The Dynamic Text Analysis Platform

Overview

B12 is a comprehensive, enterprise-grade text analysis platform that combines advanced Natural Language Processing (NLP) capabilities with a modern web interface. It provides end-to-end document processing, analysis, and insights through a scalable REST API powered by machine learning and LLM integration.

The platform demonstrates a complete pipeline from raw document ingestion through sophisticated AI-powered analytics, utilizing state-of-the-art libraries and APIs for production-ready text intelligence.

Key Features

📄 Multi-Format Document Parsing: Support for PDF, DOCX, PPTX, HTML, and image formats with layout preservation
🔍 Intelligent Text Analysis: Document summarization, sentiment analysis, and topic modeling
🧠 Advanced Topic Modeling: BERTopic-powered unsupervised topic discovery with LLM-enhanced labeling
📊 Interactive Visualizations: Comprehensive charts, word clouds, and trend analysis for insights
🌐 RESTful API: FastAPI backend with comprehensive endpoints for all analysis tasks
💻 Modern Web Interface: React-based frontend for document processing and results visualization
⚡ Concurrent Processing: Batch processing capabilities for large-scale text analysis
🔗 LLM Integration: OpenRouter API integration for enhanced summarization and analysis

Technology Stack

Backend

Framework: FastAPI with async support
Document Processing: PyMuPDF, python-docx, Docling
Text Analysis: NLTK, Sentence Transformers
Machine Learning: BERTopic, UMAP, HDBSCAN
LLM Integration: OpenRouter API
Deployment: Uvicorn

Frontend

Framework: React 19
Language: TypeScript
Build Tool: Vite
Charting: Recharts for interactive visualizations
PDF Export: jsPDF, react-to-pdf
Markdown Rendering: react-markdown

Core Dependencies

Python 3.9+
Node.js 16+
Virtual environment (venv)

Project Architecture

┌─────────────────────────────────────┐
│   Frontend (React + TypeScript)     │
│   - Document Upload & Processing   │
│   - Results Visualization           │
│   - PDF Export Functionality        │
└────────┬────────────────────────────┘
         │ HTTP/REST
         ▼
┌─────────────────────────────────────┐
│   Backend (FastAPI)                 │
├─────────────────────────────────────┤
│   ┌─────────────────────────────┐   │
│   │ Analysis Routers            │   │
│   │ - Topic Modeling            │   │
│   │ - Sentiment Analysis        │   │
│   │ - Document Summarization    │   │
│   └─────────────────────────────┘   │
│   ┌─────────────────────────────┐   │
│   │ NLP Services & Pipelines    │   │
│   │ - Async Task Processing     │   │
│   │ - Model Inference           │   │
│   │ - Data Extraction           │   │
│   └─────────────────────────────┘   │
└─────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────┐
│   ML/Data Layer                     │
│ - BERTopic Models                   │
│ - Pretrained Transformers           │
│ - External LLM APIs                 │
└─────────────────────────────────────┘

Getting Started

Prerequisites

Python 3.9 or higher
Node.js 16 or higher
pip and npm package managers

Installation

1. Clone the Repository

git clone <repository-url>
cd B12---The-Dynamic-text-analysis-Platform

2. Setup Backend

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\activate  # On Windows
# source .venv/bin/activate  # On macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
# Create .env file in backend directory with necessary API keys

3. Setup Frontend

cd frontend

# Install dependencies
npm install

# Build TypeScript
npm run build

Running the Application

Start Backend API

cd backend
uvicorn main:app --reload
# API will be available at http://localhost:8000
# Interactive API docs at http://localhost:8000/docs

Start Frontend Development Server

cd frontend
npm run dev
# Application will be available at http://localhost:5173

Build for Production

# Frontend
cd frontend
npm run build

# Backend is ready for deployment with any ASGI server

API Documentation

Base URL

http://localhost:8000

Health Check

GET /health

Topic Modeling Endpoints

POST /predict          # Predict topics for input text
GET /model-info        # Get model metadata
DELETE /reset-session  # Reset analysis session

Sentiment Analysis Endpoints

POST /sentiment        # Analyze sentiment in documents

Summarization Endpoints

POST /summarize        # Generate document summaries

Full API documentation available at /docs when backend is running.

