# Connecting Streamlit In this article, we will walk through the process of building a dashboard using Streamlit. To make it more illustrative, we will create a dashboard that connects to the analytical database and monitors the real-time status of vehicles. {% hint style="info" %} This guide is part of the **IoT Query** documentation suite and specifically covers connecting Power BI to your data warehouse. If you're still deciding which BI tool to use, refer to the [Selecting BI tools](/docs/analytics/iot-query/connection-setup/selecting-bi-tools.md) overview. {% endhint %} ## Dashboard features * Display total number of objects * Visualize movement statuses (moving/stopped/parked) * Visualize connection statuses (active/idle/offline) * Detailed table with current status of all vehicles * Filtering by vehicle type, group, movement status, and connection status * Automatic data refresh every 5 minutes * Toggle between light and dark themes ## Technical requirements * Python 3.8+ * Internet access for database connection * Minimum 2 GB RAM ## Installation and setup ### 1. Clone the repository ```sh git clone https://github.com/SquareGPS/bi-intergrations.git ``` ### 2. Create a virtual environment ``` # Windows python -m venv venv venv\Scripts\activate # Linux/macOS python -m venv venv source venv/bin/activate ``` {% hint style="danger" %} Make sure you have Python 3.8 or higher installed. You can check the version with the command `python --version`. {% endhint %} ### 3. Install dependencies After activating the virtual environment, install all necessary libraries: ```sh pip install -r requirements.txt ``` ## Database connection ### 1. Create a configuration file Create a `.env` file in the project's root directory: ``` DB_HOST=your_db_host DB_NAME=your_db_name DB_USER=your_db_user DB_PASS=your_db_password DB_PORT=5432 DB_SCHEMA=raw_business_data ``` #### Connection parameter reference
Lakehouse ParameterStreamlit Setting LocationNotes
HostDB_HOST in .env fileThe database server address provided in your welcome email
PortDB_PORT in .env fileDefault is 5432 for PostgreSQL
Database nameDB_NAME in .env fileYour assigned database name
UsernameDB_USER in .env fileYour database username
PasswordDB_PASS in .env fileYour secure database password
SSL modeConnection string in Python codeSet to require in the connection string
SchemaDB_SCHEMA in .env fileSpecify schema (raw_business_data or raw_telematics_data)
### 2. Obtaining credentials Request credentials for connecting to the demonstration database by contacting the administrator. {% hint style="info" %} The `.env` file should not be included in version control (GitHub) to ensure credentials security. The `.gitignore` file is already configured to exclude this file. {% endhint %} ## Running the dashboard After setting up the database connection, start the dashboard with the command: ``` streamlit run moving_status_dashboard.py ``` After launching, you'll see a message similar to: ``` You can now view your Streamlit app in your browser. Local URL: http://localhost:8501 Network URL: http://192.168.1.5:8501 ``` Open the specified URL in your browser. The dashboard will be available at (or at the network URL if you want to open it from another device on the network). ## Developing custom components If you want to modify the dashboard or create new components: ### 1. Modifying the existing dashboard Streamlit automatically reloads the application when you change the source code. Simply edit the `moving_status_dashboard.py` file and save your changes. ### 2. Adding new visualizations To add new charts and diagrams, use libraries: * Plotly: `import plotly.express as px` or `import plotly.graph_objects as go` * Built-in Streamlit visualizations: `st.bar_chart()`, `st.line_chart()`, etc. Example of adding a new chart: ```python import plotly.express as px # Get data from the database df = ... # your database query # Create chart fig = px.pie(df, values='count', names='status', title='Vehicle Statuses') st.plotly_chart(fig, use_container_width=True) ``` ### 3. Debugging For debugging, use ```python # Output to Streamlit interface st.write(f"Debug: {your_variable}") # Output to console print(f"Console debug: {your_variable}") # Extended data output st.json(data_dict) st.dataframe(pandas_dataframe) ``` ## Troubleshooting ### Database connection issues * **Connection error:** Check the correctness of credentials in the `.env` file and database availability * **SSL error:** Make sure your IP is on the allowlist for database access * **Timeout errors:** Check your internet connection stability and firewall settings ### Dependency issues **Error installing psycopg2-binary:** * Windows: `pip install pipwin && pipwin install psycopg2-binary` * Linux: `sudo apt install python3-dev libpq-dev` * macOS: `brew install postgresql` **Dependency conflicts:** * Create a new virtual environment * Install dependencies one by one, starting with streamlit ### Other issues Here are some tricks that can help you fix common issues: 1. Update dependencies: `pip install -r requirements.txt --upgrade` 2. Check Python compatibility: `python --version` (should be 3.8+) 3. When changing code, include debug messages: ``` st.write(f"Debug: {your_variable}") ``` 4. Streamlit cache errors: stop the application and run with the `--clear_cache` flag: ``` streamlit run moving_status_dashboard.py --clear_cache ``` ## Next steps After successfully connecting Power BI to your Private Telematics Lakehouse instance, we recommend you to: * Explore the available data schemas by reviewing the [Schema overview](/docs/analytics/iot-query/schema-overview.md) section to better understand the data structure and relationships. * Start with simple queries focused on specific business entities before building complex dashboards - check our [example queries](/docs/analytics/example-queries.md) for reference. ### **Support** For technical questions or requests for access to the demonstration database, please contact: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://navixy.com/docs/analytics/iot-query/connection-setup/selecting-bi-tools/connecting-streamlit.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.