🐇 Talking Rabbitt – Conversational AI Analytics MVP

Ask questions. Get insights. Instantly.

---

🏷️ Badges

---

📌 Overview

Talking Rabbitt is a conversational intelligence layer that enables business leaders to interact with their data using natural language queries — no dashboards, no filters, no technical expertise required.

Instead of navigating complex BI tools, users can simply ask questions about their data and receive instant insights with automatic visualizations.

This repository contains the MVP prototype of Talking Rabbitt.

---

🎯 Demo Screenshot

---

🚀 Live Demo

👉 Try Talking Rabbitt Now

---

❌ The Problem

Traditional analytics tools like:

• Microsoft Power BI
• Tableau
• Excel Pivot Tables

require users to:

• Navigate multiple dashboards
• Apply filters manually
• Create charts step by step
• Understand technical syntax

This process is time-consuming, frustrating, and excludes non-technical users.

Tool	Time to Answer	Technical Skill Required
Excel	5–10 min	Medium
Power BI	5–15 min	High
Tableau	5–15 min	High
Talking Rabbitt	5 seconds	None

---

✅ The Solution

Talking Rabbitt introduces a conversational query layer on top of structured data.

How it works:

1. 📂 Upload your CSV file
2. 💬 Ask a natural language question
3. 🤖 Receive instant text insight
4. 📊 View automated chart

Example:

User Question	System Output
"Which region had the highest revenue?"	✅ "Region with highest revenue: West" + Bar chart

---

✨ Features

Feature	Description
📁 CSV Upload	Drag & drop or browse files (up to 200MB)
🔍 Dataset Preview	See your data before asking questions
💬 Natural Language Query	Ask questions in plain English
⚡ Instant Insights	Get text answers immediately
📊 Dynamic Charts	Auto-generated bar charts for relevant queries
🧠 Extensible	Easy to add more query patterns

---

🛠️ Tech Stack

Layer	Technology
Frontend	Streamlit
Backend	Python 3.x
Data Processing	Pandas
Visualization	Matplotlib
File Handling	CSV (native support)

---

📁 Project Structure

talking-rabbitt/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── screenshot.png         # Demo screenshot
└── README.md              # This file

---

🚀 How to Run

1. Clone the repository

git clone https://github.com/harleenkaurkambojgandhi-design/talking-rabbitt-ai-analytics-mvp
cd talking-rabbitt-ai-analytics-mvp

2. Install dependencies

pip install -r requirements.txt

3. Run the app

streamlit run app.py

4. Open your browser

Navigate to http://localhost:8501

---

📦 requirements.txt

streamlit
pandas
matplotlib

---

💡 Sample Query Patterns

Question Type	Example
Highest value	"Which region had the highest revenue?"
(More coming soon)	"What was total revenue by quarter?"

---

⏱️ The Magic Moment

Before (Traditional)	After (Talking Rabbitt)
Open Excel	Upload CSV
Find correct sheet	Type question
Apply filters	Hit enter
Create pivot table	✅ Instant answer + chart
Build chart
Copy to presentation
⏰ 10 minutes	⚡ 5 seconds

10 minutes → 5 seconds
That's the magic of conversational analytics.

---

🔮 Future Enhancements

• Support for more question types (average, total, trends)
• Multiple chart types (line, pie, scatter)
• AI-powered NLP (OpenAI integration)
• Date-range filtering
• Export insights as PDF/PNG
• Multi-file uploads and joins

---

🤝 Contributing

Contributions are welcome! Feel free to:

• Fork the repo
• Create a feature branch
• Submit a pull request

---

📄 License

MIT License — free for personal and commercial use.

---

🙏 Acknowledgments

Built with:

• Streamlit — rapid app development
• Pandas — data power
• Matplotlib — beautiful charts

---

"Stop navigating dashboards. Start asking questions."
— Talking Rabbitt 🐇

## Support

If you find this project useful, please consider giving it a ⭐ star on GitHub to help others discover it.