app.py

# import os
# import logging
# import streamlit as st
# import torch
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# from langchain_community.document_loaders import PDFMinerLoader
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain_community.embeddings import SentenceTransformerEmbeddings
# from langchain_community.vectorstores import Chroma
# from langchain_community.llms import HuggingFacePipeline
# from langchain.chains import RetrievalQA

# # Set up logging
# logging.basicConfig(level=logging.INFO)

# # Paths and model
# PERSIST_DIRECTORY = "db"
# UPLOAD_FOLDER = "uploaded_files"
# os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# CHECKPOINT = "MBZUAI/LaMini-T5-738M"
# tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
# base_model = AutoModelForSeq2SeqLM.from_pretrained(CHECKPOINT)
# device = 0 if torch.cuda.is_available() else -1

# def ingest_data():
#     try:
#         st.info("📚 Ingesting documents...")

#         docs = []
#         for file_name in os.listdir(UPLOAD_FOLDER):
#             if file_name.endswith(".pdf"):
#                 path = os.path.join(UPLOAD_FOLDER, file_name)
#                 loader = PDFMinerLoader(path)
#                 loaded_docs = loader.load()
#                 docs.extend(loaded_docs)

#         if not docs:
#             st.error("No valid PDFs found.")
#             return

#         splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
#         texts = splitter.split_documents(docs)

#         embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
#         db = Chroma.from_documents(texts, embeddings, persist_directory=PERSIST_DIRECTORY)
#         db.persist()
#         st.success("✅ Ingestion successful!")
#     except Exception as e:
#         logging.error(f"Ingestion error: {str(e)}")
#         st.error(f"Ingestion error: {str(e)}")

# def get_qa_chain():
#     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
#     vectordb = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings)
#     retriever = vectordb.as_retriever()

#     pipe = pipeline(
#         "text2text-generation",
#         model=base_model,
#         tokenizer=tokenizer,
#         max_length=256,
#         do_sample=True,
#         temperature=0.3,
#         top_p=0.95,
#         device=device,
#     )
#     llm = HuggingFacePipeline(pipeline=pipe)

#     qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
#     return qa_chain

# def main():
#     st.set_page_config(page_title="CA Audit QA Chatbot", layout="wide")
#     st.title("📄 CA Audit QA Assistant")

#     with st.sidebar:
#         st.header("📤 Upload Audit PDFs")
#         uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")

#         if uploaded_file is not None:
#             file_path = os.path.join(UPLOAD_FOLDER, uploaded_file.name)
#             with open(file_path, "wb") as f:
#                 f.write(uploaded_file.getbuffer())
#             st.success(f"{uploaded_file.name} uploaded.")
#             ingest_data()

#     query = st.text_input("❓ Ask an audit-related question:")
#     if st.button("🔍 Get Answer") and query:
#         st.info("Generating answer...")
#         qa_chain = get_qa_chain()
#         prompt = f"""
#         You are an AI assistant helping Chartered Accountants (CAs) in auditing.
#         Provide accurate, concise answers based on the uploaded documents.
#         Question: {query}
#         """
#         result = qa_chain({"query": prompt})
#         st.success("✅ Answer:")
#         st.write(result["result"])

# if __name__ == "__main__":
#     main()

import os
import logging
import math
import streamlit as st
import fitz  # PyMuPDF
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain_community.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA

# Set up logging
logging.basicConfig(level=logging.INFO)

# Define global variables
device = 'cpu'
persist_directory = "db"
uploaded_files_dir = "uploaded_files"

# Streamlit app configuration
st.set_page_config(page_title="Audit Assistant", layout="wide")
st.title("Audit Assistant")

# Load the model
checkpoint = "MBZUAI/LaMini-T5-738M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
base_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

# Helper Functions

def extract_text_from_pdf(file_path):
    """Extract text from a PDF using PyMuPDF (fitz)."""
    try:
        doc = fitz.open(file_path)
        text = ""
        for page_num in range(doc.page_count):
            page = doc.load_page(page_num)
            text += page.get_text("text")
        return text
    except Exception as e:
        logging.error(f"Error reading PDF {file_path}: {e}")
        return None

def data_ingestion():
    """Function to load PDFs and create embeddings with improved error handling and efficiency."""
    try:
        logging.info("Starting data ingestion")

        if not os.path.exists(uploaded_files_dir):
            os.makedirs(uploaded_files_dir)

