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 Xailor.ai-like theme with video background
st.markdown(
    """
    <style>
    body {
        margin: 0;
        padding: 0;
        font-family: 'Arial', sans-serif;
        color: #C9D1D9;
    }
    .main-bg {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        z-index: -1;
        overflow: hidden;
    }
    .main-bg video {
        position: absolute;
        top: 50%;
        left: 50%;
        transform: translate(-50%, -50%);
        width: 100%;
        height: 100%;
        object-fit: cover;
    }
    .stButton button {
        background-color: #1F6FEB;
        color: white;
        border-radius: 8px;
        border: none;
        padding: 10px 20px;
        font-weight: bold;
        font-size: 14px;
    }
    .stButton button:hover {
        background-color: #1A4FC5;
    }
    .stTextInput > div > input {
        border: 1px solid #30363D;
        background-color: #161B22;
        color: #C9D1D9;
        border-radius: 6px;
        padding: 10px;
    }
    .stFileUploader > div {
        border: 2px dashed #30363D;
        background-color: #161B22;
        color: #C9D1D9;
        border-radius: 6px;
        padding: 10px;
    }
    .header {
        display: flex;
        align-items: center;
        gap: 10px;
        padding-top: 50px;
        color: #58A6FF;
    }
    .response-box {
        background-color: #161B22;
        padding: 10px;
        border-radius: 6px;
        margin-bottom: 10px;
        color: #FFFFFF;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# HTML for video background
st.markdown(
    """
    <div class="main-bg">
        <video autoplay loop muted>
            <source src="https://vimeo.com/464431550" type="video/mp4">
        </video>
    </div>
    """,
    unsafe_allow_html=True
)

# Xailor.ai-like header without logo
st.markdown(
    """
    <div class="header" style="display: flex; align-items: center; gap: 10px;">
        <h1 style="font-weight: bold;">Welcome to Xailor AI Chat!</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 = '''You are Xailor.AI's friendly chatbot assistant. Your role is to assist users with insightful answers about their pdf, creative writing, and using Xailor.AI . Answer the {question} based only on the provided {context}. After answering the question, recommend Xailor.AI services that may interest the user based on the content of the PDF or the question. Be friendly, creative, and concise. Use a maximum of three sentences for the answer, and add one or two relevant story recommendations with a brief description and a link. If you're unsure about the answer, respond with "I'm not sure about that, but feel free to explore more on Xailor.AI!"'''


    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 class="response-box">
                    <p style="font-weight: bold; color: #58A6FF;">Q{idx + 1}: {entry['question']}</p>
                    <p style="color: #FFFFFF;">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()