Gemini Geospatial Streamlit

A powerful interactive application that combines Google's Gemini AI with geospatial analysis capabilities, enabling natural language-driven exploration and visualization of geographic data.

🌟 Features

• AI-Powered Chat Interface: Interact with geospatial data using natural language queries
• Interactive Map Visualization: Real-time display of geospatial data with multiple layer support
• Risk Analysis Tools: Specialized tools for assessing weather risks to infrastructure
• Power Line Infrastructure Analysis: Visualize and analyze power line networks
• Integrated Weather Data: Access weather forecasts and analyze weather-related risks

📋 Requirements

• Python 3.10+
• Google Gemini API key
• Streamlit
• GeoPandas and other geospatial libraries (see requirements.txt for full details)

🚀 Quick Start

1. Clone the repository

   git clone https://github.com/yourusername/gemini-geospatial-streamlit.git
   cd gemini-geospatial-streamlit

2. Create a virtual environment and install dependencies

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Set up environment variables

   Create a .env file in the project root with the following variables:

   PROJECT_ID=your_google_cloud_project_id
   GCS_BUCKET_NAME=your_gcs_bucket_name
   

4. Download supplementary shapefiles

   python download_gcs_data.py

   This will download required shapefiles and other geospatial data from the specified GCS bucket to the data/local directory.

5. Run the application

   streamlit run app.py

6. Access the web interface

   Open your browser and navigate to http://localhost:8501

🧩 Architecture

The application is structured into several key components:

• app.py: Main entry point for the Streamlit application
• components/: UI components including chat interface, map, and sidebar
• services/: Core services for Gemini integration, map processing, and risk analysis
• data/: Data access and processing modules
• utils/: Utility functions and helpers
• action_handlers/: Handlers for map actions triggered by AI responses

🔍 Usage Examples

Ask About Regional Weather Risks

"What areas in Pennsylvania are at high risk for wind damage to power lines in the next 3 days?"

Analyze Specific Regions

"Show me the power infrastructure in Chester County and highlight areas with potential weather impacts."

Compare Multiple Regions

"Compare the weather risks between Philadelphia and Pittsburgh for the next week."

🧠 How It Works

1. User submits a query through the chat interface
2. The query is processed by the Gemini AI model with a specialized system prompt
3. Gemini generates a structured response including:
   • Text response for the user
   • Map action instructions
   • Optional structured data for visualization
4. The application processes the map actions to update the interactive map
5. Results are displayed to the user in both chat and map interfaces

🛠️ Configuration

You can configure the application behavior in the sidebar, including:

• Model parameters
• Feature toggles for specific capabilities
• Date selection for weather forecasts
• Debug panel for viewing system prompts and API responses

Cloud Deployment

1. Enable Required APIs

# enable services
gcloud services enable storage.googleapis.com &&
gcloud services enable run.googleapis.com &&
gcloud services enable aiplatform.googleapis.com &&
gcloud services enable cloudbuild.googleapis.com

# grant permissions to service account
export PROJECT_ID=`gcloud config get-value project`
export PROJECT_NUMBER=`gcloud projects describe $PROJECT_ID --format="value(projectNumber)"`
gcloud projects add-iam-policy-binding $PROJECT_ID --member=serviceAccount:$PROJECT_NUMBER[email protected] --role="roles/run.builder"
gcloud projects add-iam-policy-binding $PROJECT_ID --member=serviceAccount:$PROJECT_NUMBER[email protected] --role="roles/logging.logWriter"
gcloud projects add-iam-policy-binding $PROJECT_ID --member=serviceAccount:$PROJECT_NUMBER[email protected] --role="roles/storage.objectUser"

2. Deploy to Cloud Run

Manually enable IAP then run:

gcloud beta run deploy gemini-geospatial-streamlit \
--source . \
--region="us-central1" \
--platform managed \
--memory 32Gi \
--cpu 8 \
--region us-central1 \
--min-instances 1 \
--timeout 1h \
--no-cpu-throttling \
--cpu-boost \
--no-allow-unauthenticated \
--iap

-- Deploying from Artifact Registry ...: Y -- Allow unauthenticated invocations...: N

3. Enable IAP

2. Add IAP-secured Web App User to appropriate users

4: Configure Environment and Secrets

1. Go to Cloud Run service → Source → Edit source
2. Rename .env.example to .env and update with your configuration, for example:

GCP_PROJECT_ID=your-project-id
GCS_BUCKET_NAME=your-bucket

📊 Data Sources

The application uses several data sources:

• Weather forecast data
• US geographic boundaries (states, counties)
• Power infrastructure data for Pennsylvania
• Custom local datasets (as configured)
• Supplementary shapefiles stored in a GCS bucket specified by the GCS_BUCKET_NAME environment variable

🤝 Contributing

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

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

📄 License

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