interactive_modular_translator_tool.md

Interactive Modular Translator Tool

Overview

This tool is a Python-based pipeline designed to run within a Jupyter Notebook environment (specifically optimized for Google Colab). It automates the process of discovering, cloning, and translating entire GitHub repositories from the ASI Ecosystem into multiple languages using the Google Translate API.

Features

• Automated Discovery: Scrapes the master README to find all related repositories.
• Interactive UI: Uses ipywidgets to let users select specific repositories and target languages via checkboxes.
• Smart Translation: Handles text chunking to respect API character limits and supports retry logic for stability.
• File Format Support: Translates code comments and documentation files (e.g., .md, .py, .json, .txt).
• Post-Processing: Merges translations into single, readable text files and generates detailed reports.
• Cloud Integration: Zips the final output and uploads it directly to Google Drive.

---

Pipeline Workflow

The tool operates in a sequential 9-step pipeline:

1. Environment Setup

Cell 1: Installs necessary dependencies (requests, deep-translator, ipywidgets, datasets) and imports required libraries.

2. Repository Discovery

Cell 2:

• Fetches the main README.md from the asi-ecosystem repository.
• Uses Regex to parse the content and extract a unique list of all linked GitHub repositories.

3. Repository Selection (Interactive)

Cell 3:

• Generates a dynamic UI with checkboxes for every repository found.
• Includes Select All and Deselect All buttons for batch control.

4. Cloning Phase

Cell 4:

• Iterates through selected repositories.
• Performs a shallow clone (git clone --depth 1) to cloned_repos/ to save bandwidth and storage.
• Handles errors.

5. Language Discovery

Cell 5:

• Queries the Google Translate API to retrieve the list of all currently supported languages (133+ languages).

6. Language Selection (Interactive)

Cell 6:

• Displays a grid of checkboxes for all supported languages.
• Allows the user to select one or multiple target languages for translation.

7. Translation Engine (Core Processing)

Cell 7: This is the most computationally intensive step.

• File Filtering: Scans repositories for text-based files (extensions include .txt, .md, .py, .js, .json, etc.).
• Chunking: To bypass the API limit (approx. 5000 chars), the tool splits files into chunks of 4500 characters, respecting newline boundaries.
• Execution: Translates content chunk-by-chunk and reconstructs the file in the translations/ directory, maintaining the original folder structure.
• Reporting: Generates a translation_report.json containing statistics on success and failure rates.

8. Merging and formatting

Cell 8:

• Consolidates the fragmented file structure.
• For every repository and every language, creates a single _merged.txt file (e.g., asi-protosymbiotic-signal_cs.txt).
• Adds headers and separators between files for easier reading or ingestion by LLMs.

9. Archiving and Export

Cell 9:

• Compresses the entire translations directory into a timestamped .zip file.
• Mounts Google Drive (/content/drive).
• Copies the ZIP file to the user's Google Drive root folder for permanent storage.

---

File System Structure

/content/
├── cloned_repos/               # Raw source code
│   └── [repo_name]/
├── translations/               # Translated outputs
│   └── [repo_name]/
│       └── [lang_code]/        # (e.g., 'es', 'fr')
│           └── [original_structure]
├── merged_translations/        # Consolidated text files
│   └── [repo_name]_[lang_code].txt
└── asi_translations_[date].zip # Final archive

---

Ronni Ross 2026