div>
'''

user_template = '''
<div class="chat-message user">
    <div class="avatar">
        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
    </div>    
    <div class="message">{{MSG}}</div>
</div>
'''

st.set_page_config(
    page_icon=':balloon:',
    page_title= 'dump',
    layout='wide'
)
st.title(body='*Streamlit*')

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 get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator='\n',
        chunk_size = 500,
        chunk_overlap = 200,
        length_function = len
    )
    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":256}
    )
    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():
    load_dotenv()
    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("Your documents")
        pdf_docs = st.file_uploader(
            label="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)
        
if __name__ == '__main__':
    main()