Here’s the updated document with Planning and Requirement Gathering removed:
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Implementation Plan for Customized Odoo POS with Facial and Speech Recognition
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1. System Design
Tasks:
1. Architecture Design:
Create diagrams of system components, including frontend, backend, AI services, and databases.
Define workflows for:
Facial recognition (image capture → emotion analysis → recommendation).
Speech recognition (audio capture → transcription → compliance check).
2. Data Flow Mapping:
Design how data flows between AI components, Odoo, and the frontend.
Plan real-time data processing for AI outputs.
3. Database Schema Design:
Extend Odoo’s database to include AI-related data, such as customer images, transcripts, and recommendations.
4. API Specification:
Define REST/GraphQL APIs for communication between the POS system and AI services.
2. Development and Integration
Tasks:
1. Facial Recognition Development:
Train facial recognition models (e.g., DeepFace/FaceNet) on a customer dataset and fine-tune models to detect emotions.
Integrate Mediapipe for real-time face tracking with cameras.
Build logic for product recommendations based on facial analysis.
Develop APIs to send recommendations to the Odoo backend.
2. Speech Recognition Development:
Set up the Google Speech-to-Text API for real-time transcription and configure microphones for capturing interactions.
Build NLP pipelines using spaCy or Hugging Face for intent detection and compliance checks.
Integrate APIs to log transcripts and compliance results in Odoo.
3. Odoo Customization:
Customize the Odoo POS module to display AI-driven recommendations.
Extend customer and sales data models to store AI outputs.
Build analytics dashboards to track AI performance and staff compliance.
Create reporting tools to analyze sales impact from AI recommendations.
4. Frontend Development:
Develop the UI for the POS system using React.js/Vue.js.
Create components for displaying real-time AI suggestions and transcriptions.
Connect the frontend to the Odoo backend and AI APIs.
Ensure the frontend's responsiveness and usability across devices.
5. Integration and Testing:
Connect facial recognition and speech recognition services with the Odoo backend.
Test APIs and data flow between components.
Conduct unit, integration, performance, and usability testing to ensure system readiness.
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3. Deployment and Training
Tasks:
1. Deployment:
Use Docker to containerize all components, including Odoo, AI services, and the frontend.
Deploy to cloud infrastructure like AWS or Google Cloud.
Set up CI/CD pipelines for future updates.
2. Staff Training:
Train staff on using the new POS system and interacting with AI suggestions.
Provide comprehensive documentation and video tutorials.
3. Initial Monitoring:
Monitor system performance in real-world scenarios.
Address bugs and improve AI model accuracy based on feedback.
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4. Post-Deployment Support and Optimization
Tasks:
1. Performance Monitoring:
Use monitoring tools such as ELK Stack to track logs and analytics.
Analyze AI suggestion impact on sales and customer satisfaction.
2. Model Updates:
Periodically retrain AI models with new data to improve accuracy.
3. System Updates:
Release updates to fix bugs, enhance features, and scale the system as needed.
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Technology Stack
Facial Recognition:
Frameworks: Mediapipe, DeepFace/FaceNet.
Cloud Services: AWS Rekognition or Google Vision AI.
Speech Recognition:
Frameworks: Google Speech-to-Text API, spaCy, Hugging Face Transformers.
Backend Integration:
Core: Odoo, Python.
Frameworks: FastAPI, WebSockets, Celery + RabbitMQ.
Frontend Development:
React.js or Vue.js.
Database and Storage:
PostgreSQL, Redis, Amazon S3 or Google Cloud Storage.
Deployment:
Docker, Kubernetes, AWS or Google Cloud.