# 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](https://www.navixy.com/docs/analytics/iot-query/connection-setup/selecting-bi-tools) 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

<table><thead><tr><th width="188.81817626953125">Lakehouse Parameter</th><th>Streamlit Setting Location</th><th>Notes</th></tr></thead><tbody><tr><td><strong>Host</strong></td><td><code>DB_HOST</code> in <code>.env</code> file</td><td>The database server address provided in your welcome email</td></tr><tr><td><strong>Port</strong></td><td><code>DB_PORT</code> in <code>.env</code> file</td><td>Default is 5432 for PostgreSQL</td></tr><tr><td><strong>Database name</strong></td><td><code>DB_NAME</code> in <code>.env</code> file</td><td>Your assigned database name</td></tr><tr><td><strong>Username</strong></td><td><code>DB_USER</code> in <code>.env</code> file</td><td>Your database username</td></tr><tr><td><strong>Password</strong></td><td><code>DB_PASS</code> in <code>.env</code> file</td><td>Your secure database password</td></tr><tr><td><strong>SSL mode</strong></td><td>Connection string in Python code</td><td>Set to <strong>require</strong> in the connection string</td></tr><tr><td><strong>Schema</strong></td><td><code>DB_SCHEMA</code> in <code>.env</code> file</td><td>Specify schema (<strong>raw_business_data</strong> or <strong>raw_telematics_data</strong>)</td></tr></tbody></table>

### 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 <http://localhost:8501> (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](https://www.navixy.com/docs/analytics/iot-query/schema-overview) 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](https://www.navixy.com/docs/analytics/example-queries) for reference.

### **Support**

For technical questions or requests for access to the demonstration database, please contact: <support@squaregps.com>