Variable Name Normalizer

A Streamlit application that uses Google's Gemini AI to normalize variable names based on standard conventions and examples.

Features

• Single Variable Normalization: Normalize individual variable names in real-time
• Batch Normalization: Process multiple variables at once via manual input or CSV upload
• Custom Mappings: Add your own mappings for specific variable names
• Example Browsing: View and search through example mappings

Setup

1. Clone this repository:

   git clone <repository-url>
   cd variable-normalizer
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your API key:

   • Rename .env.example to .env
   • Add your Google API key to the .env file

   GOOGLE_API_KEY=your_api_key_here
   

4. Run the app:

   streamlit run app.py
   

How it Works

The application uses a set of example variable name mappings stored in data/filed-mappings.csv to learn how to normalize new variable names. It uses the Gemini 1.5 Flash API to generate normalized names based on these examples.

Each normalization includes:

• A display name (with proper spacing and capitalization)
• A programming variable name (in snake_case)
• The likely category of the variable
• A confidence score
• An explanation of the normalization process

Custom Mappings

You can add your own custom mappings using the sidebar. These mappings will take precedence over the AI's suggestions for specific variable names.

Requirements

• Python 3.8+
• Streamlit
• Pandas
• Google Generative AI SDK
• An API key for Google's Gemini

Limitations

• The service requires an internet connection to access the Gemini API
• The quality of normalizations depends on the examples provided
• API rate limits may apply depending on your Google API usage