        documents = []  
        for filename in os.listdir(uploaded_files_dir):
            if filename.endswith(".pdf"):
                file_path = os.path.join(uploaded_files_dir, filename)
                logging.info(f"Processing file: {file_path}")
                
                try:
                    loader = PDFMinerLoader(file_path)
                    loaded_docs = loader.load()
                    if not loaded_docs:
                        logging.warning(f"Skipping file with missing or invalid metadata: {file_path}")
                        continue
                    
                    for doc in loaded_docs:
                        if hasattr(doc, 'page_content') and len(doc.page_content.strip()) > 0:
                            documents.append(doc)
                        else:
                            logging.warning(f"Skipping invalid document structure in {file_path}")
                except ValueError as e:
                    logging.error(f"Skipping {file_path}: {str(e)}")
                    continue

        if not documents:
            logging.error("No valid documents found to process.")
            return

        logging.info(f"Total valid documents: {len(documents)}")

        # Proceed with splitting and embedding documents
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
        texts = text_splitter.split_documents(documents)

        logging.info(f"Total text chunks created: {len(texts)}")
        
        if not texts:
            logging.error("No valid text chunks to create embeddings.")
            return

        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
        
        # Proceed to split and embed the documents
        MAX_BATCH_SIZE = 5461  
        total_batches = math.ceil(len(texts) / MAX_BATCH_SIZE)
        
        logging.info(f"Processing {len(texts)} text chunks in {total_batches} batches...")

        db = None
        for i in range(total_batches):
            batch_start = i * MAX_BATCH_SIZE
            batch_end = min((i + 1) * MAX_BATCH_SIZE, len(texts))
            text_batch = texts[batch_start:batch_end]
            
            logging.info(f"Processing batch {i + 1}/{total_batches}, size: {len(text_batch)}")

            if db is None:
                db = Chroma.from_documents(text_batch, embeddings, persist_directory=persist_directory)
            else:
                db.add_documents(text_batch)

        db.persist()
        logging.info("Data ingestion completed successfully")
        
    except Exception as e:
        logging.error(f"Error during data ingestion: {str(e)}")
        raise

def llm_pipeline():
    """Set up the language model pipeline."""
    logging.info("Setting up LLM pipeline")
    pipe = pipeline(
        'text2text-generation',
        model=base_model,
        tokenizer=tokenizer,
        max_length=256,
        do_sample=True,
        temperature=0.3,
        top_p=0.95,
        device=device
    )
    local_llm = HuggingFacePipeline(pipeline=pipe)
    logging.info("LLM pipeline setup complete")
    return local_llm

def qa_llm():
    """Set up the question-answering chain."""
    logging.info("Setting up QA model")
    llm = llm_pipeline()
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
    retriever = db.as_retriever()  # Set up the retriever for the vector store
    qa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    logging.info("QA model setup complete")
    return qa

def process_answer(user_question):
    """Generate an answer to the user’s question."""
    try:
        logging.info("Processing user question")
        qa = qa_llm() 

        tailored_prompt = f"""
        You are an expert chatbot designed to assist Chartered Accountants (CAs) in the field of audits. 
        Your goal is to provide accurate and comprehensive answers to any questions related to audit policies, procedures, 
        and accounting standards based on the provided PDF documents. 
        Please respond effectively and refer to the relevant standards and policies whenever applicable.

        User question: {user_question}
        """

        generated_text = qa({"query": tailored_prompt})
        answer = generated_text['result']

        if "not provide" in answer or "no information" in answer:
            return "The document does not provide sufficient information to answer your question."

        logging.info("Answer generated successfully")
        return answer

    except Exception as e:
        logging.error(f"Error during answer generation: {str(e)}")
        return "Error processing the question."

# Streamlit UI Setup
st.sidebar.header("File Upload")
uploaded_files = st.sidebar.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)

if uploaded_files:
    # Save uploaded files
    if not os.path.exists(uploaded_files_dir):
        os.makedirs(uploaded_files_dir)

    for uploaded_file in uploaded_files:
        file_path = os.path.join(uploaded_files_dir, uploaded_file.name)
        with open(file_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
    
    st.sidebar.success(f"Uploaded {len(uploaded_files)} file(s) successfully!")

    # Run data ingestion when files are uploaded
    data_ingestion()

    # Display UI for Q&A
    st.header("Ask a Question")
    user_question = st.text_input("Enter your question here:")

    if user_question:
        answer = process_answer(user_question)
        st.write(answer)

else:
    st.sidebar.info("Upload PDF files to get started!")