Add-On modules for existing App
Budget: $250 – $750 USD
I have a Flutter mobile app under development for a weight-loss project.
The app will include:
Bluetooth integration with a smart body-fat scale
Firebase Authentication
Firestore database
User measurement dashboard (weight, body fat %, etc.)
Basic UI screens
I need a developer to add three additional features on top of the existing codebase.
This is not a full app build; only additional modules.
The app is part of a structured 30-day weight-loss challenge, activated through codes inside each scale package.
Feature 1 — One-Time Activation Code System
Each purchased scale package contains a unique activation code that unlocks the 30-day challenge inside the app.
Requirements:
Build an Activation Screen where users enter a 6–10 character code
Validate the code against a Firestore collection
Unlock the challenge only if the code is valid and unused
Mark code as used and store userId + timestamp
Firestore structure (suggested):
Collection: activationCodes
Fields: code, used, userId, createdAt
Build a CSV import mechanism so I can upload new code batches whenever new inventory arrives
A simple cloud function or upload script is enough
No complex admin dashboard required
Feature 2 — Human Coach Chat (Tawk.to Integration)
Add a “Mindset Coach” chat screen inside the app.
Requirements:
Load a Tawk.to widget via WebView or official SDK integration
Autofill Firebase user details (email or UID) into Tawk attributes if possible
Add a “Book Kickoff Session” button—opens a Calendly/Cal.com link externally
No admin panel needed; coaches reply from the existing Tawk dashboard
This is a straightforward WebView integration within a dedicated Flutter screen.
Feature 3 — AI Diet Coach (GPT-4 / GPT-4o Integration)
Add an AI-based diet coach inside the app.
The AI must be trained with my predefined nutritional guidelines and behavior rules.
Frontend (Flutter):
Chat interface (message bubbles, scroll, loading indicators)
Connect to backend API
Save chat history per user in Firestore
Retrieve user metrics (weight, body fat %, etc.) from Firestore to customize the answer
Backend (Node.js, Vercel or AWS):
Build one endpoint: /diet-chat
Input:
User message
Firebase user ID
User body metrics from Firestore
Apply my provided dietitian rulebook and prompt structure
(AI must follow these rules to behave like a structured weight-loss guide)
Call the OpenAI API with these rules
Return formatted responses
Add basic safety filtering (no medical diagnosis)
Hosting:
Deploy on Vercel or AWS Lambda using my available cloud credits
Must be lightweight and cost-efficient
No admin interface needed.
Deliverables
Updated Flutter project including:
Activation code system
Tawk.to mindset coach screen
AI Diet Coach chat screen
Node.js backend deployed and connected
Firestore integration for activation codes and chat history
A working APK for testing
Clean, maintainable code with comments
Required Skills
Flutter and Dart
Firebase Auth and Firestore
WebView integration
Node.js (Express or serverless)
OpenAI API usage
Experience with chat interfaces
Ability to work inside an existing codebase
The app will include:
Bluetooth integration with a smart body-fat scale
Firebase Authentication
Firestore database
User measurement dashboard (weight, body fat %, etc.)
Basic UI screens
I need a developer to add three additional features on top of the existing codebase.
This is not a full app build; only additional modules.
The app is part of a structured 30-day weight-loss challenge, activated through codes inside each scale package.
Feature 1 — One-Time Activation Code System
Each purchased scale package contains a unique activation code that unlocks the 30-day challenge inside the app.
Requirements:
Build an Activation Screen where users enter a 6–10 character code
Validate the code against a Firestore collection
Unlock the challenge only if the code is valid and unused
Mark code as used and store userId + timestamp
Firestore structure (suggested):
Collection: activationCodes
Fields: code, used, userId, createdAt
Build a CSV import mechanism so I can upload new code batches whenever new inventory arrives
A simple cloud function or upload script is enough
No complex admin dashboard required
Feature 2 — Human Coach Chat (Tawk.to Integration)
Add a “Mindset Coach” chat screen inside the app.
Requirements:
Load a Tawk.to widget via WebView or official SDK integration
Autofill Firebase user details (email or UID) into Tawk attributes if possible
Add a “Book Kickoff Session” button—opens a Calendly/Cal.com link externally
No admin panel needed; coaches reply from the existing Tawk dashboard
This is a straightforward WebView integration within a dedicated Flutter screen.
Feature 3 — AI Diet Coach (GPT-4 / GPT-4o Integration)
Add an AI-based diet coach inside the app.
The AI must be trained with my predefined nutritional guidelines and behavior rules.
Frontend (Flutter):
Chat interface (message bubbles, scroll, loading indicators)
Connect to backend API
Save chat history per user in Firestore
Retrieve user metrics (weight, body fat %, etc.) from Firestore to customize the answer
Backend (Node.js, Vercel or AWS):
Build one endpoint: /diet-chat
Input:
User message
Firebase user ID
User body metrics from Firestore
Apply my provided dietitian rulebook and prompt structure
(AI must follow these rules to behave like a structured weight-loss guide)
Call the OpenAI API with these rules
Return formatted responses
Add basic safety filtering (no medical diagnosis)
Hosting:
Deploy on Vercel or AWS Lambda using my available cloud credits
Must be lightweight and cost-efficient
No admin interface needed.
Deliverables
Updated Flutter project including:
Activation code system
Tawk.to mindset coach screen
AI Diet Coach chat screen
Node.js backend deployed and connected
Firestore integration for activation codes and chat history
A working APK for testing
Clean, maintainable code with comments
Required Skills
Flutter and Dart
Firebase Auth and Firestore
WebView integration
Node.js (Express or serverless)
OpenAI API usage
Experience with chat interfaces
Ability to work inside an existing codebase