Cleanup/streamlit integration

README.md

@@ -106,6 +106,7 @@ For a detailed, hands-on introduction to the project, please see our quickstart
 | **Snowflake Horizon Catalog [ FMCG ]** | [`quickstart_fmcg_snowflake.ipynb`](notebooks/quickstart_fmcg_snowflake.ipynb) | Snowflake Notebook Only |
 | **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](notebooks/quickstart_native_snowflake.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) |
 | **Native Databricks with AI/BI Genie [ Tech Manufacturing ]** | [`quickstart_native_databricks.ipynb`](notebooks/quickstart_native_databricks.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) |
+| **Streamlit App**       | [`quickstart_streamlit.ipynb`](notebooks/quickstart_streamlit.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_streamlit.ipynb) |
 
 These datasets will take you through the following steps:
 
@@ -290,11 +291,13 @@ You can launch the Streamlit application using the `intugle-mcp` command or `uvx
 ```bash
 intugle-streamlit
 # Or using uvx
-uvx --from intugle intugle-streamlit
+uvx --from intugle[streamlit] intugle-streamlit
 ```
 
 Open the URL provided in your terminal (usually `http://localhost:8501`) to access the application. For more details, refer to the [Streamlit App documentation](https://intugle.github.io/data-tools/docs/streamlit-app).
 
+To run the app in a cloud environment like Google Colab, please refer to our [Streamlit quickstart notebook](notebooks/quickstart_streamlit.ipynb).
+
 ## Community
 
 Join our community to ask questions, share your projects, and connect with other users.

docsite/docs/examples.md

@@ -17,6 +17,7 @@ For a detailed, hands-on introduction to the project, please see our quickstart
 | **Snowflake Horizon Catalog [ FMCG ]** | [`quickstart_fmcg_snowflake.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_fmcg_snowflake.ipynb) | Snowflake Notebook Only |
 | **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) |
 | **Native Databricks with AI/BI Genie [ Tech Manufacturing ]** | [`quickstart_native_databricks.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) |
+| **Streamlit App**       | [`quickstart_streamlit.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_streamlit.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_streamlit.ipynb) |
 
 These datasets will take you through the following steps:
 

docsite/docs/streamlit-app.md

@@ -49,7 +49,7 @@ You can launch the Streamlit application using the `intugle-streamlit` command o
 ```bash
 intugle-streamlit
 # Or using uvx
-uvx --from intugle intugle-streamlit
+uvx --from intugle[streamlit] intugle-streamlit
 ```
 
 Open the URL provided in your terminal (usually `http://localhost:8501`) to access the application.
@@ -64,3 +64,9 @@ The application guides you through a simple, multi-step process, which is tracke
 4.  **Build Semantic Model**: After preparing your data, click **"Create Semantic Model"**. You will be prompted to provide a "domain" (e.g., *Healthcare*, *Manufacturing*) to give the LLM context. The app will then profile the data and generate a business glossary for each table.
 5.  **Predict Links**: Once profiling is complete, click **"Run Link Prediction"** to discover the relationships between your datasets.
 6.  **Explore & Download**: View the results as an interactive graph or a detailed table. You can download the underlying YAML configuration files from the sidebar at any time.
+
+## Running on Google Colab
+
+To run the Streamlit app in a cloud environment like Google Colab, which requires exposing the server via a public URL, please refer to our quickstart notebook:
+
+-   [**Streamlit Quickstart Notebook**](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_streamlit.ipynb)

