🚀 AI-Powered Code Mentor & Debugging Assistant

📌 Categorization of Screens & Implementation Approach

This app consists of 15 screens, categorized based on implementation method:

• WebView Screens – Developed by Web Devs & Integrated in Android
• ML Kit Screens – For On-Device AI Processing
• Hugging Face ML Screens – For Advanced AI Debugging & Code Generation
• Jetpack Compose Screens – Regular UI Screens

🌐 WebView Screens

Implemented using WebView with Flask backend for dynamic content:

• Kotlin/Python Documentation Search - Fetches official documentation.
• Daily Kotlin & Android Coding News - Retrieves news from Dev.to, Medium.
• Web Scraping for Code Optimization - AI-based search for debugging solutions.
• AI-Powered Learning Paths - Dynamic content served via Flask.

🤖 ML Kit Screens

Uses Google's ML Kit for real-time AI processing on-device:

• On-Device Code Scanning (OCR) - Recognizes code from images.
• Handwriting Recognition for Code - Converts handwritten Kotlin to digital text.
• Text-to-Speech for Code Help - Reads explanations aloud.

🧠 Hugging Face ML Screens

Utilizes AI-powered models for debugging and learning assistance:

• AI Debugging Assistant (Voice-Based) - Answers debugging queries via speech.
• Smart Code Refactoring Suggestions - Improves code performance & readability.
• Personalized AI Learning Paths - Adaptive learning recommendations.
• Voice-Controlled Code Editor - Edits code using voice commands.

📱 Jetpack Compose Screens

Implemented using native Jetpack Compose for UI & local storage:

• Home Dashboard - Displays latest news & debugging insights.
• Task & Reminder Manager - Manages pending tasks & deadlines.
• Saved Code Snippets - Local storage for frequently used code.
• Offline AI Debugging Mode - Works without internet.

🛠️ Tech Stack & Architecture

• Frontend: Jetpack Compose, WebView, ML Kit
• Backend: Flask (Python), SQLite, Firebase
• AI Models: Hugging Face Transformers, Google ML Kit
• Deployment: PythonAnywhere, Local PC API

⚡ Performance Considerations

• Optimized ML Kit models for faster processing
• Uses WebView for dynamic updates without app modifications
• Offline-first approach for seamless functionality

🚀 Next Steps

1. Implement WebView screens using Flask.
2. Integrate ML Kit for OCR & Text-to-Speech.
3. Train & deploy Hugging Face AI models.
4. Build Jetpack Compose UI with local storage.

💡 Summary of Screen Distribution

Category	Screens	Implementation
WebView	Kotlin Docs, News, Web Scraping, AI Learning Paths	Web Devs + Flask Backend
ML Kit	OCR, Handwriting Recognition, Text-to-Speech	Google ML Kit
Hugging Face	AI Debugging, Code Refactoring, AI Learning, Voice Editor	Transformer Models
Jetpack Compose	Home, Tasks, Saved Code, Offline Debugging	Native Kotlin UI

🔗 Stay Tuned for Updates!