Spaces:
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| """ | |
| Tasty Bytes Cortex Analyst - Demo Space | |
| ======================================== | |
| An interactive showcase of the Tasty Bytes Customer Analytics Cortex Analyst agent. | |
| This Gradio app demonstrates: | |
| - Natural language querying capabilities | |
| - Example queries and results | |
| - Architecture and setup instructions | |
| - Live demo video | |
| Note: This is a demo/showcase space. The actual Cortex Analyst runs on Snowflake infrastructure. | |
| To try it yourself, follow the setup instructions in the GitHub repository. | |
| """ | |
| import gradio as gr | |
| import pandas as pd | |
| # Sample data for demonstration | |
| sample_customer_counts = pd.DataFrame([ | |
| {"Rank": 1, "Country": "United States", "Customers": 5420, "Total Sales": "$1,250,890.75"}, | |
| {"Rank": 2, "Country": "India", "Customers": 3890, "Total Sales": "$980,230.50"}, | |
| {"Rank": 3, "Country": "Egypt", "Customers": 2110, "Total Sales": "$520,450.00"}, | |
| ]) | |
| sample_top_customers = pd.DataFrame([ | |
| {"Customer ID": 110913, "Name": "Anna Sanchez", "City": "Boston", "Total Sales": "$3,302.00"}, | |
| {"Customer ID": 130576, "Name": "Grace Kline", "City": "Mumbai", "Total Sales": "$2,809.50"}, | |
| {"Customer ID": 90298, "Name": "Dahlia Buchanan", "City": "Cairo", "Total Sales": "$1,745.75"}, | |
| ]) | |
| example_queries = [ | |
| ("How many customers are in our loyalty program?", "SELECT COUNT(DISTINCT customer_id) FROM customer_loyalty_metrics_v", "11,420 customers"), | |
| ("Which countries have the most customers?", "SELECT country, COUNT(DISTINCT customer_id) FROM customer_loyalty_metrics_v GROUP BY country ORDER BY COUNT(DISTINCT customer_id) DESC", "See table below"), | |
| ("Show me the top 5 customers by total sales", "SELECT customer_id, first_name, last_name, city, total_sales FROM customer_loyalty_metrics_v ORDER BY total_sales DESC LIMIT 5", "See table below"), | |
| ("What's the total sales by country?", "SELECT country, SUM(total_sales) FROM customer_loyalty_metrics_v GROUP BY country ORDER BY SUM(total_sales)", "See results in table"), | |
| ] | |
| def create_demo(): | |
| with gr.Blocks(theme=gr.themes.Soft(), title="Tasty Bytes Cortex Analyst Demo") as demo: | |
| gr.Markdown(""" | |
| # 🍔 Tasty Bytes Customer Analytics - Cortex Analyst Demo | |
| **Natural Language Interface for Customer Loyalty Data** | |
| This demo showcases a Snowflake Cortex Analyst agent built on the TASTY_BYTES public dataset. | |
| Ask questions in plain English and get instant insights - no SQL knowledge required! | |
| ⚠️ **Note**: This is a demonstration space. To try the actual agent, follow the setup instructions in the GitHub repository. | |
| """) | |
| # Main demo section | |
| with gr.Tab("🎥 Demo Video"): | |
| gr.Markdown(""" | |
| ## Watch the Full Demo | |
| See Cortex Analyst in action: natural language queries, automatic SQL generation, and instant results. | |
| """) | |
| # Placeholder for video (you'll upload your actual demo video) | |
| gr.Markdown(""" | |
| 📹 **Demo video coming soon!** | |
| The video will demonstrate: | |
| - Asking questions in natural language | |
| - Automatic SQL generation by Cortex Analyst | |
| - Real-time results and insights | |
| - Various query types (counts, aggregations, rankings) | |
| **Until then, explore the other tabs to see example queries and results!** | |
| """) | |
| # Example queries tab | |
| with gr.Tab("💡 Example Queries"): | |
| gr.Markdown(""" | |
| ## Try These Questions | |
| Here are some examples of natural language queries you can ask the Cortex Analyst agent: | |
| """) | |
| for i, (question, sql, result) in enumerate(example_queries, 1): | |
| with gr.Accordion(f"Example {i}: {question}", open=(i==1)): | |
| gr.Markdown(f"**Natural Language Query:**") | |
| gr.Code(question, language=None) | |
| gr.Markdown(f"**Generated SQL:**") | |
| gr.Code(sql, language="sql") | |
| gr.Markdown(f"**Result:**") | |
