TextMind is a lightweight, privacy-first text intelligence tool that can analyze, summarize, and understand any text document — all offline, without sending data to the cloud.
It’s built under extreme engineering constraints:
- 🧩 Only one file
- 🧠 Maximum 8 variables
- 📏 Under 400 lines of code
Yet it performs advanced text analysis, tone detection, keyword extraction, and debate-style polarity breakdowns — all while keeping your data safe on your device.
"Powerful Text Intelligence. Complete Privacy."
We live in a world filled with text — documents, research papers, emails, chats, feedback, and articles.
But most existing tools are either:
- 🌐 Cloud-based → risky for privacy
- 🧰 Heavy libraries → large dependencies, slow setup
- 💰 Paid or limited
TextMind bridges this gap — a smart, fast, offline text processing solution that works locally and gives AI-like intelligence in seconds.
TextMind offers an AI-inspired yet offline text understanding tool that works directly in your terminal. From summaries to sentiment and contextual keyword search, it delivers human-like insights in one click. All results are generated using a rule-based, language-aware processing engine — completely transparent, interpretable, and private.
Generate quick or detailed summaries of any document:
python TextMind.py Example-paper.pdf --summary➡ Outputs a concise, meaningful paragraph that captures document essence.
Get the tone of a document or specific sections:
python TextMind.py Example-paper.txt --tone➡ Classifies tone as Positive, Negative, or Neutral and gives percentage distribution.
Perfect for research or discussion prep:
python TextMind.py Example-paper.txt --debate➡ Lists advantages vs disadvantages and computes argument balance score.
Search any keyword and see how it’s used:
python TextMind.py Example-paper.docx --context AI➡ Displays surrounding sentences for true context understanding.
Generate an all-in-one intelligence report:
python TextMind.py Example-paper.txt --report➡ Includes:
- Document Summary
- Top Keywords
- Word Count
- Sentence Count
- Positive/Negative Sentence Lists
- Tone Analysis
- Word Intelligence Index
- Sentence Ranking
- Debate Statistics
python TextMind.py Example-paper.txt --export-jsonpython TextMind.py Example-paper.txt --export-ml➡ Exports structured data for ML, dashboard visualization, or further research.
Fully colored CLI interface for better readability and instant pattern recognition — making data fun to explore!
Supports:
.txt.pdf.docx.md
No cloud upload — everything runs locally.
Add the following visuals to your repo:
| Screenshot | Description |
|---|---|
 |
Command Help Menu – shows available options |
 |
Terminal Summary Output – highlights colored analysis |
|
Full Report View – detailed statistics and summary |
 |
Debate Analyzer – positive vs negative results |
 |
Export – export structure data into ML and JSON format |
🎬 YouTube Presentation Video:
📺 Watch Full Hackathon Presentation →
🧑💻 Try it Yourself:
git clone https://github.com/DhruvP2205/TextMind
cd TextMind
python TextMind.py sample.txt --report| Domain | Use Case |
|---|---|
| 🎓 Education | Summarize research papers, find tone of student essays |
| 🗞️ Journalism | Extract key quotes, find sentiment of news articles |
| 🏢 Business | Analyze customer reviews, support tickets, or internal reports |
| ⚖️ Legal | Summarize long case files while preserving privacy |
| 🧑💻 Developers | Integrate as a command-line analyzer for automation pipelines |
| 🤖 AI Research | Export rule-based intelligence as ML-ready JSON data |
- Brings NLP intelligence offline — no data exposure
- Bridges rule-based + AI concepts for interpretable models
- Empowers privacy-first data analysis
- Reduces cost & complexity of text understanding tasks
TextMind redefines what’s possible within constraints — proving that minimalism and intelligence can coexist.
✅ Runs fully offline
✅ Uses no external API
✅ Tiny footprint — just one Python file
✅ Constraint-engineered: 400 lines, 8 variables
✅ Works across text, PDF, and DOCX
✅ Generates explainable intelligence
✅ Produces AI-ready structured data
No bloat. No dependencies. 100% transparency.
| Component | Technology |
|---|---|
| 💻 Language | Python 3.x |
| ⚙️ Core Logic | Native Python |
| 🗃️ Input/Output | File handling for .txt, .pdf, .docx, .md |
| 🎨 Terminal UI | ANSI color codes for colored output |
| 📊 Export Format | JSON |
| 🧩 Architecture | Single-file, modular functions, functional paradigm |
python TextMind.py Example-paper.txt --reportOutput:
-------------------------------------------------
📄 Document Summary:
This feedback set reflects overall customer satisfaction with minor concerns about response time.
💬 Tone Analysis:
Positive: 78% | Negative: 14% | Neutral: 8%
🔑 Top Keywords:
support, response, product, delay, amazing
🧭 Word Intelligence Index: 7.4/10
🏁 Sentence Ranking: 9/10
⚖️ Debate Balance: 3:1 (Pros vs Cons)
-------------------------------------------------
- Clone the repository:
git clone https://github.com/DhruvP2205/TextMind cd TextMind - Run the script:
python TextMind.py yourfile.txt --report
- Explore all commands:
python TextMind.py --help
🚧 Planned upgrades:
- 🌐 Lightweight web UI (Flask/FastAPI)
- 🧾 PDF & DOCX export support for reports
- 🤖 Integration with local LLMs for hybrid offline AI
- 📊 Visual dashboard (HTML report)
- 🧠 Adaptive learning from tone history
We welcome open-source contributions!
- Fork the repo 🍴
- Create your feature branch (
git checkout -b feature/your-feature) - Commit changes (
git commit -m 'Add new feature') - Push (
git push origin feature/your-feature) - Create a Pull Request 🎉
⭐ If you found TextMind useful, give it a Star on GitHub!
💬 Fork it, modify it, and make it yours!
| Challenge | Limitation |
|---|---|
| Few-Variable Hero | Max 8 variables |
| Feature-Rich Dev | Max 400 lines |
| Domain | Text Processing |
Despite these constraints, TextMind delivers multi-layered intelligence usually seen only in AI tools — a perfect blend of creativity, optimization, and functionality.
This project demonstrates how:
- Minimal rule-based models can mimic early-stage AI understanding.
- Data can be structured for ML training offline.
- Constraints can enhance creativity and efficiency.
It’s a proof of concept for building explainable, resource-efficient AI utilities without relying on large-scale frameworks.
This project is licensed under the MIT License — free for personal and commercial use.
👤 Developer: Dhruv Prajapati
💼 LinkedIn: Dhruv Prajapati Linkedin
📺 YouTube Demo: Watch Here
🐙 GitHub: github.com/DhruvP2205
“TextMind isn’t just a tool — it’s a proof that less can do more.
It redefines what’s possible in 400 lines of Python.”
If you enjoyed this project —
🌟 Star it, 🍴 Fork it, and 🧠 Explore your own text insights!
Made with ❤️ by BeTheNoob