app.py

import os
import sys
import subprocess
from dotenv import load_dotenv
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS

# Install missing dependencies
try:
    from InstructorEmbedding import INSTRUCTOR
except ImportError:
    subprocess.check_call([sys.executable, "-m", "pip", "install", "InstructorEmbedding==1.0.1", "sentence-transformers==2.2.2"])
    os.environ["INSTRUCTOR_EMBEDDING_SKIP_CHECK"] = "1"  # Bypass version checks
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_community.llms import HuggingFaceHub

# Load custom HTML templates
css = """
<style>
@import url('https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap');

.chat-message {
    font-family: 'Source Sans Pro', sans-serif; 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;
}
</style>
"""

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>
"""

# Load the Hugging Face API token from environment variables
load_dotenv()
hf_token = os.getenv("HUGGINGFACE_API_TOKEN")

if hf_token is None:
    raise ValueError("Hugging Face API Token not found. Please make sure it's stored as a secret in Hugging Face.")

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=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

def get_vectorstore(text_chunks):
    # Load HuggingFace token from environment variables
    huggingface_token = os.getenv("HUGGINGFACE_API_TOKEN")

    # Use the token properly
    embeddings = HuggingFaceInstructEmbeddings(
        model_name="hkunlp/instructor-large",
        model_kwargs={
            "trust_remote_code": True,
            "use_auth_token": huggingface_token
        }
    )

    # Create FAISS vectorstore
    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="Chat with multiple PDFs", page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    st.header("Chat with multiple PDFs :books:")

    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

    user_question = st.text_input("Ask a question about your documents:")

    if user_question and st.session_state.conversation:
        handle_userinput(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)

        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)
                st.success("Documents processed! You can now chat.")

if __name__ == "__main__":
    main()