Disaster Resilience Platform

Problem Statement : Natural disasters such as floods, hurricanes, etc cause significant damage to life, property, and infrastructure.​

A major challenge during disasters is the lack of real-time communication, effective coordination, and actionable insights for rescue teams, governments, and affected individuals. ​

During disasters, donations are often mismanaged or lack transparency, causing mistrust and inefficiency. ​

An AI-powered disaster management platform is needed to identify disasters and assess risk zones, coordinate rescue and relief efforts in real-time, and provide post-disaster recovery assistance.​

Documentation

Overview

Project for the IBM Skills Build Hackathon

The Disaster Resilience Platform is a web application designed to provide real-time information and analysis on various disaster-related topics. It includes features such as a dashboard, affected areas visualization, donation tracking, visualizations, a chatbot, and SOS messages.

Installation

1. Clone the repository:

   git clone https://github.com/adv-11/Disaster_Resilience_Platform.git
   cd Disaster_Resilience_Platform

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the root directory of the project and add the following variables:

    WATSON_API_KEY = ""
   
    NEWS_API_KEY= ""
    CLOUDANT_API_KEY = ""
    CLOUDANT_URL = ""
    CLOUDANT_DB_NAME = ""
    TAVILY_API_KEY = ""
    NLU_API_KEY = ""
    NLU_URL = ""
   
    TWITTER_API_KEY = ""
   
    TWITTER_API_SECRET =' '
   
    TWITTER_BEARER_TOKEN = ''
   
    TWITTER_ACCESS_TOKEN = ''
   
    TWITTER_ACCESS_TOKEN_SECRET = ''
   
    NASA_API_KEY = ''
   
    # for watson granite models
   
    apikey = ''
   
    watson_url = ''
   
    project_id = ''
   
    HF_TOKEN = ''

Running the Application

5. Run the Streamlit application:

   streamlit run app.py

Project Structure

• app.py: The main entry point of the application. It sets up the navigation and page configuration.

• views/dashboard.py: Contains the code for the dashboard page, which provides an overview of the disaster-related data.

• views/affected_areas.py: Contains the code for visualizing affected areas using GeoJSON data.

• views/visualizations.py: Contains various visualizations related to disaster data.

• views/donations.py: Tracks and displays donation information.

• views/chatbot.py: Implements a chatbot for user interaction.

• views/sos.py: Displays SOS messages and related information.

Purpose of Each Python File

• dashboard.py: Provides a summary and key metrics related to disasters.
• affected_areas.py: Downloads and visualizes GeoJSON data to show affected areas.
• visualizations.py: Generates charts and graphs to visualize disaster data.
• donations.py: Manages and displays information about donations.
• chatbot.py: Implements a chatbot to assist users with queries.
• sos.py: Displays emergency SOS messages and alerts taken from mobile devices of users.

IBM Technologies Used

• IBM IAM
• IBM Cloudant
• IBM Secrets Manager
• Natural Language Understanding
• IBM Watsonx.ai Studio
• IBM Watsonx.ai Runtime
• IBM Watsonx.ai Assistant
• IBM Internet Services
• IBM Cloud Object Storage
• IBM AutoML
• IBM Event Notifications

Additional Information

For more details, please refer to the individual Python files and their respective comments and documentation.

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Feel free to contribute to this project by submitting issues or pull requests. For any questions, please contact the project contributors.