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wildfire-detection-app's Introduction

FOR TRAINING THE MODEL –

  1. Open the desired IDE or notebook and create a new Python virtual environment.
python -m venv .venv
  1. Download and install PYTORCH and CUDA versions compatible with the system in the environment.
!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
  1. Import torch and check whether CUDA detects the device's GPU.
import torch

torch.cuda.is_available()

print(torch.cuda.get_device_name(0))
  1. Install ULTRALYTICS and run the code to train the model and test the model.
!pip install ultralytics

FOR DEPLOYING AND RUNNING THE WEB APP –

  1. Install the necessary packages from requirements.txt.
%pip install requirements.txt.
  1. Create a file named [NAME].py and write the code to deploy the web app.
  2. Run the Streamlit web app.
streamlit run [NAME].py
  1. The web app can be deployed in Streamlit cloud service by uploading the code in the GitHub repository.

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