notebooks/quickstart_streamlit.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a6f5a26",
+   "metadata": {},
+   "source": [
+    "# Running the Intugle Streamlit App on Google Colab\n",
+    "\n",
+    "This notebook guides you through the process of running the Intugle semantic model builder, which is a Streamlit application, directly within a Google Colab environment.\n",
+    "\n",
+    "Since Colab provides a containerized environment, we cannot directly access the web server started by Streamlit. To solve this, we will use `ngrok`, a tool that creates a secure public URL (a \"tunnel\") to the Streamlit application running inside our Colab instance.\n",
+    "\n",
+    "**Steps:**\n",
+    "\n",
+    "1.  **Install** the required Python packages.\n",
+    "2.  **Configure and run `ngrok`** to expose the Streamlit app.\n",
+    "3.  **Shut down** the tunnel when you are finished."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c20194c1",
+   "metadata": {},
+   "source": [
+    "## 1. Install Dependencies\n",
+    "\n",
+    "First, we need to install the `intugle` library. We use the `[streamlit]` extra, which ensures that Streamlit and all other necessary dependencies for the web app are installed at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b26de4ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install intugle[streamlit]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df8720f3",
+   "metadata": {},
+   "source": [
+    "## 2. Configure and Run the Streamlit App\n",
+    "\n",
+    "Next, we will start the Intugle Streamlit app and expose it to the web using `ngrok`.\n",
+    "\n",
+    "**Important:** You need a free `ngrok` authentication token for this step.\n",
+    "\n",
+    "1.  Go to the [ngrok Dashboard](https://dashboard.ngrok.com/get-started/your-authtoken) and sign up for a free account.\n",
+    "2.  Copy your authentication token.\n",
+    "3.  Paste the token into the `PYNGROK_TOKEN` variable in the code block below, replacing `<your-key>`.\n",
+    "\n",
+    "The script will then:\n",
+    "1.  Set your `ngrok` auth token.\n",
+    "2.  Start the Intugle Streamlit server in the background on its default port (`8501`).\n",
+    "3.  Create a secure public URL that tunnels traffic to the Streamlit server.\n",
+    "\n",
+    "Once you run the cell, click the public URL that is printed to access the application in your browser.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4564ff48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyngrok import ngrok\n",
+    "\n",
+    "# TODO: Replace <your-key> with your actual ngrok authentication token.\n",
+    "# You can get a free token from https://dashboard.ngrok.com/get-started/your-authtoken\n",
+    "PYNGROK_TOKEN = \"<your-key>\"\n",
+    "\n",
+    "# Set authentication token\n",
+    "ngrok.set_auth_token(PYNGROK_TOKEN)\n",
+    "\n",
+    "# Start the Intugle Streamlit server in the background on port 8501\n",
+    "# The `nohup` command ensures it keeps running, and `&` runs it as a background process.\n",
+    "!nohup intugle-streamlit &\n",
+    "\n",
+    "# Start an ngrok tunnel to expose the Streamlit server\n",
+    "# This connects to the default Streamlit port (8501)\n",
+    "ngrok_tunnel = ngrok.connect(addr='8501', proto='http', bind_tls=True)\n",
+    "\n",
+    "# Print the public URL to access the app\n",
+    "print(' * Tunnel URL:', ngrok_tunnel.public_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "808123d4",
+   "metadata": {},
+   "source": [
+    "## 3. Shut Down the Tunnel\n",
+    "\n",
+    "When you are finished using the application, it is good practice to close the `ngrok` tunnel to release the resources. The following script will find all active tunnels and disconnect them.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fb8abf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Close all active tunnels\n",
+    "from pyngrok import ngrok\n",
+    "\n",
+    "active_tunnels = ngrok.get_tunnels()\n",
+    "for tunnel in active_tunnels:\n",
+    "    ngrok.disconnect(tunnel.public_url)\n",
+    "    print(f\"Closed tunnel: {tunnel.public_url}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/intugle/streamlit_app/.streamlit/config.toml

@@ -5,12 +5,4 @@ primaryColor = "#8B5CF6"
 backgroundColor = "#ffffff"
 secondaryBackgroundColor = "#F7F5FF"
 textColor = "#111827"
-font = "sans serif"
-
-[smtp]
-host = "smtp.gmail.com"
-port = 465
-username = "hello@intugle.ai"
-password = "1234"
-from_email = "hello@intugle.ai"
-from_name = "Intugle Bot"
+font = "sans serif"