Project Structure

B12---The-Dynamic-text-analysis-Platform/
├── backend/                         # FastAPI application
│   ├── main.py                      # Application entry point
│   ├── core/
│   │   └── config.py               # Configuration management
│   ├── models/
│   │   └── schemas.py              # Data models & validation
│   ├── routers/
│   │   ├── topic.py                # Topic modeling endpoints
│   │   ├── sentiment.py            # Sentiment analysis endpoints
│   │   └── summarize.py            # Summarization endpoints
│   └── services/
│       ├── async_tasks.py          # Async task processing
│       ├── extractor.py            # Text extraction utilities
│       └── pipelines.py            # ML inference pipelines
├── frontend/                        # React application
│   ├── src/
│   │   ├── App.tsx                 # Main component
│   │   ├── main.tsx                # Entry point
│   │   ├── types.ts                # TypeScript types
│   │   └── assets/                 # Static assets
│   ├── index.html                  # HTML template
│   ├── package.json                # Frontend dependencies
│   ├── vite.config.ts              # Vite configuration
│   └── tsconfig.json               # TypeScript configuration
├── task1/                          # Parser Evaluation & Research
│   ├── task1_eval.py               # Library comparison script
│   └── Task1_Parser_Evaluation.md  # Evaluation report
├── task2/                          # Basic Parser Implementation
│   └── pymupdf_parser.py           # PDF/DOCX extraction
├── task3/                          # Advanced Parser
│   └── doc_parser.py               # Multi-format parsing with Docling
├── task4/                          # Text Preprocessing
│   ├── preprocessing.py            # Tokenization, stemming, lemmatization
│   └── step_by_step_demo.py        # Pipeline demonstration
├── task5/                          # Document Summarization
│   └── summarizer_openrouter.py   # LLM-based summarization
├── task6/                          # Sentiment Analysis
│   └── sentiment_analyzer.py       # Multi-batch sentiment processing
├── task7/                          # Topic Modeling
│   ├── train_topic_model.py        # BERTopic model training
│   ├── run_inference.py            # Topic inference
│   ├── models/                     # Trained model artifacts
│   └── results/                    # Classification results
├── task8/                          # Legacy API Implementation
│   └── api.py                      # Original FastAPI setup
├── processed_data/                 # Sample outputs
├── assets/                         # Input documents
├── LICENSE
└── README.md                       # This file

Workflow & Data Pipeline

Raw Documents (PDF, DOCX, PPTX, etc.)
           ↓
   Document Parsing (Task 3)
 [Docling + PyMuPDF Fallback]
           │
           ├──────────────────────────────────────────┐
           │                                          │
           ↓                                          ↓
  Raw, Formatted Text                     [Archived / Standalone Utility]
(Maintains natural grammar)                 Text Preprocessing (Task 4)
           │                               (Not used in current pipeline)
           │
   ┌───────┼──────────────┐                              
   ↓       ↓              ↓                              
 Task 5   Task 6        Task 7
(Summ.)  (Sent.)       (Topic)
 [LLM]  [FinBERT]    [BERTopic]
   │       │              │                              
   └───────┼──────────────┘
           ↓
   REST API (FastAPI Backend)
           ↓
   Web Interface (React Frontend)
           ↓
   Results, Dashboards & Insights

Core Features in Detail

📄 Document Processing

Multi-format support: PDF, DOCX, PPTX, HTML, XML, images
Layout preservation: Maintains document structure in Markdown output
Batch processing: Handle multiple documents concurrently
Format evaluation: Comparative analysis of parsing libraries with WER metrics

📊 Interactive Visualizations

Word Clouds: Visual representation of key themes and keywords
Topic Distribution Charts: Bar charts showing all discovered topics with confidence scores
Sentiment Distribution: Pie charts for positive/negative/neutral sentiment breakdowns
Sentiment Trend Analysis: Scatter plots showing sentiment evolution through the document
Custom Tooltips: Interactive hover information for detailed insights
PDF Export: All visualizations included in exported reports

🧠 Intelligent Analysis

Sentiment Analysis: LLM-powered sentiment detection with concurrent processing
Document Summarization: Extract key information using advanced LLMs (Gemini, Llama)
Topic Modeling: Unsupervised topic discovery with semantic understanding
Text Preprocessing: Advanced NLP preprocessing pipeline

🔗 Extensible Architecture

Modular design: Each analysis component is independently deployable
Async-first: Built for high-throughput concurrent processing
LLM integration: Seamless integration with external AI APIs
RESTful endpoints: Standard HTTP API for all functionality

Development

Running Tests & Validation

# Example: Run document parser
python task3/doc_parser.py

# Example: Run preprocessing demo
python task4/step_by_step_demo.py

# Example: Run sentiment analysis
python task6/sentiment_analyzer.py

# Example: Train topic model
python task7/train_topic_model.py

Code Quality

cd frontend
npm run lint          # Run ESLint
npm run build         # Verify TypeScript compilation

