NLP_Final/app_og.py at main · mhorns/NLP_Final · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template


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(raw_text):
    text_splitter = CharacterTextSplitter(separator="\n",chunk_size=1000,chunk_overlap=200,length_function=len)
    chunks = text_splitter.split_text(raw_text)
    return chunks

def get_vector_store(text_chunks):
    embeddings = OpenAIEmbeddings()
    vector_store = FAISS.from_texts(text_chunks,embeddings)
    return vector_store

def get_conversation_chain(vector_store):
    llm = ChatOpenAI()
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(llm = llm, retriever = vector_store.as_retriever(), memory = memory)
    return conversation_chain

def handle_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)


def main():
    load_dotenv()
    st.set_page_config(page_title="AI for Financial Advice",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("AI for Financial Advice :books:")
    user_question = st.text_input("Ask a question:")
    if user_question:
        handle_user_input(user_question)


    with st.sidebar:
        st.subheader("Your Documents")
        pdf_docs = st.file_uploader("Upload your pdfs here and click 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
                vector_store = get_vector_store(text_chunks)

                # Create conversation chain
                st.session_state.conversation = get_conversation_chain(vector_store)


if __name__ == '__main__' :
    main()