Climate Change Forecast

Overview

The Climate Change Forecast tool provides an interactive graphical interface for analyzing climate change trends in a selected region. It enables users to visualize historical climate data and forecast potential changes influenced by global warming. This tool is designed to support informed decision-making and enhance climate awareness through predictive analytics.

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Installation & Setup

Prerequisites

Ensure the following are installed before proceeding:

• Enable Virtualization in your system's BIOS
• Docker Desktop (Download and sign in)
• VS Code with the following extensions:
  • Docker
  • Dev Containers

Setup Instructions

1. Clone this repository:

   git clone https://github.com/caleb-j-kim/climate-change-forecast.git
   cd climate-change-forecast

2. Install dependencies:

   pip install -r requirements.txt

3. Open VS Code and install the necessary extensions.
4. Reopen the project in a Dev Container:
   • Press Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (Mac)
   • Type: Remote-Containers: Reopen in Container
5. Set up a terminal connection with Docker:
   • Install Ubuntu and configure remote connections in VS Code.
   • Run the following command in the terminal (Windows only):

     wsl --install --no-distribution

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Running the Application

Build the Docker Image

Ensure Docker Desktop is running before executing the following command:

docker build -t climate-change-forecast .

To force a fresh build (if dependencies or the Dockerfile have been updated):

docker build --no-cache -t climate-change-forecast .

Run the Application

Run the application using one of the following methods:

1. Standard execution:

   docker run -p 5000:5000 climate-change-forecast

2. Interactive mode:

   docker run -it -p 5000:5000 climate-change-forecast bash

   Then, manually start Flask:

   python -m flask run --host=0.0.0.0

3. Testing changes while developing:

   docker run -p 5000:5000 -v "$(pwd)":/app climate-change-forecast

4. AWS Run

docker run -e AWS_ACCESS_KEY_ID=putKeyHere \
           -e AWS_SECRET_ACCESS_KEY=putKeyHere \
           -e AWS_DEFAULT_REGION=us-east-2 \
           -p 5050:5000 climate-change-forecast

5. Docker Bash

   docker run -it \

-e AWS_ACCESS_KEY_ID=putKeyHere
-e AWS_SECRET_ACCESS_KEY=putSecretKeyHere
-e AWS_DEFAULT_REGION=us-east-2
climate-change-forecast /bin/bash

---

## Git Workflow
### Committing and Pushing Changes
1. Stage changes:
```sh
git add .

2. Commit changes:

   git commit -m "Describe changes here"

3. Push to the remote repository:

   git push

---

Backend Testing

1. Train models:

   Invoke-RestMethod -Method GET -Uri "http://localhost:5000/train"
   

2. Test models:

   Invoke-RestMethod -Method GET -Uri "http://localhost:5000/test"
   

3. Predict climate:

Invoke-RestMethod -Method POST -Uri "http://localhost:5000/predict" -Body '{"dataset": "country", "year": 2020, "month": 07, "location": "United States"}' -ContentType "application/json"

Invoke-RestMethod -Method POST -Uri "http://localhost:5000/predict" `
 -Body '{"dataset": "city", "year": 2000, "month": 7, "location": {"city": "New York", "country": "United States"}}' `
 -ContentType "application/json"

``` sh
Invoke-RestMethod -Method POST -Uri "http://localhost:5000/predict" `
 -Body '{"dataset": "state", "year": 2000, "month": 7, "location": {"state": "Virginia", "country": "United States"}}' `
 -ContentType "application/json"

4. Tomorrow.io tests:
``` sh
Invoke-RestMethod http://localhost:5000/weather/forecast/random_country |
ConvertTo-Json -Depth 5

``` sh
Invoke-RestMethod http://localhost:5000/weather/forecast/random_city    |
ConvertTo-Json -Depth 5

``` sh
Invoke-RestMethod http://localhost:5000/weather/forecast/random_state   |
ConvertTo-Json -Depth 5