Environment Variables

Backend requires the following environment variables (.env file):

OPENROUTER_API_KEY=your_api_key_here
DATABASE_URL=optional_database_url
LOG_LEVEL=INFO

Deployment

Production Deployment

Backend (Using Gunicorn + Uvicorn)

# Install production server
pip install gunicorn

# Run with multiple workers
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

Frontend (Static Files)

# Build for production
npm run build

# Serve with your preferred web server (nginx, Apache, etc.)
# Outputs to: frontend/dist/

Docker Deployment (Optional)

Create Dockerfile for backend:

FROM python:3.10-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Performance Optimization

Backend: Consider using Redis for caching model predictions
Frontend: Implement lazy loading for large document uploads
Database: Monitor query performance with appropriate indexing
API: Implement rate limiting for production use

Troubleshooting

Common Issues

Issue: Backend fails to start with model loading errors

# Solution: Ensure all model dependencies are installed
pip install -r requirements.txt
python -c "import torch; import transformers; print('Dependencies OK')"

Issue: Frontend cannot connect to backend API

# Solution: Check backend is running and CORS is properly configured
# Verify Backend is running on http://localhost:8000
# Check frontend environment configuration

Issue: Out of memory errors during topic modeling

# Solution: Reduce batch size in task7/train_topic_model.py
# or use GPU acceleration if available

Issue: API returns 500 errors during document processing

# Solution: Check backend logs and ensure OpenRouter API key is valid
# Verify input documents are in supported formats

Performance Benchmarks

Document Parsing: ~50-100 KB/s depending on format complexity
Text Preprocessing: ~10,000 tokens/second
Topic Modeling Inference: <1s per document
API Response Time: <500ms for single document analysis

API Response Examples

Topic Prediction

{
  "topics": [
    {
      "id": 0,
      "label": "Finance",
      "probability": 0.95
    },
    {
      "id": 1,
      "label": "Technology",
      "probability": 0.03
    }
  ],
  "processing_time_ms": 245
}

Sentiment Analysis

{
  "sentiment": "positive",
  "confidence": 0.92,
  "entities": ["company", "growth", "innovation"],
  "summary": "Document discusses positive business developments"
}

Document Summary

{
  "original_length": 2500,
  "summary_length": 450,
  "summary": "Key points about the document...",
  "key_phrases": ["metric1", "metric2", "metric3"]
}

Research & Evaluation

The project includes comprehensive research documentation:

Parser Evaluation (Task 1): Comparative analysis of PDF parsing libraries
Preprocessing Techniques (Task 4): Detailed results and comparisons
Model Performance (Task 7): Topic modeling accuracy and metrics

See individual task directories for detailed reports and evaluation results.

Contributing

Contributions are welcome! Please follow these guidelines:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Code Standards

Python: Follow PEP 8 guidelines
TypeScript: Use provided ESLint configuration
Documentation: Update README and add docstrings for new functions
Testing: Include test cases for new features

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

BERTopic: Advanced topic modeling framework
Sentence Transformers: Semantic embeddings
OpenRouter: LLM API aggregation
FastAPI: Modern web framework
React: UI framework

Project Timeline

Phase 1: Document parsing and evaluation
Phase 2: Text preprocessing pipeline
Phase 3: Advanced analysis modules (summarization, sentiment)
Phase 4: Topic modeling integration
Phase 5: API deployment and backend integration
Phase 6: Frontend development and visualization

Roadmap

v1.1 (Planned)

Multi-language support
Advanced visualization dashboard
Real-time collaborative analysis
Model fine-tuning interface

v1.2 (Planned)

GraphDB integration for entity relationships
Knowledge graph extraction
Advanced search capabilities
Batch processing optimization

v2.0 (Long-term)

Distributed processing support
Custom model training UI
Advanced reporting and analytics
Enterprise integrations

Support & Contact

For issues, feature requests, or questions:

Issues: Use GitHub Issues tracker
Documentation: See task-specific markdown files
API Docs: Visit http://localhost:8000/docs when running backend

Quick Links

Last Updated: March 2026 Maintainer: B12 Development Team Status: Active Development

Name		Name	Last commit message	Last commit date
assets		assets
backend		backend
frontend		frontend
node_modules		node_modules
task1		task1
task2		task2
task3		task3
task4		task4
task5		task5
task6		task6
task7		task7
task8		task8
.gitignore		.gitignore
README.md		README.md
package.json		package.json

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