🤟 SignSpeak AI — Adaptive Multi-Modal Learning Platform

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📌 Executive Summary

SignSpeak AI is an adaptive AI learning platform designed to provide accessible, multi-modal education for blind, deaf, and speech-impaired students covering the Class 6–12 NCERT curriculum. Powered by browser-native Web Speech API, real-time natural language processing, and Supabase cloud storage, SignSpeak AI breaks down traditional learning barriers with tailored interaction modes.

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📊 Project Presentation & Slide Deck Showcase

Important

📥 Official Presentation & Project Documentation:

• 📄 Slide Deck (PDF): ppt.pdf (Comprehensive 6-slide presentation deck covering adaptive learning modes, speech navigation flow, React/Pollinations AI architecture, and Supabase teacher portal integration)

📑 Presentation Slide Deck Breakdown

Slide #	Slide Topic	Engineering & Functional Content Highlighted
01	Title & Vision	Adaptive AI Learning Platform for Class 6–12 NCERT Syllabus. Custom-tailored voice, visual, and text environments.
02	Three Tailored Sensory Modes	Blind Mode (Full Voice Assistant + TTS/STT), Deaf Mode (Visual cards, diagrams & subtitles), Speech-Impaired (Tap-word chip sentence builder).
03	7-Step Autonomous Speech Flow	Auto Prompt welcome, Voice Configuration, Dynamic Audio Lectures, and Real-Time Interruption & Resumption Q&A logic.
04	React Scaffold & AI Engine	Modern React + TS frontend, Tailwind CSS styling, Framer Motion animations, Pollinations AI API core, and Web Speech API integration.
05	Supabase Teacher Portal & Offline Resilience	Cloud database setup for syllabus media management (teacher_contents table) with offline local storage fallback.
06	Execution & Rewards System	Google Chrome Web Speech API execution requirements, NCERT curriculum alignment, 60%+ passing quiz threshold, and printable completion certificates.

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🌟 Adaptive Learning Modes

👁️ Blind Mode Full voice assistant interface utilizing browser Web Speech API (TTS & STT). Students listen to chapter lectures and can interrupt mid-lesson to ask natural questions before resuming.

👂 Deaf Mode Visual-first learning interface featuring sign language cards, emoji illustrations, diagrams, and auto-generated subtitles without reliance on audio cues.

🗣️ Speech-Impaired Text-based interactive interface with an intuitive sentence builder that allows non-verbal students to construct questions using interactive tap-word chips.

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🏗️ System Architecture

graph TD
    subgraph Student Interface
        A[User Input: Voice / Visual Cards / Word Chips] --> B[Mode Selector Engine]
        B -->|Blind Mode| C[Web Speech Recognition & Synthesis]
        B -->|Deaf Mode| D[Visual Deck & Emoji Diagram Renderer]
        B -->|Speech-Impaired| E[Tap-Word Chip Sentence Builder]
    end

    subgraph AI & Backend Engine
        C --> F[Pollinations AI NLP Engine]
        D --> F
        E --> F
        F --> G[Lesson & Quiz Generator]
    end

    subgraph Cloud Storage & Teacher Portal
        G --> H[Supabase Cloud Database]
        H --> I[Progress & Certificate Generator]
    end

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📂 Repository Hierarchy

Sign_Speak_AI/
├── README.md                      # Master Project README
├── ppt.pdf                        # Official Project Presentation Slide Deck (PDF)
└── qwe/                           # Main Web Application Workspace
    ├── index.html                 # Core UI Structure
    ├── style.css                  # Custom Accessibility Styles
    ├── app.js                     # Web Speech API & AI Handler Logic
    ├── server.py                  # Local Python HTTP Server
    ├── supabase-config.js         # Supabase Cloud Database Config
    ├── package.json               # Node.js Dependencies & Scripts
    └── src/                       # React + TypeScript Scaffold & Components

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⚙️ Running Locally

1. Python Server (Simple Web App)

Run the pre-configured local HTTP server:

cd qwe
python server.py

Open Google Chrome and navigate to http://localhost:3000.

2. React + TypeScript Scaffolding

To run the developer scaffold:

cd qwe
npm install
npm run dev

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📜 License

Distributed under the MIT License.

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Developed by A Sharath — ECE Placement Portfolio