---
title: Pravaah - Ocean Hazard Detection System
emoji: 🌊
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit
short_description: AI-powered system to detect ocean hazards
---

# 🌊 Ocean Hazard Detection System

An AI-powered system that analyzes social media posts to detect ocean-related hazards in real-time. This system uses advanced natural language processing to identify hazardous tweets, translate them to English, analyze sentiment, and extract location information.

## 🚀 Features

- **Multilingual Support**: Analyzes tweets in 20+ Indian languages including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, and English
- **Hazard Classification**: Uses XLM-RoBERTa zero-shot classification to identify ocean hazards
- **Sentiment Analysis**: Analyzes emotional context using GoEmotions model
- **Named Entity Recognition**: Extracts hazard types and locations from text
- **Real-time Processing**: Processes tweets from Indian coastal regions
- **Database Storage**: Stores hazardous tweets for tracking and analysis

## 🔍 What It Detects

### Hazard Types
- Floods and tsunamis
- Cyclones and storm surges
- High tides and waves
- Coastal flooding and erosion
- Rip currents and marine debris
- Water discoloration and algal blooms
- Marine pollution

### Geographic Coverage
- **Major Cities**: Mumbai, Chennai, Kolkata, Vizag, Puri
- **States**: Odisha, Kerala, Gujarat, Goa, Andhra Pradesh, West Bengal
- **Water Bodies**: Bay of Bengal, Arabian Sea

## 🛠️ Technical Stack

- **AI Models**: 
  - DeBERTa-v3 for hazard classification
  - Helsinki-NLP for translation
  - GoEmotions for sentiment analysis
  - DistilBERT NER for location extraction
- **Backend**: FastAPI + Gradio
- **Database**: PostgreSQL
- **Languages**: Python 3.9+

## 📊 How It Works

1. **Tweet Collection**: Scrapes tweets using Twitter API with hazard and location keywords
2. **Translation**: Translates ALL tweets to English for consistent processing (more efficient)
3. **Hazard Classification**: Uses zero-shot learning on translated text to classify as hazardous or safe
4. **Sentiment Analysis**: Analyzes emotional context (panic, calm, confusion, neutral) for hazardous tweets
5. **Entity Extraction**: Identifies specific hazard types and locations from translated text
6. **Database Storage**: Stores hazardous tweets with metadata for tracking

## 🚀 Usage

### Web Interface (Gradio)
1. **Set Tweet Limit**: Choose how many tweets to analyze (1-50)
2. **Click Analyze**: The system will process tweets and show results
3. **View Results**: See hazardous tweets with sentiment, location, and hazard type
4. **Export Data**: Download complete analysis as JSON

### API Endpoints (FastAPI)

#### **POST /analyze**
Analyze tweets for ocean hazards
```bash
# Basic analysis
curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: application/json" \
  -d '{"limit": 20}'

# Keyword-based search
curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: application/json" \
  -d '{"limit": 20, "hazard_type": "tsunami", "location": "Mumbai", "days_back": 2}'

# Custom query
curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: application/json" \
  -d '{"limit": 20, "query": "flood OR tsunami"}'
```

#### **GET /hazardous-tweets**
Get stored hazardous tweets
```bash
curl "http://localhost:8000/hazardous-tweets?limit=50&offset=0"
```

#### **GET /keywords/hazards**
Get available hazard types for keyword search
```bash
curl "http://localhost:8000/keywords/hazards"
```

#### **GET /keywords/locations**
Get available locations for keyword search
```bash
curl "http://localhost:8000/keywords/locations"
```

#### **GET /stats**
Get analysis statistics
```bash
curl "http://localhost:8000/stats"
```

#### **GET /health**
Health check endpoint
```bash
curl "http://localhost:8000/health"
```

### API Documentation
- **Swagger UI**: `http://localhost:8000/docs`
- **ReDoc**: `http://localhost:8000/redoc`

## 🔧 Environment Variables

The system requires the following environment variables:

```bash
# Twitter API (required)
TWITTER_API_KEY=your_twitter_api_key

# PostgreSQL Database (optional for demo)
PGHOST=localhost
PGPORT=5432
PGDATABASE=postgres
PGUSER=postgres
PGPASSWORD=your_password
```

## 📈 Use Cases

- **Emergency Response**: Early detection of ocean hazards for rapid response
- **Environmental Monitoring**: Track marine pollution and coastal issues
- **Research**: Analyze public sentiment about ocean-related events
- **Policy Making**: Data-driven insights for coastal management policies

## 🔬 Model Details

- **Classification Model**: `cross-encoder/nli-deberta-v3-base`
- **Translation Model**: Helsinki-NLP OPUS-MT models
- **Sentiment Model**: Google GoEmotions
- **NER**: DistilBERT NER with keyword-based fallback

## 📝 License

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

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 📞 Support

For support, please open an issue in the GitHub repository.

---

**Note**: This is a demonstration system. In production, it would process real-time tweets and integrate with emergency response systems.