app.py

import streamlit as st
from transformers import pipeline
import pdfplumber
import re
from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer, util

# Load Hugging Face Question Answering model
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")

# Load Embeddings Model for Better Context Matching
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")

# Function to Extract and Clean Text from PDF
def extract_clean_text(pdf_path):
    text = ""
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            text += page.extract_text() + "\n"

    # Remove extra spaces and newlines
    text = re.sub(r'\s+', ' ', text)  # Replace multiple spaces/newlines with a single space
    text = text.replace(" .", ".")  # Fix misplaced spaces before periods

    # Add section headers where possible
    text = re.sub(r'(?<=\n)([A-Z][a-z]+.*?):', r'\n\n## \1\n', text)  # Convert labels into headings

    return text

# Function to Split Text into Chunks
def split_text(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    chunks = text_splitter.split_text(text)
    return chunks

# Function to Find the Most Relevant Chunk Using Embeddings
def find_best_chunk(question, chunks):
    question_embedding = embedding_model.encode(question, convert_to_tensor=True)
    chunk_embeddings = [embedding_model.encode(chunk, convert_to_tensor=True) for chunk in chunks]

    # Compute similarity between question and each chunk
    similarities = [util.pytorch_cos_sim(question_embedding, chunk_emb).item() for chunk_emb in chunk_embeddings]

    # Find the most relevant chunk
    best_chunk_index = similarities.index(max(similarities))
    return chunks[best_chunk_index]

# Streamlit UI
st.title("Chat with AWS Restart PDF")

# Load and Process PDF
pdf_path = "AWS restart program information.docx.pdf"  # Change to your uploaded file
pdf_text = extract_clean_text(pdf_path)  # Extract & clean text
chunks = split_text(pdf_text)  # Split into chunks

st.write("✅ PDF Loaded Successfully!")

# User Input
question = st.text_input("Ask a question about AWS Restart program:")

if st.button("Get Answer") and question:
    relevant_chunk = find_best_chunk(question, chunks)  # Retrieve the best chunk
    response = qa_pipeline(question=question, context=relevant_chunk)  # Ask the model
    st.write("Answer:", response['answer'])