app.py

import os
import fitz
from docx import Document
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import pickle
import gradio as gr
from typing import List
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from nltk.tokenize import sent_tokenize  # Import for sentence segmentation
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_path):
    text = ""
    try:
        doc = fitz.open(pdf_path)
        for page_num in range(len(doc)):
            page = doc.load_page(page_num)
            text += page.get_text()
    except Exception as e:
        print(f"Error extracting text from PDF: {e}")
    return text

# Function to extract text from a Word document 
def extract_text_from_docx(docx_path):
    """Extracts text from a Word document."""
    text = ""
    try:
        doc = Document(docx_path)
        text = "\n".join([para.text for para in doc.paragraphs])
    except Exception as e:
        print(f"Error extracting text from DOCX: {e}")
    return text


# Initialize the embedding model (same as before)
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')


# Hugging Face API token (same as before)
api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
if not api_token:
    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set")


# Define RAG models (replace with your chosen models)
generator_model_name = "facebook/bart-base"
retriever_model_name = "facebook/bart-base"  # Can be the same as generator

generator = AutoModelForSeq2SeqLM.from_pretrained(generator_model_name)
generator_tokenizer = AutoTokenizer.from_pretrained(generator_model_name)

retriever = AutoModelForSeq2SeqLM.from_pretrained(retriever_model_name)
retriever_tokenizer = AutoTokenizer.from_pretrained(retriever_model_name)


# Load or create FAISS index
index_path = "faiss_index.pkl"
document_texts_path = "document_texts.pkl"
document_texts = []
if os.path.exists(index_path) and os.path.exists(document_texts_path):
    try:
        with open(index_path, "rb") as f:
            index = pickle.load(f)
            print("Loaded FAISS index from faiss_index.pkl")
        with open(document_texts_path, "rb") as f:
            document_texts = pickle.load(f)
            print("Loaded document texts from document_texts.pkl")
    except Exception as e:
        print(f"Error loading FAISS index or document texts: {e}")
else:
    # Create a new FAISS index if it doesn't exist
    index = faiss.IndexFlatL2(embedding_model.get_sentence_embedding_dimension())
    with open(index_path, "wb") as f:
        pickle.dump(index, f)
        print("Created new FAISS index and saved to faiss_index.pkl")


def preprocess_text(text):
    sentences = sent_tokenize(text)
    return sentences


def upload_files(files):
    global index, document_texts
    try:
        for file_path in files:
            if file_path.endswith('.pdf'):
                text = extract_text_from_pdf(file_path)
            elif file_path.endswith('.docx'):
                text = extract_text_from_docx(file_path)
            else:
                return "Unsupported file format"

            # Preprocess text (call the new function)
            sentences = preprocess_text(text)

            # Encode sentences and add to FAISS index
            embeddings = embedding_model.encode(sentences)
            index.add(np.array(embeddings))

        # Save the updated index and documents (same as before)
        # ...
        return "Files processed successfully"
    except Exception as e:
        print(f"Error processing files: {e}")