backend.py

# Import Dependencies (dependencies.py)
import streamlit as st
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import PyPDFLoader, OnlinePDFLoader
from transformers import pipeline
import re
import sqlite3
from sqlite3 import Error
from langchain.text_splitter import RecursiveCharacterTextSplitter
import requests
import pandas as pd
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from io import BytesIO
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
from google.oauth2 import service_account
import tempfile
import os
from langchain.llms import OpenAI  # Import the OpenAI class
from langchain.chat_models import ChatOpenAI  # Import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.agents import create_openai_tools_agent, AgentExecutor, Tool
from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
)  # Import necessary classes


# SQLite Database Functions (database.py)
def create_connection(db_file):
    try:
        conn = sqlite3.connect(db_file)
        return conn
    except Error as e:
        st.error(f"Error: {e}")
    return None


def create_tables(conn):
    try:
        sql_create_documents_table = """
        CREATE TABLE IF NOT EXISTS documents (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            content TEXT NOT NULL,
            upload_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
        );
        """

        sql_create_queries_table = """
        CREATE TABLE IF NOT EXISTS queries (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            query TEXT NOT NULL,
            response TEXT NOT NULL,
            document_id INTEGER,
            query_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            FOREIGN KEY (document_id) REFERENCES documents (id)
        );
        """

        sql_create_annotations_table = """
        CREATE TABLE IF NOT EXISTS annotations (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            document_id INTEGER NOT NULL,
            annotation TEXT NOT NULL,
            page_number INTEGER,
            annotation_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            FOREIGN KEY (document_id) REFERENCES documents (id)
        );
        """

        c = conn.cursor()
        c.execute(sql_create_documents_table)
        c.execute(sql_create_queries_table)
        c.execute(sql_create_annotations_table)
    except Error as e:
        st.error(f"Error: {e}")


# FAISS Initialization (faiss_initialization.py)
def initialize_faiss(embeddings, documents, document_names):
    try:
        vector_store = FAISS.from_texts(
            documents,
            embeddings,
            metadatas=[{"source": name} for name in document_names],
        )
        return vector_store
    except Exception as e:
        st.error(f"Error initializing FAISS: {e}")
        return None


# Document Upload & Parsing Functions (document_parsing.py)
@st.cache_data
def upload_and_parse_documents(documents):
    all_texts = []
    document_names = []
    document_pages = []
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    for doc in documents:
        try:
            if doc.name in document_names:
                st.warning(
                    f"Duplicate file name detected: {doc.name}. This file will be ignored.",
                    icon="⚠️",
                )
                continue  # Skip to the next file

            # Create a temporary file
            with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
                tmp_file.write(doc.read())
                tmp_file_path = tmp_file.name

            loader = PyPDFLoader(tmp_file_path)
            pages = loader.load()
            document_names.append(doc.name)
            page_contents = []
            for page in pages:
                chunks = text_splitter.split_text(page.page_content)
                all_texts.extend(chunks)
                page_contents.append(page.page_content)
            document_pages.append(page_contents)

            # Remove the temporary file
            os.remove(tmp_file_path)

        except Exception as e:
            st.error(f"Error parsing document {doc.name}: {e}")
    return all_texts, document_names, document_pages


@st.cache_data
def parse_pdf_from_url(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        with open("temp.pdf", "wb") as f:
            f.write(response.content)
        loader = PyPDFLoader("temp.pdf")
        pages = loader.load()
        all_texts = []
        document_name = url.split("/")[-1]
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=100
        )
        for page in pages:
            chunks = text_splitter.split_text(page.page_content)
            all_texts.extend(chunks)
        return all_texts, document_name
    except requests.exceptions.RequestException as e:
        st.error(f"Failed to download PDF from URL: {e}")
        return None, None
    except Exception as e:
        st.error(f"Error parsing PDF from URL: {e}")
        return None, None


@st.cache_data
def parse_pdf_from_google_drive(file_id):
    try:
        # Authenticate and create the drive service
        credentials = service_account.Credentials.from_service_account_info(
            st.secrets["gdrive_service_account"],
            scopes=["https://www.googleapis.com/auth/drive"],
        )
        service = build("drive", "v3", credentials=credentials)
        request = service.files().get_media(fileId=file_id)
        fh = BytesIO()
        downloader = MediaIoBaseDownload(fh, request)
        done = False
        while not done:
            status, done = downloader.next_chunk()
        fh.seek(0)
        with open("temp_drive.pdf", "wb") as f:
            f.write(fh.read())
        loader = PyPDFLoader("temp_drive.pdf")
        pages = loader.load()
        all_texts = []
        document_name = f"GoogleDrive_{file_id}.pdf"
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=100
        )
        for page in pages:
            chunks = text_splitter.split_text(page.page_content)
            all_texts.extend(chunks)
        return all_texts, document_name
    except Exception as e:
        st.error(f"Error downloading PDF from Google Drive: {e}")
        return None, None


# Embeddings for Semantic Search (embeddings.py)
@st.cache_resource
def get_embeddings_model():
    try:
        model_name = "sentence-transformers/all-MiniLM-L6-v2"
        embeddings = HuggingFaceEmbeddings(model_name=model_name)
        return embeddings
    except Exception as e:
        st.error(f"Error loading embeddings model: {e}")
        return None


# QA System Initialization (qa_system.py)


@st.cache_resource
def initialize_qa_system(vector_store):
    """Initialize QA system with proper chat handling."""
    try:
        llm = ChatOpenAI(
            temperature=0.5,
            model_name="gpt-4",
            api_key=os.environ.get("OPENAI_API_KEY")
        )

        # Create a more basic prompt template
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert consultant specializing in analyzing Request for Proposal (RFP) documents. 
            Your goal is to provide clear, accurate responses based on the provided context. 
            Start with a direct answer and organize additional details under relevant headers."""),
            ("human", "{input}")
        ])

        # Create the retriever chain
        retriever = vector_store.as_retriever(
            search_type="similarity",
            search_kwargs={"k": 3}
        )

        chain = (
            {
                "input": RunnablePassthrough()
            }
            | {"input": lambda x: x["input"], "docs": retriever}
            | {
                "input": lambda x: x["input"],
                "context": lambda x: "\n\n".join([doc.page_content for doc in x["docs"]])
            }
            | prompt
            | llm
        )

        return chain

    except Exception as e:
        st.error(f"Error initializing QA system: {e}")
        return None