NLP Assistant: Automated Text Analysis App with AI
Waqas Ahmed
🔤 NLP Assistant
Automated Text Analysis with AI-Powered Insights
A comprehensive Streamlit application for text analysis and natural language processing. Upload text files, CSV datasets, or paste text directly to get detailed insights about your content.
✨ Features
📊 Current Features
Multiple Input Methods
Direct text input with large text area
Text file upload (.txt files)
CSV file upload with automatic text column detection
Comprehensive Text Statistics
Word count, character count (with/without spaces)
Document count and multi-document analysis
Sentence and paragraph counting
Vocabulary richness and unique word analysis
Readability metrics (avg words per sentence, chars per word)
Smart CSV Processing
Automatic delimiter detection (comma, semicolon, tab, pipe)
Text column identification (columns with avg >20 characters)
Document-level analysis for CSV rows
Interactive UI
Clean sidebar workflow
Real-time statistics display
Text preview with smart truncation
Responsive metrics layout
🚀 Coming Soon
Advanced text preprocessing options
Word frequency analysis and word clouds
Sentiment analysis with visualizations
AI-powered insights and recommendations
Topic modeling and keyword extraction
🛠️ Installation
Prerequisites
Python 3.7 or higher
pip package manager
Setup
Clone or download the project
Install dependencies
Run the application
Open in browser
The app will automatically open at
http://localhost:8501If it doesn't open automatically, navigate to the URL shown in terminal
📋 Requirements
Create a
requirements.txt file with the following dependencies:🚀 Usage
1. Choose Input Method
Direct Text Input
Paste your text directly into the text area
Ideal for: Articles, reviews, social media posts, documents
Upload Text File
Upload
.txt files from your computerSupports UTF-8 encoded text files
Ideal for: Large documents, reports, literature
Upload CSV File
Upload CSV files with text columns
Automatic text column detection and selection
Ideal for: Customer reviews, survey responses, social media data
2. Analyze Your Text
Click "📊 Analyze Text" to process your input
View comprehensive statistics instantly
Explore text preview and document structure
3. Interpret Results
Core Metrics:
Total Words: Overall word count in your text
Total Characters: Character count including/excluding spaces
Unique Words: Vocabulary diversity measure
Documents: Number of text segments or CSV rows
Advanced Metrics:
Vocabulary Richness: Percentage of unique words (higher = more diverse)
Avg Words/Sentence: Readability indicator (12-20 is typical)
Sentences & Paragraphs: Document structure analysis
Text Density: Average content per document
📁 File Support
Text Files (.txt)
UTF-8 encoding recommended
Any size supported (large files handled efficiently)
Automatic character count and preview
CSV Files (.csv)
Multiple delimiter support (comma, semicolon, tab, pipe)
Automatic text column detection
Minimum 20 characters average per cell for text columns
Multi-document analysis (each row = one document)
🎯 Use Cases
Content Analysis
Blog Posts & Articles: Analyze readability and word count
Social Media: Examine post engagement patterns
Marketing Copy: Optimize content length and complexity
Data Analysis
Customer Reviews: Bulk analysis of feedback data
Survey Responses: Open-ended response analysis
Research Data: Academic text corpus analysis
Writing Assistance
Document Review: Check length and complexity metrics
Content Planning: Understand text structure and density
Quality Assessment: Vocabulary richness and readability
🔧 Technical Details
Architecture
Frontend: Streamlit with responsive layout
Data Processing: Pandas for CSV handling
Text Analysis: Python regex and string operations
Caching: Streamlit caching for efficient file processing
Performance
Large Files: Efficient processing of substantial text datasets
Memory Management: Smart loading and session state handling
Real-time Updates: Instant statistics calculation and display
🤝 Contributing
This project is designed for educational and research purposes. Future enhancements will include:
Advanced NLP Features
Sentiment analysis
Named entity recognition
Topic modeling
Visualization Enhancements
Word clouds
Interactive charts
Text distribution plots
Export Capabilities
Statistics export to CSV/Excel
Report generation
Batch processing
📞 Support
For issues, suggestions, or contributions:
Create detailed issue reports with sample data
Include error messages and system information
Suggest new features with use case descriptions
📄 License
This project is open source and available for educational and research use.
Built with ❤️ using Streamlit
Transform your text data into actionable insights with NLP Assistant!
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Posted Sep 21, 2025
NLP Assistant: Automated text analysis. Instantly get deep insights (readability, structure, vocabulary) from pasted text, TXT, or smart-processed CSV files.
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Timeline
Aug 27, 2025 - Sep 10, 2025
