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https://github.com/gaiborjosue/hackathon_2023

This repository provides a Streamlit template that you can use to showcase your machine learning models during the hackathon. The template allows you to create a user-friendly web interface where you can demonstrate your models and perform live predictions.
https://github.com/gaiborjosue/hackathon_2023

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This repository provides a Streamlit template that you can use to showcase your machine learning models during the hackathon. The template allows you to create a user-friendly web interface where you can demonstrate your models and perform live predictions.

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## **Using the Streamlit Template**



1. The provided code is a Streamlit app template that can be used to showcase a machine learning model in a hackathon.

2. The template uses the Streamlit library to create a user-friendly web interface for the model.

3. You need to have Streamlit and other necessary dependencies installed. You can install them by running **pip install streamlit joblib pandas** in your terminal or command prompt.

4. Ensure that you have a trained model saved as **model.joblib** in the same directory as your Streamlit script.

5. Customize the template according to your requirements:

   1. Import any additional dependencies you need for your model.

   2. Modify the code in the **single\_predict** and **batch\_predict** functions to fit your model's prediction logic.

   3. Update the sidebar title, project description, and the input fields as needed.

6. To run the app locally, navigate to the project's directory in your terminal and execute the following command: **streamlit run **, replacing **** with the name of your Streamlit script file.

7. The app will start running locally, and you can access it by opening the provided local URL in your web browser.



## **Additional Streamlit Features**



- The template provides a basic structure, but Streamlit offers many more features to enhance your app. You can refer to the [Streamlit documentation](https://docs.streamlit.io/) to explore additional functionalities, such as displaying images, creating interactive visualizations, adding sliders and buttons, and much more.

- Streamlit supports various interactive widgets, such as sliders, checkboxes, and select boxes, which can be used to enhance user interaction with the app.

- You can customize the appearance of your app using the **st.set\_page\_config** function to set the page title and favicon, as well as the **st.sidebar** function to create a sidebar with additional options or information.



## **Deploying the Streamlit App**



1. Streamlit offers a hosting service called Streamlit Sharing, where you can deploy your app for free.

2. To deploy your app to Streamlit Sharing, you can follow these steps:

   - Install the **streamlit** package by running **pip install streamlit** if you haven't done so already.

   - Create a GitHub repository containing your Streamlit script and any necessary files.

   - Sign in to [https://share.streamlit.io](https://share.streamlit.io/) with your GitHub account.

   - Follow the instructions provided on the Streamlit Sharing website to deploy your app.

   - Once deployed, Streamlit Sharing will provide you with a public URL where your app is accessible to anyone.



## **Example Deployment**



- You can find a deployed example of the template at .

- Participants can visit the link to see the deployed Streamlit app in action and get an idea of how their own apps could look when deployed.