WeatherGPT

Hyper-Local Meteorological Intelligence Platform

WeatherGPT is a full-stack meteorological intelligence and weather analysis platform designed to make weather information easier to understand and more actionable.

The platform combines real-time weather observations, forecasts, air-quality information, geospatial visualization, radar data, weather advisories, climate analysis, cyclone monitoring, and conversational weather assistance in a single interface.

It is designed with practical use cases in mind, including agriculture, disaster preparedness, travel planning, urban monitoring, and general weather awareness.

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Key Features

Real-Time Weather Information

WeatherGPT provides real-time atmospheric information including:

• Temperature
• Feels-like temperature
• Relative humidity
• Atmospheric pressure
• Cloud cover
• UV index
• Wind speed
• Wind gusts
• Wind direction
• Air quality information

Air Quality Monitoring

The platform provides air-quality information and particulate measurements including:

• PM2.5
• PM10
• NO2
• O3
• CO
• SO2

AQI information is presented with corresponding health-risk classifications.

Interactive Weather Map

The platform provides an interactive geospatial weather interface with:

• Radar visualization
• Rainfall information
• Wind-flow visualization
• Temperature zones
• Weather station information
• Geographic overlays
• Interactive map navigation

Radar and Rainfall Visualization

WeatherGPT integrates radar tile data to visualize precipitation activity and rainfall patterns.

The radar interface includes range-based visualization and precipitation intensity information.

Wind Stream Visualization

A custom HTML5 Canvas-based visualization represents atmospheric wind flow using animated particles and directional vectors.

The visualization is generated from wind speed and direction information.

Conversational Weather Assistant

The conversational interface allows users to ask weather-related questions using natural language.

Examples include:

• "Will it rain today?"
• "What will the weather be tomorrow?"
• "Is it safe to travel?"
• "Should I water my crops today?"
• "What is the weather forecast for the next seven days?"

The assistant converts available meteorological information into structured and understandable responses.

Multilingual Support

WeatherGPT is designed to support multiple Indian languages, including:

• English
• Hindi
• Marathi
• Bengali
• Tamil
• Telugu
• Gujarati
• Kannada
• Malayalam
• Punjabi
• Odia

Agricultural Weather Advisory

The agriculture module focuses on weather-based decision support for farmers.

Potential applications include:

• Irrigation planning
• Crop spraying conditions
• Harvest planning
• Rainfall planning
• Weather-risk awareness
• Soil moisture considerations
• Weather-based agricultural decisions

The objective is to make complex weather information easier to use for practical agricultural decisions.

Cyclone Monitoring

The cyclone module provides a dedicated interface for monitoring tropical disturbances and cyclone-related information.

It can display information such as:

• Storm position
• Movement
• Central pressure
• Storm trajectory
• Coastal warning information

Forecast Analysis

WeatherGPT provides forecast information across multiple time scales, including:

• Current conditions
• Hourly forecast
• Daily forecast
• Seven-day forecast
• Precipitation probability
• Temperature trends

Climate Analysis

The platform includes components for examining historical weather information and climate-related trends.

These tools are intended to help users understand changes and patterns in weather data over time.

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Technology Stack

Frontend

• React 19
• TypeScript
• Vite
• Tailwind CSS
• Motion
• React-Leaflet
• Leaflet
• Recharts
• HTML5 Canvas
• Lucide React

Backend

• Node.js
• Express
• TypeScript
• tsx
• esbuild

Data and Weather Services

• Open-Meteo
• RainViewer
• Public meteorological data sources
• Geospatial weather data

AI Integration

• Server-side AI service integration
• Natural-language weather query processing
• Meteorological response generation

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Project Structure

weathergpt/
│
├── public/
│   ├── favicon.svg
│   └── weathergpt-logo.svg
│
├── server/
│   ├── geminiService.ts
│   └── weatherService.ts
│
├── src/
│   ├── components/
│   │   ├── agriculture/
│   │   ├── alerts/
│   │   ├── brand/
│   │   ├── chat/
│   │   ├── climate/
│   │   ├── cyclone/
│   │   ├── forecast/
│   │   ├── layout/
│   │   ├── map/
│   │   └── weather/
│   │
│   ├── data/
│   ├── types.ts
│   ├── App.tsx
│   └── main.tsx
│
├── server.ts
├── vite.config.ts
├── package.json
└── README.md

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Getting Started

Prerequisites

• Node.js 18 or later
• npm

You can also use Bun or Yarn if preferred.

