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```python
import csv
from collections import defaultdict

# Change this to the path of your CSV file
filename = "sales.csv"

# Dictionary that starts each new product at 0.0
totals = defaultdict(float)

# Open the CSV and read it as a dictionary (so we can use column names)
with open(filename, newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        product = row["product"]
        # Convert the amount from text to a number; skip rows with bad/missing values
        try:
            amount = float(row["amount"])
        except (ValueError, TypeError):
            continue
        totals[product] += amount

# Sort products by total revenue, highest first
sorted_totals = sorted(totals.items(), key=lambda item: item[1], reverse=True)

# Print a simple report
print("Total revenue per product:")
for product, total in sorted_totals:
    print(f"  {product}: ${total:,.2f}")
```

**How it works:** The script uses the built-in `csv.DictReader` to read each row as a dictionary keyed by your column names. A `defaultdict` accumulates the `amount` for each `product`, then `sorted()` orders the results from highest to lowest before printing. No external dependencies are needed — just make sure `sales.csv` exists with the headers `date,product,amount`; rows with non-numeric amounts are safely skipped.

```python
import csv
from collections import defaultdict

# Read the CSV file and calculate total revenue per product
revenue_by_product = defaultdict(float)

with open('sales_data.csv', 'r') as file:
    reader = csv.DictReader(file)  # Reads CSV with headers as column names
    for row in reader:
        product = row['product']
        amount = float(row['amount'])
        revenue_by_product[product] += amount

# Sort products by revenue (highest to lowest) and print results
sorted_revenue = sorted(revenue_by_product.items(), key=lambda x: x[1], reverse=True)

print("Total Revenue by Product:")
print("-" * 40)
for product, total in sorted_revenue:
    print(f"{product}: ${total:.2f}")
```

**Explanation:**
This script opens your CSV file and uses `csv.DictReader` to read it with column headers, then accumulates the sales amounts for each product using a dictionary. Finally, it sorts the products by their total revenue in descending order and prints them in a formatted table.

**Dependencies:** None—uses only Python's standard library (`csv` and `collections`).

**Assumptions:** Your CSV file is named `sales_data.csv` in the current directory, the 'amount' column contains numeric values, and there are no missing values in the required columns.

```python
import csv
import sys


def total_revenue_per_product(csv_file_path):
    totals = {}

    with open(csv_file_path, "r", newline="", encoding="utf-8") as file:
        reader = csv.DictReader(file)

        # Check that the required columns exist
        required_columns = {"date", "product", "amount"}
        if not required_columns.issubset(reader.fieldnames or []):
            missing = required_columns - set(reader.fieldnames or [])
            raise ValueError(f"Missing required columns: {', '.join(sorted(missing))}")

        for row in reader:
            product = row["product"].strip()
            amount_text = row["amount"].strip()

            if not product:
                continue  # Skip rows with no product name

            try:
                amount = float(amount_text)
            except ValueError:
                continue  # Skip rows with invalid amount values

            totals[product] = totals.get(product, 0) + amount

    # Sort by total revenue from highest to lowest
    sorted_totals = sorted(totals.items(), key=lambda item: item[1], reverse=True)
    return sorted_totals


if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python sales_totals.py path/to/sales.csv")
        sys.exit(1)

    csv_path = sys.argv[1]

    try:
        results = total_revenue_per_product(csv_path)

        if not results:
            print("No valid sales data found.")
        else:
            print("Total revenue per product:")
            for product, total in results:
                print(f"{product}: {total:.2f}")
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)
```

This script reads the CSV file using Python’s built-in `csv` module, adds up the `amount` for each `product`, and then prints the totals sorted from highest to lowest. Run it like this: `python sales_totals.py sales.csv`. It uses only the standard library, so no extra installs are needed. Rows with missing product names or invalid amounts are skipped.

```python
import csv
import io

def calculate_product_revenue(csv_filepath):
    """
    Reads a CSV file, calculates total revenue per product, and prints the results.

    Args:
        csv_filepath (str or io.StringIO): The path to the CSV file or a string buffer.
    """
    product_revenues = {}

    try:
        # Use a context manager to ensure the file is closed properly
        # If it's a real file path, use: with open(csv_filepath, mode='r', newline='') as file:
        with (csv_filepath if isinstance(csv_filepath, io.StringIO) 
              else open(csv_filepath, mode='r', newline='')) as file:
            
            # DictReader treats each row as a dictionary with headers as keys
            reader = csv.DictReader(file)
            
            for row in reader:
                try:
                    product = row['product']
                    # Convert amount from string to float, handle potential errors
                    amount = float(row['amount'])
                    
                    # Add amount to the existing total or initialize it if new
                    product_revenues[product] = product_revenues.get(product, 0) + amount
                except (ValueError, KeyError) as e:
                    # Skip rows with missing columns or non-numeric amounts
                    print(f"Skipping malformed row: {row} - Error: {e}")

    except FileNotFoundError:
        print(f"Error: The file '{csv_filepath}' was not found.")
        return
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        return

    # Sort the products by their total revenue in descending order
    # .items() gets a list of (key, value) pairs
    # The lambda function tells sorted() to use the second item (the value) for sorting
    sorted_revenues = sorted(product_revenues.items(), key=lambda item: item

```python
import csv
from collections import defaultdict  # Built-in module for convenient dictionary summing

# Prompt user for CSV file path (simple for beginners)
filename = input("Enter the CSV filename (e.g., sales.csv): ").strip()

# Use defaultdict to auto-initialize missing products to 0.0
totals = defaultdict(float)

# Read CSV with DictReader, which uses header row to create dicts per row
with open(filename, 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        # Ignore 'date'; sum 'amount' per 'product' (convert to float for decimals)
        totals[row['product']] += float(row['amount'])

# Sort products by total revenue descending (highest first)
sorted_totals = sorted(totals.items(), key=lambda x: x[1], reverse=True)

# Print each product and its total revenue, formatted to 2 decimal places
for product, total in sorted_totals:
    print(f"{product}: ${total:.2f}")
```

This script prompts you for the CSV filename, reads the file using Python's built-in `csv` module (which handles headers automatically), and sums the 'amount' column for each unique 'product' while ignoring 'date'. It uses `defaultdict(float)` to easily accumulate totals, sorts them from highest to lowest revenue, and prints formatted results. No external dependencies are needed (all standard library); assumes the CSV has exact column names, valid numeric 'amount' values, and the file exists—add error handling like try/except for production use.