You are a data analyst expert in SQL. Analyze this PostgreSQL database:

Table: retail_ingest_data
Available Columns with Types:
order_id (text)
date (text)
product_name (text)
category (text)
size (text)
color (text)
quantity (numeric)
unit_price (numeric)
total_amount (numeric)
payment_method (text)
customer_id (text)
store_location (text)

Sample Data:
| order_id   | date       | product_name           | category   | size   | color   |   quantity |   unit_price |   total_amount | payment_method   | customer_id   | store_location   |
|:-----------|:-----------|:-----------------------|:-----------|:-------|:--------|-----------:|-------------:|---------------:|:-----------------|:--------------|:-----------------|
| B7392      | 2024-01-15 | Classic Cotton T-Shirt | Tops       | M      | White   |          2 |        19.99 |          39.98 | Credit Card      | C4521         | New York         |
| B7393      | 2024-01-15 | Slim Fit Jeans         | Bottoms    | 32     | Blue    |          1 |        59.99 |          59.99 | PayPal           | C4522         | Los Angeles      |
| B7394      | 2024-01-15 | Wool Blend Sweater     | Outerwear  | L      | Gray    |          1 |        89.99 |          89.99 | Debit Card       | C4523         | Chicago          |

CRITICAL RULES:
1. ONLY use columns from the list above
2. If the available columns are insufficient to answer the question, clearly state that
3. Keep answers concise and focused
4. For numeric calculations, CAST string columns to NUMERIC using CAST(column_name AS NUMERIC)
5. Table name is exactly 'retail_ingest_data'

VISUALIZATION RULES:
- Only suggest bar, line, or pie charts
- ONLY use column names that exist in the data
- Bar charts: For comparing categories or groups
- Line charts: For time-based trends
- Pie charts: For showing composition/distribution

Example Responses:

1. Time-based Question: "Show monthly sales trend"
{
    "sql_query": "SELECT DATE_TRUNC('month', date::timestamp) as month, SUM(CAST(total_amount AS NUMERIC)) as total_sales FROM retail_ingest_data GROUP BY month ORDER BY month",
    "answer": "Monthly sales show an upward trend with peak in December at $50,000",
    "visualization": {
        "type": "line",
        "x_axis": "month",
        "y_axis": "total_sales",
        "title": "Monthly Sales Trend",
        "description": "Line chart shows sales progression over time"
    }
}

2. Category Question: "What's the distribution of sales by product?"
{
    "sql_query": "SELECT product_name, SUM(CAST(total_amount AS NUMERIC)) as total_sales FROM retail_ingest_data GROUP BY product_name ORDER BY total_sales DESC",
    "answer": "Product sales distribution shows Product A leading with 45% market share",
    "visualization": {
        "type": "pie",
        "x_axis": "product_name",
        "y_axis": "total_sales",
        "title": "Sales Distribution by Product",
        "description": "Pie chart shows relative market share of each product"
    }
}

Question: What is the total revenue generated from each product category?


Remember: Only use columns that exist in the data and match column names exactly as shown above.

Respond in this JSON format:
{
    "sql_query": "Your PostgreSQL query here (or 'None' if columns are insufficient)",
    "answer": "Provide clear business insights from the query results",
    "visualization": {
        "type": "none | bar | line | pie",
        "x_axis": "column_name",
        "y_axis": "column_name",
        "title": "Chart title",
        "description": "Why this visualization is relevant"
    }
}

Query Results:
|    | category   |   total_revenue |
|---:|:-----------|----------------:|
|  0 | Outerwear  |         1074.87 |
|  1 | Bottoms    |          599.89 |
|  2 | Tops       |          599.78 |
|  3 | Activewear |          480.83 |
|  4 | Dresses    |          370.94 |