PowerBIMentor 0.1.0

Extract metadata from Power BI .pbit templates and evaluate them using AI models.

PowerBIMentor

A Python package for extracting metadata from Power BI .pbit templates and evaluating them using AI models like Google Gemini.

Features

• 📊 PBIT Processing: Extract and parse Power BI template metadata (DataModelSchema)
• 🤖 AI Evaluation: Evaluate DAX formulas, visualizations, and written answers using Google Gemini
• 📝 Detailed Reports: Generate comprehensive grading reports from Power BI models
• 📦 ZIP Support: Automatically handles ZIP file submissions - no manual extraction needed
• 🔧 Easy Integration: Simple API for evaluating student assignments and projects

Installation

From Source

git clone https://github.com/yourusername/PowerBIMentor.git
cd PowerBIMentor
pip install -e .

Using pip (when published)

pip install PowerBIMentor

Quick Start

from PowerBIMentor import PowerBIMentor

# Initialize with your Gemini API key
mentor = PowerBIMentor(api_key="your-api-key")

# Evaluate a DAX assignment
# Works with both directories AND ZIP files!
result = mentor.evaluate_dax(
    answer_path="path/to/assignment/",  # or "submission.zip"
    question="Create measures for total sales and profit margin",
    prompt="Evaluate the DAX implementation for correctness and best practices"
)

print(f"Score: {result['score']}/100")
print(f"Feedback: {result['feedback']}")

Package Structure

PowerBIMentor/
├── __init__.py           # Main package entry point
├── core.py               # PowerBIMentor class with evaluation methods
├── models/               # AI model wrappers
│   ├── __init__.py
│   ├── model.py          # Abstract base model
│   └── gemini.py         # Google Gemini implementation
└── utils/                # Utility functions
    ├── __init__.py
    ├── processor.py      # PBIT parsing and report generation
    ├── checker.py        # File discovery helpers
    └── extractor.py      # ZIP extraction utilities

Core Components

PowerBIMentor Class

The main class provides three evaluation methods:

• evaluate_dax(answer_path, question, prompt): Evaluates Power BI data models and DAX formulas
• evaluate_visual(answer_path, question, prompt): Evaluates dashboard visualizations from PDFs
• evaluate_write(answer_path, question, prompt): Evaluates written answers from text files
• evaluate_all(answer_path, questions, prompts): Evaluates all three aspects at once

PBIT Processor

PowerBIMentor.utils.processor provides functions for processing Power BI templates:

pbit_to_json(pbit_path)

Extracts and parses the DataModelSchema from a .pbit file:

• Handles UTF-16 encoding and quote normalization
• Returns the parsed JSON model structure

extract_grading_info(model)

Extracts key elements for grading:

• Tables, columns, and measures (excluding hidden/private items)
• Relationships (excluding LocalDateTable)
• Hierarchies (excluding template hierarchies)
• Data source information from M code

generate_grading_report(grading_info)

Formats extracted information into a readable text report

analyze_pbit(pbit_path)

Convenience function that chains all three steps above

AI Models

Gemini Model

PowerBIMentor.models.gemini.Gemini

from PowerBIMentor.models import Gemini

model = Gemini(api_key="your-api-key", model_name="gemini-2.0-flash-exp")

# Evaluate text-based answers
result = model.evaluate(
    question="What is DAX?",
    answer="DAX stands for Data Analysis Expressions...",
    prompt="Evaluate for accuracy and completeness"
)

# Evaluate PDF visualizations
result = model.evaluate_visual(
    question="Review the dashboard design",
    prompt="Evaluate layout, clarity, and best practices",
    pdf_path="dashboard.pdf"
)

Response Format:

{
  "score": 85,
  "feedback": "Strong implementation with minor issues..."
}

Configuration

API Key Setup

Create a .env file in your project root:

API_KEY=your_gemini_api_key_here

Load it in your code:

from dotenv import load_dotenv
import os

load_dotenv()
api_key = os.getenv("API_KEY")

Detailed Usage Examples

1. Analyze a PBIT File

from PowerBIMentor.utils import analyze_pbit

# Generate a detailed report from a Power BI template
report = analyze_pbit("path/to/file.pbit")
print(report)

Sample Output:

Model: Sales Analysis
Compatibility Level: 1550

Data Source:
  Type: File
  Path: C:\Data\Sales.xlsx

Tables:
  - Sales
    Columns:
      • Date (type=dateTime, summarize_by=none, calculated=False)
      • Product (type=string, summarize_by=none, calculated=False)
      • Amount (type=double, summarize_by=sum, calculated=False)
    Measures:
      • Total Sales
      • Profit Margin

Measures (details):
  - Total Sales (table: Sales)
      SUM(Sales[Amount])

  - Profit Margin (table: Sales)
      DIVIDE([Total Profit], [Total Sales], 0)

Relationships:
  - Sales[Date] -> Calendar[Date] (standard)

Summary:
  - main_table: Sales
  - total_columns: 3
  - total_measures: 2
  - total_relationships: 1
  - has_time_intelligence: False

2. Complete Evaluation Pipeline

from PowerBIMentor import PowerBIMentor

mentor = PowerBIMentor(api_key="your-api-key")

# Define questions and prompts for each evaluation type
questions = {
    "dax": "Create measures for total sales and YoY growth",
    "visual": "Create a sales dashboard with KPIs and trends",
    "write": "Explain your dashboard design choices"
}

prompts = {
    "dax": "Evaluate DAX correctness, best practices, and time intelligence",
    "visual": "Evaluate layout, chart selection, and design principles",
    "write": "Evaluate clarity, justification, and understanding"
}

# Evaluate all aspects
results = mentor.evaluate_all(
    answer_path="path/to/student/submission/",
    questions=questions,
    prompts=prompts
)

print(f"DAX Score: {results['dax']['score']}/100")
print(f"Visual Score: {results['visual']['score']}/100")
print(f"Written Score: {results['write']['score']}/100")

3. Custom Model Usage

from PowerBIMentor.models import Gemini
from PowerBIMentor.utils import analyze_pbit

# Initialize with specific model
model = Gemini(api_key="your-api-key", model_name="gemini-2.0-flash-exp")

# Get PBIT analysis
analysis = analyze_pbit("assignment.pbit")

# Evaluate with custom prompt
result = model.evaluate(
    question="Implement sales analysis with time intelligence",
    answer=analysis,
    prompt="""
    Evaluate the implementation considering:
    1. Correct use of DAX functions
    2. Time intelligence measures
    3. Proper relationships
    4. Performance optimization
    """
)

print(f"Score: {result['score']}/100")
print(f"Feedback:\n{result['feedback']}")

Examples

The examples/ directory contains comprehensive usage examples:

• quick_start.py: Simplest introduction (start here!)
• basic_usage.py: Comprehensive guide to core features
• advanced_usage.py: Advanced features and batch processing
• classroom_grading.py: Practical example for grading student assignments

See examples/README.md for detailed documentation.

# Run the quick start example
python examples/quick_start.py

# Run comprehensive examples
python examples/basic_usage.py

Development

Running Tests

# Run processor tests
python tests/processors/processor.py

# Run model tests
python tests/models/gemini.py

# Run integration tests
python tests/test.py

Project Dependencies

Core:

• google-genai>=1.0.0 - Google Gemini API client
• python-dotenv>=1.0.0 - Environment variable management

Optional:

• google-cloud-aiplatform>=1.0.0 - For Vertex AI support

Requirements

• Python 3.9 or higher
• Google Gemini API key (get one at Google AI Studio)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details