iNotes 1.2.0
iNotes is a simple Python package that uses AI to generate  clear, concise notes from just topics or summarize long documents. It's designed to assist students, professionals, and researchers in make short notes easily.
License: MIT

# iNotes 🧠✍️

iNotes is a simple yet powerful Python package that uses AI to generate clear, structured notes and summaries from just a topic or document. Whether you're a student, researcher, or professional, iNotes helps you create high-quality notes in various formats — effortlessly.

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## 🚀 Features

- 📝 **Generate detailed or concise notes** from a given topic
- 📄 **Summarize .pdf, .txt, or .docx documents** and save summaries in the same format
- 📤 **Export notes** in multiple formats: .pdf, .txt, .docx, and .md
- 🧠 **Supports multiple advanced AI models**
- 🔍 **Highlights key points** with structured headings and subheadings
- ⚙️ **Customizable note length** (short or long)
- 💡 **Simple, user-friendly Python interface**

---

## 🔧 Supported AI Models

- deepseek/deepseek-r1
- deepseek/deepseek-chat
- neversleep/llama-3-lumimaid-8b:extended
- anthropic/claude-3-7-sonnet-20250219
- sao10k/l3-euryale-70b
- openai/gpt-4o-mini
- gryphe/mythomax-l2-13b
- google/gemini-pro-1.5
- x-ai/grok-2
- nvidia/llama-3.1-nemotron-70b-instruct

⚠️ **Note**: Some models are used through unofficial APIs and may behave unpredictably.

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## 📦 Installation

To install the iNotes package, use pip:

```bash
pip install iNotes
```
---

## Notes Generator

Here's a basic example of how to use the package:

```python
from iNotes import generate_notes

#topic for notes

topic = "Machine Learning"

# Generate notes 
# filepath: where to save the generated notes
# model: custom model for the AI model
# short_notes: if True, generates short notes
# format: format of notes to be generated

generate_notes(topic, filepath="output_notes",model = "deepseek/deepseek-r1", short_notes = False, format = "pdf")
```

---

## 📋 Output Example

```
.pdf file :

   ** MACHINE LEARNING NOTES **
 ** INTRODUCTION TO MACHINE LEARNING **
 ***What is Machine Learning?***
 *   Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on building
 systems which can learn from data.
 *   The core idea is to enable computers to improve their performance on a specific task
 through experience (data), without being explicitly programmed for every possible scenario.
 *   Instead of writing rigid rules for every situation, ML algorithms learn patterns,
 relationships, and structures within the data.
 *   This allows them to make predictions, classifications, or decisions on new, unseen data.
 *   It's about learning from examples and adapting behavior based on new information


    and many more...



    short notes:

    "Okay, here are your notes on Machine Learning:

Machine Learning

What is ML?
*   A type of Artificial Intelligence (AI).
*   Enables computers to learn from data without being explicitly programmed.
*   Identifies patterns and makes predictions or decisions based on data.

Types of Machine Learning
Supervised Learning
*   Learns from labeled data (input-output pairs).
*   Goal: Predict output for new, unseen inputs.
*   Examples: Classification (spam detection), Regression (house price prediction).

 and much more....
```

---

## Document Summarizer

Here's a basic example of how to use the package:

```python
from iNotes import summarize_notes

#document for notes

document = "document"

# Generate notes 
# filepath: path of document to be summarized
# model: custom model for the AI model
# format: format of document to be summarized and saves in same format
# length: length of summary

summarize_notes(document, filepath="document",model = "deepseek/deepseek-r1", format = "pdf", length = "1000")
```

---
## Warning 
Older versions of iNotes may have some error in them. Please use the latest version of iNotes.

## 📜 License
This project is licensed under the MIT License. You're free to use, modify, and distribute it with proper attribution.

## 🌐 Project Links

Documentation and additional resources coming soon!