ipytool 1.0.0

A lightweight Jupyter-like notebook interface for Python with local code execution

🎯 IPyTool v0.3 - Lightweight Jupyter Alternative

A lightweight, browser-based Python notebook tool that looks and works just like Jupyter Notebook. Runs in your local Python environment - no Pyodide, no PyScript.

📦 Installation

Install via pip:

pip install ipytool

🚀 Quick Start

Just run:

ipytool

The notebook interface will automatically open in your default browser at http://localhost:5000.

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✨ Key Features

🆕 Version 0.3 Updates

• 📝 Markdown Cells - Full rich text documentation support
• 🎨 Beautiful Rendering - Headings, lists, tables, code blocks
• ✏️ Double-click Edit - Intuitive markdown editing
• 🔄 Cell Type Toggle - Easy switch between Code/Markdown

Version 0.2 Features

• 🔤 Intelligent Autocomplete - VSCode-style code suggestions (Ctrl+Space)
• ⬆️⬇️ Cell Reordering - Move cells up/down with arrow buttons
• ➕ Flexible Cell Addition - Insert cells at any position
• 📊 Smart DataFrame Display - Automatic HTML table rendering
• 📦 Package Installation - !pip install support with progress
• 💬 Interactive Input - Modal-based input() function

🎨 Authentic Jupyter Interface

• Exact Jupyter Notebook design aur color scheme
• Professional typography aur spacing
• In [1]: / Out[1]: execution counters
• Hover-based cell toolbars
• Smooth animations aur transitions

🚀 Powerful Code Execution

• Stateful execution - Variables persist across cells (Jupyter-like behavior)
• Full numpy, pandas, aur sabhi installed packages support
• Real-time output display with proper formatting
• Error messages with complete tracebacks
• Execution counter tracking

⌨️ Professional Features

• Code Autocomplete - Python keywords, builtins, aur user-defined suggestions
• Keyboard shortcuts (Shift+Enter, Ctrl+Enter, Ctrl+Space)
• Cell management - Reorder, insert, delete
• Kernel restart capability
• Notebook save/load with JSON format

🔧 Enterprise-Grade Design

• Clean, minimal, professional UI
• Responsive design for all screen sizes
• Smooth state management
• Proper error handling
• Session-based architecture

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🚀 Quick Start

1. Install Dependencies

# Virtual environment already active
source venv/bin/activate

# Install requirements (already done)
pip install -r requirements.txt

2. Run the Application

python app.py

3. Open Browser

Navigate to: http://localhost:5000

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📖 Usage Guide

Basic Operations

Create a New Cell

• Click ➕ button in toolbar
• Or press B key (when not in edit mode)

Run a Cell

• Click ▶ Run button
• Or press Shift+Enter (runs and selects next cell)
• Or press Ctrl+Enter (runs current cell)

Delete a Cell

• Click 🗑 button in cell toolbar (appears on hover)

Run All Cells

• Click ▶▶ Run All button in toolbar

Advanced Features

Using input() Function

# This now works perfectly!
name = input("Enter your name: ")
age = input("Enter your age: ")
print(f"Hello {name}, you are {age} years old!")

A modal dialog will appear for each input() call.

Save Notebook

1. Enter notebook name in top input field
2. Click 💾 button
3. Notebook saved in notebooks/ folder as JSON

Load Notebook

1. Click 📂 Load button
2. Enter notebook name
3. Notebook loads with all cells and outputs

Restart Kernel

1. Click ⟳ Restart button
2. All variables cleared
3. New session created

Keyboard Shortcuts

Shortcut	Action
Shift+Enter	Run cell and select next
Ctrl+Enter	Run current cell
Tab	Indent code (in cell)
B	Insert cell below

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🧪 Testing Examples

Example 1: NumPy & Pandas

# Cell 1
import numpy as np
import pandas as pd

arr = np.arange(10)
df = pd.DataFrame({
    "values": arr,
    "squared": arr ** 2
})
print(df)

# Cell 2 - Variables persist!
total = df["squared"].sum()
print(f"Total: {total}")

Example 2: Interactive Input

# Cell 1
name = input("What's your name? ")
age = int(input("What's your age? "))

print(f"\nHello {name}!")
print(f"In 10 years, you'll be {age + 10} years old.")

