⚡Streamr Node Dashboard App⚡

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Streamr Node Dashboard is an application built using Streamlit inspired by BrubeckScan, a Streamr node and rewards monitoring dApp built and maintained by Streamr community member Adam Phi Vo. The application is built around the concept of a Streamr Node, an entity in the network that processes and stores data.

This makes it an essential tool for anyone interested in or working with the Streamr Network, including node operators.

Deployed Streamlit App

Click to view 👇🏾

Features

• Ethereum Address Input: Simply input the Ethereum address of a Streamr node to start fetching data.
• Detailed Node Information: The application displays comprehensive information about a node including its status, staked $DATA, to be received rewards, total rewards, claim count, and claim percentage.
• Reward Payouts and Reward Codes: It also presents the reward payouts and the latest claimed reward codes in a well-organized, easy-to-read format.
• Efficient Data Fetching: The application employs multi-threading to fetch data, ensuring that all data is fetched in an efficient and timely manner.
• User Timezone Selection: Users can select their own timezone for the display of time-related information.

Installation

To set up the Streamr BrubeckScan Dashboard App by replicating this project, follow the steps below:

1. Python: Ensure that you have Python 3.7 or later installed on your machine.

2. Clone the Repository: Clone this repository to your local machine:

   git clone https://github.com/tonykipkemboi/StreamrDashboard.git

3. Install Requirements: Navigate to the cloned repository and install the necessary Python packages using pip:

   pip install -r requirements.txt

Usage

1. Running the Application: To run the Streamlit app, use the command:

   streamlit run streamr_app.py

2. Accessing the Dashboard: Open your web browser and navigate to http://localhost:8501 to interact with the app.

3. Entering Node Address: Enter the Ethereum address of the Streamr node for which you want to fetch and display data; I provided an example for testing!

4. Viewing Data: The fetched data will be displayed in an organized and user-friendly manner. You can also select your timezone for the display of time-related data.

With these steps, you can easily and efficiently fetch, visualize, and analyze data for any Streamr node in the network.

If you're feeling generous, please buy me coffee by scanning the QR code below

Thanks for the coffee ☕