A sophisticated AI-powered multilingual voice assistant for mobile phone sales, featuring real-time speech-to-text, multilingual response generation, and text-to-speech capabilities.
- Real-time Speech-to-Text in 15+ languages
- Multilingual Text-to-Speech output with Murf AI integration
- Voice-controlled mobile shopping experience
- Browser-based voice recognition
- English (Default)
- Hindi (ΰ€Ήΰ€Ώΰ€ΰ€¦ΰ₯) - hi-IN-kabir voice
- Korean (νκ΅μ΄) - ko-KR-gyeong voice
- Japanese (ζ₯ζ¬θͺ) - ja-JP-kenji voice
- Chinese (δΈζ) - zh-CN-tao voice
- Spanish (EspaΓ±ol) - es-ES-elvira voice
- 10+ additional languages with native voice outputs
- Gemini AI-powered conversational agent
- Smart product search with Pinecone vector database
- Personalized deal generation based on conversation context
- Natural language understanding for mobile sales
- Mobile product catalog with detailed specifications
- Intelligent product recommendations
- Dynamic discount generation
- Visual product displays with images
- Advanced negotiation capabilities that create personalized shopping experiences
This project would not have been possible without the following amazing technologies and services:
- Google Gemini AI - For providing powerful multilingual language capabilities and intelligent conversational AI
- Murf AI - For high-quality, natural-sounding text-to-speech services across multiple languages
- Pinecone - For robust vector database infrastructure enabling intelligent product search
- LangChain - For the comprehensive AI agent framework that powers our conversational workflows
- FastAPI - For the high-performance, modern web framework that makes our API development efficient
- React - For the declarative and component-based frontend library
- Vite - For the fast build tool and development server
- Web Speech API - For browser-based speech recognition capabilities
- Google Cloud Speech-to-Text - For accurate speech recognition technology
- Pydantic - For data validation and settings management using Python type annotations
- Uvicorn - For the lightning-fast ASGI server implementation
- Hugging Face - For embeddings models and transformer resources
To run this project, you will need to add the following environment variables to your .env file
MURF_API_KEY
GOOGLE_API_KEY
PINECONE_API_KEY
MURF_VOICE_ID
(optional - defaults to en-US-natalie)
PINECONE_INDEX_NAME
(optional - defaults to mobile-phones)
git clone https://github.com/codewhyofficial/building-on-murf-challenge.git
cd building-on-murf-challenge
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
cd ../frontend
npm install
multilingual-voice-assistant/
βββ backend/ # FastAPI Backend Application
β βββ agent.py # AI Agent Workflows
β βββ tools.py # LangChain Tools
β βββ vector_store.py # Pinecone Integration
β βββ schemas.py # Pydantic Models
β βββ main.py # FastAPI Application Entry Point
β βββ config.py # Configuration Settings
β βββ requirements.txt # Python Dependencies
β βββ .env # Environment Variables
β
βββ frontend/ # React Frontend Application
β βββ src/
β β βββ components/ # React Components
β β βββ hooks/ # Custom React Hooks
β β βββ utils/ # Utility Functions
β β βββ App.jsx # Main App Component
β β βββ main.jsx # Application Entry Point
β βββ package.json
β βββ vite.config.js
β βββ index.html
β
βββ data/ # Sample Data Files
βββ .gitignore
βββ Dockerfile
βββ docker-compose.yml
βββ README.md
We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your help is appreciated.