Example 3: Data Analysis

# Cell 1
import pandas as pd
import numpy as np

# Create sample data
data = pd.DataFrame({
    'product': ['A', 'B', 'C', 'D'],
    'sales': [100, 150, 200, 175],
    'profit': [20, 35, 50, 40]
})

print(data)

# Cell 2 - Analyze the data
data['profit_margin'] = (data['profit'] / data['sales']) * 100
print("\nWith Profit Margins:")
print(data)

print(f"\nAverage Profit Margin: {data['profit_margin'].mean():.2f}%")

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📁 Project Structure

pyide/
├── app.py              # Flask backend with session management
├── requirements.txt    # Python dependencies
├── static/
│   ├── index.html     # Jupyter-style UI
│   ├── main.js        # Frontend logic with input() support
│   └── styles.css     # Professional Jupyter styling
├── notebooks/         # Saved notebooks (JSON format)
├── venv/             # Python virtual environment
└── README.md         # This file

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🔧 Technical Architecture

Backend (Flask)

• Session Management: UUID-based sessions with isolated namespaces
• Code Execution: exec() with proper stdout/stderr capture
• Input Handling: Interactive input() support with callback mechanism
• State Persistence: Variables persist across cells within same session
• Error Handling: Full traceback capture and display

Frontend (Vanilla JS)

• Cell Management: Dynamic cell creation/deletion with execution counters
• Input Modal: Beautiful dialog for input() function calls
• Keyboard Shortcuts: Jupyter-like keyboard navigation
• Real-time Updates: Async execution with loading states
• Session Tracking: Maintains connection to backend session

Storage

• Notebooks saved as JSON in notebooks/ folder
• Contains: cell code, outputs, execution counts
• Easy to version control and share

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🎨 Design Philosophy

Yeh tool enterprise-grade software ki tarah design kiya gaya hai:

1. ✅ Professional Look - Exact Jupyter Notebook appearance
2. ✅ Clean Code - Modular, well-commented, maintainable
3. ✅ User Experience - Smooth interactions, helpful feedback
4. ✅ Reliability - Proper error handling, state management
5. ✅ Performance - Efficient rendering, async operations
6. ✅ Extensibility - Easy to add new features

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🐛 Troubleshooting

Port Already in Use

# Find and kill process on port 5000
lsof -ti:5000 | xargs kill -9

# Or use different port
# Edit app.py: app.run(..., port=5001)

Module Not Found

# Ensure virtual environment is active
source venv/bin/activate

# Reinstall requirements
pip install -r requirements.txt

Session Lost

• Click ⟳ Restart button to create new session
• Or refresh the page

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🚀 Future Enhancements

Possible improvements for even more professional feel:

• Markdown cells support
• Rich output (plots, images, HTML)
• Code syntax highlighting
• Auto-completion
• Cell output collapsing
• Export to .ipynb format
• Multiple kernel support
• Real-time collaboration

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📝 Notes

• Security: Yeh tool sirf local use ke liye hai. Production deployment ke liye proper security measures add karein.
• Python Environment: Same Python environment use hota hai jahan Flask run ho raha hai.
• Browser Support: Modern browsers (Chrome, Firefox, Edge, Safari) mein best kaam karta hai.

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👨‍💻 Developer Notes

Code quality professional standards follow karta hai:

• Backend: Clean Flask routes with proper error handling
• Frontend: Modular JavaScript with clear function separation
• Styling: Professional CSS with Jupyter-matching design
• Documentation: Extensive comments and docstrings

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🎉 Enjoy Coding!

Ab aap ek professional Jupyter Notebook experience enjoy kar sakte hain apne local machine par! 🚀

Happy Coding! 💻✨