Installation

Clone the repository:

git clone https://github.com/YOUR_USERNAME/weathergpt.git
cd weathergpt

Install dependencies:

npm install

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Environment Configuration

Create a .env file in the project root:

GEMINI_API_KEY=your_api_key_here

The API key should remain server-side and must not be exposed through frontend environment variables.

Do not commit .env files or API keys to GitHub.

Recommended .gitignore entries:

.env
.env.local
.env.production

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Development

Start the development server:

npm run dev

The application will be available at the local development URL shown by Vite.

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Production Build

Build the application:

npm run build

Start the production server:

npm start

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Deployment

Vercel

WeatherGPT can be deployed using Vercel.

Deployment Steps

1. Push the project to GitHub.
2. Open the Vercel dashboard.
3. Create a new project.
4. Import the GitHub repository.
5. Configure the required environment variables.
6. Deploy the project.

Environment Variables

Add the following environment variable in the Vercel project settings:

GEMINI_API_KEY

Use the corresponding API key as its value.

Do not add the API key directly to source code.

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Data Sources

Open-Meteo

WeatherGPT uses Open-Meteo for weather and forecast information.

Website:

https://open-meteo.com/

RainViewer

RainViewer is used for weather radar and precipitation visualization.

Website:

https://www.rainviewer.com/

Leaflet

Leaflet provides the interactive map framework.

Website:

https://leafletjs.com/

React-Leaflet

React-Leaflet integrates Leaflet with React.

Website:

https://react-leaflet.js.org/

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API and Service Architecture

                    User
                     |
                     v
              React Frontend
                     |
          +----------+----------+
          |                     |
          v                     v
   Weather Services       AI Assistant
          |                     |
          v                     v
    Weather Data          AI Processing
          |                     |
          +----------+----------+
                     |
                     v
              WeatherGPT UI

The frontend communicates with backend services, while external weather providers supply meteorological data.

AI-related processing is handled through server-side services.

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Use Cases

Agriculture

WeatherGPT can assist farmers with weather-based decisions such as:

• Irrigation timing
• Crop spraying conditions
• Harvest planning
• Rainfall awareness
• Weather-risk assessment

Disaster Preparedness

The platform can help users access:

• Severe-weather information
• Heavy rainfall information
• Cyclone information
• Weather alerts
• Location-specific forecasts

Travel and Daily Planning

Users can use the platform to understand:

• Current weather
• Rain probability
• Temperature trends
• Wind conditions
• Air quality
• Short-term forecasts

Urban Weather Monitoring

Weather information can support:

• Local weather awareness
• Air-quality monitoring
• Rainfall monitoring
• Temperature analysis
• Weather-related planning

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Accessibility

WeatherGPT is designed with accessibility in mind.

The platform includes:

• Multilingual interaction
• Voice-based interaction
• Conversational queries
• Visual weather information
• Location-based weather information

These features are intended to make meteorological information easier to access for users with different technical and language backgrounds.

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Project Goals

The main goals of WeatherGPT are:

1. Make weather information easier to understand.
2. Bring multiple meteorological data sources into one interface.
3. Provide contextual weather information through natural-language queries.
4. Improve accessibility for rural and multilingual users.
5. Support agriculture and weather-based decision making.
6. Improve access to severe-weather and cyclone information.
7. Provide useful visualization of complex meteorological data.

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Limitations

WeatherGPT depends on the availability, accuracy, coverage, and update frequency of its external weather data sources.

The platform should not be considered a replacement for official emergency warning systems or authoritative meteorological agencies.

For critical weather events and emergency decisions, users should always verify information with official meteorological authorities.

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Security

Sensitive credentials must never be committed to the repository.

Before pushing the project to GitHub, verify that the repository does not contain:

.env
.env.local
API keys
Private credentials
Access tokens
Service credentials

If an API key is accidentally exposed, revoke and replace it immediately.

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License

WeatherGPT is open-source software licensed under the MIT License.

Copyright (c) 2026 Rahul.

See the LICENSE file for the complete license terms.

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Author

Developed by Rahul

B.Tech Artificial Intelligence and Machine Learning
D. Y. Patil University

WeatherGPT was developed as a practical implementation of a Smart India Hackathon problem statement focused on conversational weather forecasting, alerts, climate information, and meteorological decision support.

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Project Status

Active development.

The project may be extended with additional meteorological datasets, official weather services, improved forecasting capabilities, additional regional languages, and enhanced decision-support features.