Revised Project Brief: Voice-Based Order Management System for Street Vendors

Objective: Develop a cross-platform voice-to-order management system for mobile food vendors using modern web technologies and AI integration.

Key Features:

Voice-to-Order Conversion

Real-time Database Management

Multi-platform Deployment (Web/Android/iOS)

Technical Stack:

Frontend:

• Ionic React/Angular + Capacitor
• UI Components: @ionic/core, @capacitor/media
• Voice Processing: @capacitor-community/voice-recognition

Backend:

• Node.js + Express
• Database: PostgreSQL/Firestore
• ORM: Prisma/TypeORM

AI Services:

• Speech-to-Text: OpenAI Whisper API (Large v3)
• Text Classification: Custom spaCy NER model/BERT fine-tuning System Architecture:

Client-Side Components

Voice Recording Interface

Order Visualization Dashboard

Real-time Order Updates

Server-Side Services

Audio Processing API

NLP Classification Engine

Order Management System

Core Implementation Flow:

Voice Input Handling

typescript // Capacitor Voice Recording Implementation import { VoiceRecorder } from 'capacitor-voice-recorder';

const toggleRecording = async () => { if (isRecording) { const { value: audioData } = await VoiceRecorder.stop(); await processAudio(audioData); } else { await VoiceRecorder.start(); } }; Audio Processing Pipeline

typescript // Whisper API Integration async function transcribeAudio(audioBlob: Blob) { const formData = new FormData(); formData.append('file', audioBlob, 'recording.webm'); formData.append('model', 'whisper-large-v3');

const response = await fetch('https://api.openai.com/v1/audio/transcriptions', { method: 'POST', headers: { 'Authorization': Bearer ${OPENAI_KEY} }, body: formData });

return response.json(); } Text Classification System

typescript // Order Parsing Logic Example interface OrderItem { dish: string; quantity: number; modifiers: string[]; }

function parseOrder(text: string): OrderItem[] { // Implement either: // 1. Rule-based parsing with regex // 2. ML model inference // 3. Hybrid approach } Database Schema:

typescript // Prisma Schema Example model Order { id Int @id @default(autoincrement()) createdAt DateTime @default(now()) items OrderItem[] }

model OrderItem { id Int @id @default(autoincrement()) dish String quantity Int modifiers Modifier[] }

model Modifier { id Int @id @default(autoincrement()) name String } Model Selection Guide:

Speech-to-Text

Production: OpenAI Whisper API (Best accuracy)

Local Alternative: HuggingFace Transformers.js with Distil-Whisper

Text Classification

Phase 1: Regex-based parser (Quick implementation)

Phase 2: spaCy NER model (Custom training)

Advanced: Fine-tuned BERT-base (Highest accuracy)

Development Roadmap:

Phase Duration Tasks

1. Setup 3 Days - Project initialization

• CI/CD pipeline
• Basic UI scaffolding

2. Core Development 14 Days - Voice recording system

• Whisper integration
• Order parsing logic
• Database integration

3. Optimization 5 Days - Real-time updates

• Error handling
• Performance tuning

4. Testing 5 Days - Unit tests

• User acceptance testing
• Cross-platform validation

5. Deployment 3 Days - App Store preparation

• Vendor onboarding docs Critical Considerations:

Audio Quality Management

Background noise suppression techniques

Sample rate optimization (16kHz recommended)

Order Validation System

Conflict resolution for ambiguous orders

Vendor confirmation workflow

Security Measures

API rate limiting

Vendor authentication system

Data encryption at rest/transit

Alternative Approaches:

On-device STT: TensorFlow.js + QuartzNet model

Local Database: SQLite for offline-first capability

Real-time: Firebase Realtime Database alternative

Documentation Plan:

Vendor onboarding guide

API specification docs

Troubleshooting manual

Maintenance checklist

This architecture provides a balance between development speed and scalability. For initial launch, consider using Whisper API + regex parsing, then gradually implement ML-based classification as order data accumulates. The hybrid approach allows for iterative improvement while meeting immediate vendor needs.

Create the frontend