🌌 NoteSense

An Advanced AI-Powered NLP Document Analyzer & Summarization Matrix

---

📖 Overview

NoteSense is an intelligent, modern, and highly advanced web application designed to leverage Natural Language Processing (NLP) for instantaneous document digestion. It effortlessly processes large volumes of raw text, .txt, and .pdf files to generate concise summaries, answer specific questions, and extract critical intelligence.

Built for students, researchers, and professionals, NoteSense combines a powerful Hugging Face backend with an immersive, 3D CyberBento frontend interface.

---

✨ Core Features

• Immersive 3D UI: A sleek, dark-mode glassmorphism interface featuring a reactive Three.js particle matrix background and Vanilla-tilt 3D hover cards.
• Advanced Text Chunking: Intelligently bypasses standard token limits by chunking massive documents, summarizing individual segments via t5-small, and seamlessly stitching them back together.
• Smart Entity Categorization (NER): Utilizes spaCy to contextually identify and categorize data into Persons, Organizations, and Locations.
• Document Query Matrix (Q&A): Ask direct questions about your uploaded document! Powered by a distilled transformer (distilbert-squad), the AI scans your text to provide accurate answers on demand.
• VADER Sentiment Analysis: Lightning-fast tone detection that instantly calculates whether the uploaded data leans Positive, Negative, or Neutral.
• Matrix Log Export: Generate and download a perfectly formatted PDF report containing your generated summary, sentiment analysis, and vital entities in one click.
• Privacy-First: All files are processed locally and wiped instantly from the server upon completion to guarantee maximum confidentiality.

---

📂 Project Structure

NoteSense/
├── app.py                      # Main Flask application and API routing
├── requirements.txt            # Python dependencies
├── LICENSE                     # MIT License
├── README.md                   # Project documentation
├── Dockerfile                  # Container deployment instructions
├── render.yaml                 # Infrastructure as Code for Render.com
│
├── ai_engine/                  # NLP Core Services
│   ├── __init__.py
│   └── analyzer.py             # Summarization, NER, Q&A, Sentiment logic
│
├── static/                     # Static assets (CSS, JS)
│   ├── css/
│   │   └── style.css           # 3D styling, animations, and CyberBento grid
│   └── js/
│       └── main.js             # API integrations, Three.js logic, UI state
│
├── templates/                  # HTML templates
│   └── index.html              # Main frontend UI structure
│
└── uploads/                    # Temporary storage for document processing

---

🚀 Getting Started

Follow these instructions to set up and run NoteSense on your local machine.

Prerequisites

• Python 3.8 or higher installed.
• Git installed on your system.

1. Clone the Repository

git clone https://github.com/abhranilsingharoy-cloud/NoteSense.git
cd NoteSense

2. Environment Setup

It is highly recommended to use a virtual environment to manage dependencies securely.

python -m venv venv

# Activate on Windows:
venv\Scripts\activate

# Activate on macOS/Linux:
source venv/bin/activate

3. Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

4. Download AI Datasets

NoteSense requires specific language models for NER and Sentiment Analysis. Download them using the following commands:

python -m spacy download en_core_web_sm
python -c "import nltk; nltk.download('vader_lexicon')"

5. Launch the Server

Start the Flask application:

python app.py

Open your web browser and navigate to: http://127.0.0.1:5000/

Note: The first time you summarize text or ask a question, the Hugging Face transformer models (t5-small, distilbert-squad) will be automatically downloaded to your machine.

---

🛠️ Technology Stack

Domain	Technologies
Backend Framework	Python, Flask
NLP & AI Engine	Hugging Face Transformers (t5-small, distilbert-squad)
Entity & Sentiment	spaCy (en_core_web_sm), NLTK (VADER)
Data Processing	PyPDF2 (Parsing), fpdf2 (PDF Generation)
Frontend UI	HTML5, Vanilla CSS3 (Variables, Grid), Vanilla JavaScript
Frontend 3D Visuals	Three.js, Vanilla-tilt.js

---

🤝 Contributing

Contributions, issues, and feature requests are welcome! If you have suggestions for improving NoteSense, feel free to check the issues page.

---

📝 License

This project is open-source and distributed under the terms of the MIT License.

---

Developed with passion by Abhranil Singha Roy