app.py

import os
import shutil
import streamlit as st
from dotenv import load_dotenv
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.storage import LocalFileStore
from langchain.embeddings import CacheBackedEmbeddings
from langchain_groq import ChatGroq
from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from streamlit_chat import message

# Load environment variables
load_dotenv()
os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API')
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = os.getenv('LANGSMITH_API')

UPLOAD_DIR = "uploaded_files"

def cleanup_files():
    if os.path.isdir(UPLOAD_DIR):
        shutil.rmtree(UPLOAD_DIR, ignore_errors=True)
    if 'file_handle' in st.session_state:
        st.session_state.file_handle.close()


if 'cleanup_done' not in st.session_state:
    st.session_state.cleanup_done = False

if not st.session_state.cleanup_done:
    cleanup_files()

if not os.path.exists(UPLOAD_DIR):
    os.makedirs(UPLOAD_DIR, exist_ok=True)

# Custom CSS for Wattpad-like theme with background and header
st.markdown(
    """
    <style>
    body {
        background-color: #FFF7F0;
        color: #333333;
        font-family: 'Helvetica Neue', sans-serif;
        background-image: url('https://drive.google.com/uc?export=view&id=17Vg5hM0-X7fUy2BdYCFqSAQtJVDqYErU');
        background-size: cover;
        background-position: top center;
    }
    .stButton button {
        background-color: #FF5000;
        color: white;
        border-radius: 12px;
        border: none;
        padding: 10px 20px;
        font-weight: bold;
    }
    .stButton button:hover {
        background-color: #E64500;
    }
    .stTextInput > div > input {
        border: 1px solid #FF5000;
        border-radius: 10px;
        padding: 10px;
    }
    .stFileUploader > div {
        border: 2px dashed #FF5000;
        border-radius: 10px;
        padding: 10px;
    }
    .header {
        display: flex;
        align-items: center;
        gap: 10px;
        padding-top: 50px;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Wattpad-like header without logo
st.markdown(
    """
    <div class="header" style="display: flex; align-items: center; gap: 10px;">
        <h1 style="color: #FF5000; font-weight: bold;">Hi, we're here to help you.</h1>
    </div>
    """,
    unsafe_allow_html=True
)

# Spacer to push chatbot below the header
st.write("<div style='height: 100px;'></div>", unsafe_allow_html=True)

st.title("Chat with your PDF!!")

uploaded_file = st.file_uploader("Upload a file")

if uploaded_file is not None:
    file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
    file_path = os.path.abspath(file_path)

    with open(file_path, 'wb') as f:
        f.write(uploaded_file.getbuffer())
    st.write("You're Ready For a Chat with your PDF")

    docs = PyPDFLoader(file_path).load_and_split()

    embedding = HuggingFaceEmbeddings(
        model_name='BAAI/llm-embedder',
    )

    store = LocalFileStore("./cache/")
    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
        embedding, store, namespace='embeddings'
    )

    vector_base = FAISS.from_documents(
        docs,
        embedding
    )

    template = '''Act as a professional and empathetic customer support representative for Xailor.ai. Your job is to address customer inquiries related to their pdf, provide technical assistance, and deliver clear, concise, and friendly responses. Always reflect Xailor.ai's values of innovation, reliability, and customer focus. Use simple and easy-to-understand language, but tailor responses to the user's level of expertise'''
    
    prompt = ChatPromptTemplate.from_template(template)
    retriever = vector_base.as_retriever()

    llm = ChatGroq(
        model='mixtral-8x7b-32768',
        temperature=0,
    )

    if 'history' not in st.session_state:
        st.session_state.history = []

    query = st.text_input("Enter your question", placeholder="Ask something interesting...")

    if st.button("Submit!", key="submit_button"):
        if query:
            chain = (
                {'context': retriever, 'question': RunnablePassthrough()} 
                | prompt | llm | StrOutputParser()
            )
            answer = chain.invoke(query)
            st.session_state.history.append({'question': query, 'answer': answer})

    if st.session_state.history:
        st.write("### Previous Questions and Answers")
        for idx, entry in enumerate(st.session_state.history):
            st.markdown(
                f"""
                <div style="background-color: #FFFAF5; padding: 10px; border-radius: 10px; margin-bottom: 10px;">
                    <p style="font-weight: bold; color: #FF5000;">Q{idx + 1}: {entry['question']}</p>
                    <p style="color: #333333;">A{idx + 1}: {entry['answer']}</p>
                </div>
                """,
                unsafe_allow_html=True
            )

# Reset functionality
if st.button("Reset and Upload a New PDF"):
    st.session_state.clear()
    st.session_state.cleanup_done = False
    st.experimental_rerun()

if st.session_state.cleanup_done:
    cleanup_files()