An intelligent converter has been built to translate Python scripts into R code while integrating Google’s free Gemini API for review and refinement. Designed for tasks from basic data cleaning to statistical modeling, the tool first maps Python functions, libraries, and syntax to R equivalents using conversion rules. After translating each segment, it sends the code to Gemini AI to collect accuracy scores, suggested improvements, and rewritten snippets, delivering high-quality R output from Python sources.
The workflow starts by importing key Python modules—re for pattern matching, requests for HTTP calls, json for data handling, and os to access environment variables. It loads GEMINI_API_KEY from the operating environment, which keeps credentials secure when calling Google’s API. With these dependencies in place, the script can invoke Gemini AI without exposing sensitive keys, making it suitable for local development, CI pipelines, or cloud-hosted notebooks.
GeminiValidator handles AI-powered review. It constructs a prompt that pairs the original Python snippet with its R translation, then requests Gemini AI to check semantic accuracy, suggest optimizations, and return an improved R version. After receiving the JSON response, it extracts fields like validation_score, suggested_changes, and refined_code, then passes them back for further refinement in the conversion pipeline.
The EnhancedPythonToRConverter class implements the conversion rules. It uses mapping dictionaries to link common Python libraries—pandas, NumPy, and matplotlib—to R packages such as dplyr, data.table, and ggplot2. Regex-based routines update import statements to library() calls, convert function definitions, and adapt pandas DataFrame operations to R’s tidyverse syntax. Plot instructions from matplotlib become ggplot2 layers. Additional handlers translate list comprehensions, lambda expressions, and try/except blocks into R’s map(), anonymous functions, and tryCatch equivalents. It also converts I/O calls such as pd.read_csv to read.csv and pd.DataFrame.to_csv to write.csv. A single convert_code() method runs these steps in order. Unit tests confirm that simple scripts yield identical outcomes in both languages.
To simplify setup, setup_gemini_key() guides users through creating a free API key on Google Cloud, setting the GEMINI_API_KEY environment variable, and testing connectivity. It prints concise shell commands and Python snippets for account registration and environment configuration, reducing errors and accelerating the initial setup process.
The demo_with_gemini() function runs a sample Python data analysis script—covering CSV ingestion, pandas-based cleaning, summary statistics, and a matplotlib plot—then invokes EnhancedPythonToRConverter to generate comparable R code. If GEMINI_API_KEY is set, it calls GeminiValidator with parameters like temperature and max_tokens, prints the validation_score, outlines suggested_changes, and shows the refined R script. This demonstration highlights how AI review can seamlessly integrate into automated code translation.
For users on Google Colab, a colab_setup() routine details pip install commands for required packages, steps to set the GEMINI_API_KEY variable within the notebook, and code cells to run the demo. This makes it easy to evaluate the converter in a hosted Jupyter environment without local installs, enabling collaboration on examples and tuning the conversion rules or AI prompts directly in the cloud.
When executed as a script, the main guard calls demo_with_gemini(), triggering the full conversion and review sequence automatically. This design lets the tool serve as both a standalone utility and a library: developers can import GeminiValidator and EnhancedPythonToRConverter into larger pipelines for batch processing or continuous integration workflows.