Technical Details (docs/technical-details.md)

1. System Architecture Overview

Zoar AI operates as a modular, cloud-based system, ensuring scalability, reliability, and high performance.

Key Components:

1. Frontend:

   • Cross-platform mobile and web applications for user interaction.

   • Features include onboarding, fitness/nutrition plan visualization, and progress tracking.

2. Backend:

   • Handles business logic, API integrations, and user data processing.

   • Facilitates communication between the frontend and AI modules.

3. AI Models:

   • Processes user inputs to generate personalized fitness and nutrition plans.

   • Continuously learns and adapts based on user behavior and feedback.

4. Database:

   • Stores user data, including health metrics, preferences, and progress logs.

5. Cloud Infrastructure:

   • Provides scalability and handles large-scale user interactions.

   • Managed using services like AWS, Google Cloud, or Azure.

Architecture Diagram:

plaintextCopier le code  User Device (Mobile/Web)
         |
      Frontend
         |
      Backend API
         |
  AI Models & Databases
         |
    Cloud Infrastructure

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2. Technology Stack

Frontend

• Framework: React Native for a seamless cross-platform experience.

• UI/UX Design: Figma for prototyping and Material-UI for components.

• API Communication: Axios for handling REST API calls.

Backend

• Framework: Node.js with Express.js for building APIs.

• Database: PostgreSQL for structured data and Firebase for real-time updates.

• Authentication: OAuth2 and JWT for secure user login and session management.

• Cloud Functions: Serverless functions to handle specific tasks efficiently.

AI/ML Models

• Language: Python for model development.

• Frameworks: TensorFlow, PyTorch, and scikit-learn for training and inference.

• Datasets: Pre-trained datasets from public repositories (e.g., Kaggle, UCI Machine Learning Repository).

Cloud Infrastructure

• Hosting: AWS Elastic Beanstalk or Google Cloud App Engine.

• Storage: Amazon S3 or Google Cloud Storage for secure file handling.

• Monitoring: New Relic or Datadog for performance tracking.

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3. Data Flow

1. User Inputs:

   • Data from the user (e.g., fitness goals, dietary preferences) is captured via the frontend.

   • Example input:

     jsonCopier le code{
       "age": 30,
       "weight": 75,
       "goal": "muscle_gain",
       "diet_preference": "keto"
     }

2. Processing in Backend:

   • The backend validates inputs and forwards them to the AI models.

3. AI Output:

   • AI modules generate workout plans, meal suggestions, and progress analytics.

   • Example output:

     jsonCopier le code{
       "workout_plan": [
         {"exercise": "Push-ups", "sets": 4, "reps": 12},
         {"exercise": "Plank", "duration": "30 seconds"}
       ],
       "meal_plan": [
         {"meal": "Grilled Chicken Salad", "calories": 300},
         {"meal": "Avocado Smoothie", "calories": 250}
       ]
     }

4. User Feedback:

   • Users can log adherence or provide feedback, which feeds into the AI for continuous improvement.

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4. Scalability and Security

Scalability:

• Load Balancing: Auto-scaling services like AWS Elastic Load Balancer to handle traffic spikes.

• Microservices: Modular architecture allows independent scaling of components (e.g., AI, database).

Security:

• Data Encryption: AES-256 for database encryption and HTTPS for API calls.

• Authentication: Multi-factor authentication (MFA) for user accounts.

• Compliance: Adherence to GDPR and CCPA for data privacy.

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5. Code Example: Backend API

Fitness Plan Generation API

javascriptCopier le codeconst express = require('express');
const app = express();
app.use(express.json());

// Mock database
const workouts = {
  beginner: ["Push-ups", "Squats", "Plank"],
  intermediate: ["Deadlift", "Bench Press", "Pull-ups"],
};

app.post('/api/generate-workout', (req, res) => {
  const { goal, level } = req.body;
  if (!goal || !level || !workouts[level]) {
    return res.status(400).json({ error: "Invalid inputs" });
  }

  const plan = workouts[level].map(exercise => ({
    exercise,
    sets: 4,
    reps: 10,
  }));

  res.json({ workout_plan: plan });
});

app.listen(3000, () => console.log('Server running on port 3000'));

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6. Future Enhancements

• Advanced Analytics:

  • Use machine learning to predict user performance and optimize plans.

• Wearable Integration:

  • Incorporate real-time data from Fitbit, Apple Watch, etc., for adaptive recommendations.

• Gamification:

  • Add features like challenges, leaderboards, and rewards to enhance user engagement.