```html Project Setup 
# Contents of execute.py
import json
import pandas as pd

def main():
    # Read the data
    df = pd.read_csv("data.csv")  # Correct extension

    # Compute revenue
    df["revenue"] = df["units"] * df["price"]

    # row_count
    row_count = len(df)

    # regions: count of distinct regions
    regions_count = df["region"].nunique()

    # top_n_products_by_revenue (n=3)
    n = 3
    top_products = (
        df.groupby("product")["revenue"]
        .sum()  # Corrected
        .sort_values(ascending=False)
        .head(n)
        .reset_index()
    )
    top_products_list = [
        {"product": row["product"], "revenue": float(row["revenue"])}
        for _, row in top_products.iterrows()
    ]

    # rolling_7d_revenue_by_region: for each region, last value of 7-day moving average of daily revenue
    df["date"] = pd.to_datetime(df["date"])  # ensure datetime
    daily_rev = (
        df.groupby(["region", "date"])["revenue"]  # Corrected column name
        .sum()
        .reset_index()
        .sort_values(["region", "date"])  # ensure sorted for rolling
    )

    # Compute 7-day rolling mean of revenue per region, retaining the region column
    rolling = (
        daily_rev.groupby("region")
        .apply(lambda g: g.set_index("date")["revenue"].rolling("7D").mean())
        .reset_index(name="rolling_7d_revenue")
    )

    last_rolling = (
        rolling.sort_values(["region", "date"])  # ensure order
        .groupby("region")
        .tail(1)
    )

    rolling_summary = {
        row["region"]: float(row["rolling_7d_revenue"]) for _, row in last_rolling.iterrows()
    }

    result = {
        "row_count": int(row_count),
        "regions": int(regions_count),
        "top_n_products_by_revenue": top_products_list,
        "rolling_7d_revenue_by_region": rolling_summary,
    }

    print(json.dumps(result, indent=2))

if __name__ == "__main__":
    main()

# Save the above code to execute.py

# Convert data.xlsx to data.csv, assuming the existing data in data.xlsx, save only required data:
# Use a tool like pandas for conversion, assuming data.xlsx is correctly formatted:
# import pandas as pd
# pd.read_excel('data.xlsx').to_csv('data.csv', index=False)

# Contents of .github/workflows/ci.yml
name: CI

on:
  push:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - name: Check out code
      uses: actions/checkout@v2
      
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.11'
    
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pandas==2.3 ruff

    - name: Lint with Ruff
      run: ruff execute.py

    - name: Run execute.py and generate result.json
      run: python execute.py > result.json

    - name: Deploy to GitHub Pages
      uses: peaceiris/actions-gh-pages@v3
      with:
        github_token: ${{ secrets.GITHUB_TOKEN }}
        publish_dir: ./
        publish_branch: gh-pages