| gr.Markdown(f"`{result}`") | |
| gr.Markdown(""" | |
| ### More Example Questions: | |
| **Customer Analytics:** | |
| - "How many customers do we have in total?" | |
| - "Show me all customers from Boston" | |
| - "Which customer has the highest total sales?" | |
| - "List customers who have visited more than 50 locations" | |
| **Geographic Analysis:** | |
| - "What cities have the most customers?" | |
| - "Compare customer counts across countries" | |
| - "Show me the geographic distribution of our customers" | |
| **Sales Insights:** | |
| - "What's the average sales per customer?" | |
| - "Which country generates the most revenue?" | |
| - "Show me total sales by city" | |
| - "Who are our top 10 customers by spend?" | |
| """) | |
| # Sample results tab | |
| with gr.Tab("📊 Sample Results"): | |
| gr.Markdown(""" | |
| ## Example Query Results | |
| Here's what the actual results look like when you query the agent: | |
| """) | |
| gr.Markdown("### Query: 'Which countries have the highest number of customers?'") | |
| gr.Dataframe(sample_customer_counts, label="Customer Count by Country") | |
| gr.Markdown("---") | |
| gr.Markdown("### Query: 'Show me the top 3 customers by total sales'") | |
| gr.Dataframe(sample_top_customers, label="Top Customers") | |
| gr.Markdown(""" | |
| --- | |
| 💡 **Tip**: All numeric values are automatically rounded to 2 decimal places as specified | |
| in the semantic view's custom instructions. | |
| """) | |
| # Architecture tab | |
| with gr.Tab("🏗️ Architecture"): | |
| gr.Markdown(""" | |
| ## System Architecture | |
| The Cortex Analyst agent uses a layered architecture to transform natural language | |
| into actionable insights: | |
| """) | |
| gr.Markdown(""" | |
| ``` | |
| ┌─────────────────────────────────────────┐ | |
| │ Business User │ | |
| │ (Natural Language Questions) │ | |
| └──────────────┬──────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────┐ | |
| │ Cortex Analyst (AI Layer) │ | |
| │ • Understands natural language │ | |
| │ • Maps questions to business concepts │ | |
| │ • Generates SQL queries │ | |
| └──────────────┬──────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────┐ | |
| │ Semantic View (Business Logic) │ | |
| │ • Business-friendly names │ | |
| │ • Metrics & dimensions │ | |
| │ • Pre-verified queries │ | |
| │ • Custom instructions │ | |
| └──────────────┬──────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────┐ | |
| │ TASTY_BYTES Dataset │ | |
| │ • Customer loyalty data │ | |
| │ • Order history │ | |
| │ • Geographic information │ | |
| └─────────────────────────────────────────┘ | |
| ``` | |
| """) | |
| gr.Markdown(""" | |
| ### Key Components: | |
| **1. Data Layer** - Snowflake TASTY_BYTES public dataset | |
| - Customer loyalty program data | |
| - Order transactions | |
| - Location and geographic information | |
| **2. Semantic Layer** - Native Snowflake Semantic View | |
| - Translates technical column names to business terms | |
| - Defines metrics (e.g., "total sales") and dimensions (e.g., "country") | |
| - Includes verified query examples | |
| - Enforces data access controls | |
| **3. AI Layer** - Cortex Analyst | |
| - Powered by LLM (Large Language Model) | |
| - Understands natural language intent | |
| - Automatically generates SQL queries | |
| - Returns results in business-friendly format | |
| **4. Interface** - Snowflake Intelligence & Cortex Analyst UI | |
| - Chat-based interaction | |
| - Visual result presentation | |
| - Query history and refinement | |
| """) | |
| # Setup guide tab | |
| with gr.Tab("🚀 Setup Guide"): | |
| gr.Markdown(""" | |
| ## How to Set This Up Yourself | |
| Follow these steps to create your own Cortex Analyst agent with the TASTY_BYTES dataset: | |
| ### Prerequisites | |
| - Snowflake account (free trial available at [signup.snowflake.com](https://signup.snowflake.com)) | |
| - ACCOUNTADMIN role or equivalent permissions | |
| - Cortex Analyst feature enabled (available in most regions) | |
