Goals: Learn to use Flask for APIs, Learn React for front end, Learn NumPys and Pandas to handle my data for my AI
I currently do not know React and know very basic Python ( haven’t taken DSA yet but have been learning the concepts myself from YouTube and ChatGPT). However, I am taking two classes ( Calc 3 and Ethics ) and want to make a specialized AI myself that will make these classes ( and future classes ) less work.
I predict it will take me about 1-2 months, but we shall see! I will record my progress down below, and then make a final post later on :)
♡ Executive Summary
AI-HW-Helper is a full-stack application designed to assist students by leveraging Google’s Generative AI for intelligent homework assistance. Built with a Python/Flask backend and a React frontend, the project features a modular architecture with specialized modules for query classification, translation, and step-by-step tutoring. The goal is to create not just a helpful tool, but a system that provides personalized learning experiences through intelligent problem-solving guidance.
♡ Project Blueprint
This project is an exercise in both full-stack software engineering and applied AI. The architecture is designed to be modular, allowing for robust features and intelligent tutoring capabilities.
♡ Tech Stack
- Frontend: React, CSS3, HTML5
- Backend: Python, Flask
- AI Integration: Google Generative AI (Gemini Pro)
- Math Computation: Wolfram Alpha API
- Database: PostgreSQL (planned for storing user queries and application data)
- Data Science: Pandas, NumPy, Scikit-learn, Plotly (planned)
- Deployment: Vercel (Frontend), Heroku/Render (Backend & Database)
♡ Core Features & Roadmap
✅ Completed Features
AI-Powered Math Tutoring (SWE): Developed a comprehensive backend service that processes user math queries and provides step-by-step explanations using Google’s Generative AI. The system includes intelligent query classification and translation modules.
Modular Backend Architecture (SWE): Built a robust Flask application with specialized modules:
- tutor.py: Handles AI-generated tutoring responses with step-by-step explanations
- query_classifier.py: Classifies user queries for appropriate handling
- query_translator.py: Translates queries for better AI processing
- algebra_solver.py: Dedicated module for algebraic problem-solving
- general_handler.py: Manages general query processing
- history_handler.py: Tracks user interaction history
- wolfram_api_solver.py: Integration with Wolfram Alpha for computational solutions
🚧 In Progress
Frontend User Interface (SWE): Currently developing React components for an intuitive user experience with real-time AI interactions.
Enhanced Algebra Solver (SWE): Expand algebra_solver.py with additional algorithms for complex mathematical operations, equation solving, and symbolic computation.
User Interaction Dashboard (SWE/DS): Create a user-facing dashboard that visualizes personal usage statistics, query history, and learning progress using data processed by Pandas and visualized with Plotly.
Usage Analytics & Insight Engine (DS): Build an admin-facing analytics module to analyze aggregated, anonymized user data to understand learning patterns and query characteristics.
Predictive Quality Monitoring (DS): Implement user feedback mechanisms and satisfaction prediction using Scikit-learn classification models.
Database Integration (SWE): Implement PostgreSQL database for persistent storage of user queries, interaction history, and analytics data.
♡ Case Study & Implementation
Milestone 1: Backend API & AI Integration ✅
Completed: Successfully implemented a comprehensive Flask backend with Google Generative AI integration. The system features:
- Intelligent Query Processing: Multi-module architecture that classifies, translates, and processes user queries
- Step-by-Step Tutoring: AI-powered explanations that adapt based on user preferences (with/without detailed steps)
- Specialized Math Handling: Dedicated algebra solver with Wolfram Alpha integration for computational solutions
- Modular Design: Clean separation of concerns with specialized handlers for different query types
Technical Highlights: - Integrated Google’s Gemini Pro model for natural language processing - Implemented query classification system for optimal AI response generation - Built translation layer for improved query understanding - Created flexible tutoring system that respects user preferences for explanation depth
Milestone 2: Core Frontend Development in React 🚧
In Progress: Developing React frontend with modern UI/UX principles for seamless user interaction.
Planned Features: - Real-time chat interface for AI interactions - Math equation rendering and input capabilities - Responsive design for mobile and desktop - User preference settings for explanation depth
Milestone 3: Data Analytics Module Implementation 📋
Planned: Comprehensive data science pipeline for learning analytics and user insights.
Features to Implement: - User query pattern analysis - Learning progress tracking - Performance analytics dashboard - Predictive modeling for user satisfaction
♡ Current Status
(As of January 2025)
The project has successfully completed its core backend development phase with a fully functional AI-powered tutoring system. The modular architecture is in place and ready for frontend integration.
Completed:
✅ Flask backend with Google AI integration
✅ Modular query processing system
✅ Step-by-step tutoring functionality
✅ Specialized math handling modules
✅ Query classification and translation
In Progress:
🚧 React frontend development
🚧 User interface design and implementation
Next Steps:
Complete React frontend with modern UI/UX
Expand algebra_solver.py with additional algorithms
Implement PostgreSQL database for data persistence
Develop analytics dashboard with Pandas/Plotly
Add user feedback and satisfaction prediction systems
Timeline Update: The project is progressing well within the original 1-2 month timeline, with core AI functionality complete and frontend development underway.