Welcome to the SuperDataScience Community Project!

Welcome to Polyglot: The AI Language Companion repository! 🎉

This project is a collaborative initiative brought to you by SuperDataScience, a thriving community dedicated to advancing the fields of data science, machine learning, and AI. We are excited to have you join us in this journey of learning, experimentation, and growth.

To contribute to this project, please follow the guidelines avilable in our CONTRIBUTING.md file.

Project Scope of Works:

Project Overview

Polyglot is an AI-powered personal language tutor designed to help users learn a new language through interactive conversations, vocabulary drills, and grammar exercises. Leveraging an Agentic AI pattern, the system autonomously guides learners by adapting to their proficiency levels and learning styles. The final solution will be deployed on Hugging Face Spaces with an intuitive chat-based interface for seamless interaction.

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Objectives

🌍 AI-Powered Language Learning

• Develop an AI tutor that engages users in conversational learning.
• Generate personalized exercises, quizzes, and feedback to improve learning outcomes.

🧠 Agentic AI System

• Implement an autonomous AI agent that navigates through structured lessons.
• Utilize memory and context awareness to tailor responses based on past interactions.

☁ Cloud Deployment & UI Development

• Deploy the AI tutor on Hugging Face Spaces for global accessibility.
• Build an interactive web interface with a chat-based learning environment.

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Tools & Technologies

• Gen AI Models: OpenAI, Anthropic, Google Gemini, Huggingface
• UI: Streamlit, Gradio
• Deployment: Streamlit, Huggingface spaces

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Scope 1: Beginner Friendly

Phase 1: Setup (1 Week)

• Repository & Environment:
  • Setup GitHub repository and project folders.
  • Configure virtual environments and install necessary Python libraries.

Phase 2: Application Logic Development (2 Weeks)

• LLM Integration:
  • Choose a suitable language model to power the tutor.
  • Integrate the model via API calls to handle user input and generate responses.
  • Introduce multi-modal capabilities by having the model speak to the user and help with pronounciation of words.
• Content Generation:
  • Develop basic routines to generate conversational responses.

Phase 3: Application UI Development (1 Week)

• User Interface:
  • Build a basic chat-based UI using frameworks like Streamlit or Gradio.
  • Allow users to interact with the AI tutor, input queries, and receive tailored language exercises.

Phase 4: Deployment (1 Week)

• Cloud Deployment:
  • Deploy the application on streamlit or Hugging Face Spaces.

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Scope 2: Advanced

Phase 1: Setup (1 Week)

• Repository & Environment:
  • Setup GitHub repository and project folders.
  • Configure virtual environments and install advanced libraries.

Phase 2: Application Logic Development (2 Weeks)

• Agent-Based Architecture:
  • Identify and design specific agents for distinct tasks:
    • Conversation Agent: Handles interactive dialogue.
    • Exercise Generator Agent: Creates personalized drills and quizzes.
    • Feedback & Context Agent: Monitors user progress, maintains context, and adjusts difficulty.
• Custom Orchestration:
  • Develop a custom orchestration engine to manage inter-agent communication.
  • Implement memory and context-awareness to tailor interactions based on historical user data.

Phase 3: Application UI Development (1 Week)

• Enhanced User Interface:
  • Develop an enriched chat-based UI that supports interactive learning and visualizes the underlying agent workflow.

Phase 4: Deployment (1 Week)

• Cloud Deployment:
  • Deploy the advanced version on streamlit or Hugging Face Spaces.

Timeline

Phase	Task	Duration
Phase 1: Setup	Project Setup	Week 1
Phase 2: Logic	Application Logic	Weeks 2-3
Phase 3: UI	User Interface	Week 4
Phase 4: Deployment	Deployment	Week 5