| ### Step 1: Clone the Repository | |
| ```bash | |
| git clone https://github.com/aamanlamba/tasty-bytes-cortex-analyst.git | |
| cd tasty-bytes-cortex-analyst | |
| ``` | |
| ### Step 2: Load the TASTY_BYTES Dataset | |
| ```sql | |
| -- Execute in Snowflake Snowsight or your SQL client | |
| -- This creates the database, schemas, and loads sample data | |
| @scripts/load_tasty_bytes_data.sql | |
| ``` | |
| ⏱️ Takes approximately 5-10 minutes | |
| ### Step 3: Create the Semantic View | |
| ```sql | |
| -- Execute the semantic view creation script | |
| @scripts/create_semantic_view.sql | |
| ``` | |
| ⏱️ Takes less than 1 minute | |
| ### Step 4: Create the Cortex Analyst Agent | |
| 1. Navigate to **AI & ML** → **Cortex Analyst** in Snowsight | |
| 2. Click **Create new agent** | |
| 3. Configure: | |
| - **Name**: Tasty Bytes Customer Analytics | |
| - **Description**: Natural language interface for customer data | |
| 4. Add Tool: | |
| - **Type**: Semantic View | |
| - **View**: `HARMONIZEDCUSTOMERMETRICSSEMANTICVIEW` | |
| - **Description**: | |
| ``` | |
| Use this tool to answer questions about Tasty Bytes customer | |
| loyalty program metrics, including customer demographics, | |
| total sales, and location visit patterns. | |
| ``` | |
| 5. Click **Create agent** | |
| ### Step 5: Test Your Agent | |
| Try asking: | |
| - "How many customers are in our loyalty program?" | |
| - "Which countries have the most customers?" | |
| - "Show me the top 10 customers by sales" | |
| ### 📚 Full Documentation | |
| For detailed instructions, troubleshooting, and advanced customization: | |
| - [GitHub Repository](https://github.com/aamanlamba/tasty-bytes-cortex-analyst) | |
| - [Snowflake Cortex Analyst Docs](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) | |
| - [Semantic Views Guide](https://docs.snowflake.com/en/user-guide/views-semantic/overview) | |
| """) | |
| # About tab | |
| with gr.Tab("ℹ️ About"): | |
| gr.Markdown(""" | |
| ## About This Project | |
| This demo showcases **Snowflake Cortex Analyst**, an AI-powered natural language | |
| interface for querying structured data. The project uses the publicly available | |
| TASTY_BYTES sample dataset from Snowflake. | |
| ### Dataset | |
| **TASTY_BYTES** represents a fictional global food truck franchise with: | |
| - 11,420+ customer loyalty program members | |
| - Order transaction history | |
| - Multiple countries and cities | |
| - Customer demographics and preferences | |
| ### Features Demonstrated | |
| - ✅ Natural language to SQL translation | |
| - ✅ Automatic query generation | |
| - ✅ Business-friendly semantic layer | |
| - ✅ Pre-verified query examples | |
| - ✅ Custom SQL generation instructions | |
| ### Use Cases | |
| This pattern applies to many industries: | |
| - **Retail**: Customer segmentation, sales analysis | |
| - **Finance**: Client analytics, transaction patterns | |
| - **Healthcare**: Patient demographics, visit patterns | |
| - **SaaS**: User engagement, feature adoption | |
| - **E-commerce**: Customer lifetime value, purchase behavior | |
| ### Author | |
| **Aaman Lamba** | |
| - Strategy Consultant & Author | |
| - AI Governance & Data Economy Expert | |
| - Former Senior Industry Principal, Infosys | |
| - IAPP Certified AI Governance Professional (in progress) | |
| ### Resources | |
| - 📦 [GitHub Repository](https://github.com/aamanlamba/tasty-bytes-cortex-analyst) | |
| - 📖 [Snowflake Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) | |
| - 💼 [LinkedIn](https://linkedin.com/in/aamanlamba) | |
| ### License | |
| MIT License - Open source and free to use | |
| --- | |
| ⭐ **Like this project?** Star it on [GitHub](https://github.com/aamanlamba/tasty-bytes-cortex-analyst)! | |
| 🐛 **Found an issue?** [Report it here](https://github.com/aamanlamba/tasty-bytes-cortex-analyst/issues) | |
| """) | |
| return demo | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo = create_demo() | |
| demo.launch() | |