Disaster Classifier

This project classifies disaster-related messages into relevant categories to assist in emergency response. Follow the steps below to set up, train the model, and deploy a Streamlit web application for inference.

Setup Instructions

1. Clone the Repository

git clone https://github.com/sadavaidya/Disaster_classifier.git
cd Disaster_classifier

2. Create a Conda Virtual Environment

Ensure you have Conda installed. Then, create and activate the virtual environment:

conda create --name disaster_classifier python=3.8
conda activate disaster_classifier

3. Install Required Packages

Install the necessary dependencies:

pip install -r requirements.txt

Running the Data Processing Pipeline

Execute the pipeline script to process data and train the model:

python src/pipeline.py

This step will generate the trained model and save it in the models directory.

Running the Streamlit Application for Inference

To deploy the model using Streamlit:

1. Ensure the trained model (classifier.pkl) is available in the models directory.

2. Run the Streamlit app:

   streamlit run app.py

This command will launch a web interface where users can enter a message and receive a classification. 3. Sample screenshot of how streamlit app will look, can be found in the repository as demp_streamlit_app.png

Notes

• The Streamlit app (app.py) is included in this repository.
• Modify app.py as needed to customize the UI or enhance functionality.
• Ensure all dependencies are installed before running the app.