PDF Search Engine

This project is a PDF search engine that utilizes Natural Language Processing (NLP) techniques to extract and rank pages based on user queries. The application analyzes the content of PDF documents and provides real-time ranking for pages relevant to the user's input.

Features

• PDF Upload: Users can upload PDF files for analysis.
• Real-Time Query Processing: The search engine provides real-time ranking of PDF pages based on user queries.
• NLP Techniques: Utilizes tokenization, stemming, lemmatization, and TF-IDF for improved search accuracy.
• Page Ranking: Returns ranked pages based on relevance to the query, allowing users to directly view content from specific pages.

Technologies Used

• Python: The main programming language for development.
• Streamlit: A framework to create the web application.
• NLTK: Natural Language Toolkit for NLP tasks.
• LangChain: For handling PDF document loading.

Requirements

Make sure you have the following libraries installed:

pip install streamlit langchain_community nltk

You also need to download the necessary NLTK resources by running:

import nltk
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/pdf-search-engine.git
   cd pdf-search-engine

2. Run the Streamlit application:

   streamlit run app.py

3. Upload a PDF document and enter your query in the provided text input. The application will display ranked pages based on the relevance of the query.

How It Works

1. Preprocessing: The application preprocesses the PDF content by tokenizing, stemming, lemmatizing, and removing stopwords to generate a word frequency dictionary for each page.

2. PageRank Algorithm: The application implements a PageRank-like algorithm that calculates TF-IDF scores for words in the query and ranks the pages accordingly

3. Results: Users can view the ranked pages and click on the page number to see the content directly.

Thank you!!