FitPlan Flask Prototype

A full-stack web application prototype for personalized health and fitness planning, built with Flask and SQLite.

Features

Completed ✅

• User Authentication: Registration and login system
• Onboarding Flow: 6-step questionnaire matching your wireframes
• Database Integration: SQLite with user profiles and plans
• Responsive Design: Mobile-first UI matching wireframe aesthetics
• Interactive Dashboard: Tabbed interface for workouts, meals, and grocery lists
• Progress Tracking: Local storage for workout completion and meal logging
• Form Validation: Client and server-side validation
• Calories and Macro targets: Calculation of BMR, TDEE, and macros based on user needs
• Sample Data: Pre-populated workout plans, meal plans, and grocery lists

Work in Progress 🚧

• ML Pipeline Integration (placeholder endpoints created)
• Advanced meal plan generation
• Workout plan customization

File Structure

fitplan-app/
├── app.py                          # Main Flask application
├── fitplan.db                      # SQLite database (auto-created)
├── requirements.txt                # Python dependencies
├── runtime.txt                     # Python version 
├── static/
│   ├── css/
│   │   ├── main.css               # Global styles
│   │   ├── forms.css              # Onboarding forms
│   │   └── dashboard.css          # Dashboard & plans
│   └── js/
│       ├── main.js                # Core functionality
│       ├── forms.js               # Form handling
│       └── plans.js               # Dashboard interactions
└── templates/
    ├── base.html                  # Common layout
    ├── welcome.html               # Login page
    ├── signup.html                # Registration
    ├── questionnaire_intro.html   # Onboarding start
    ├── basic_info.html            # Step 1: Basic info
    ├── activity_level.html        # Step 2: Activity Level
    ├── fitness_goals.html         # Step 3: Goals
    ├── dietary_restrictions.html  # Step 4: Diet restrictions
    ├── physical_limitations.html  # Step 5: Limitations
    ├── equipment_access.html      # Step 6: Equipment
    ├── profile_summary.html       # Step 7: Summary
    └── dashboard.html             # Main dashboard

Setup Instructions

Prerequisites

• Python 3.9.23
• pip (Python package manager)
• Flask

Installation

1. Create project directory:

   mkdir fitplan-app
   cd fitplan-app

2. Create virtual environment:

   python -m venv venv
   
   # Activate virtual environment
   # On Windows:
   venv\Scripts\activate
   # On Mac/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install flask sqlite3

4. Create all the files from the artifacts above in their respective directories.

5. Run the application:

   python app.py

6. Open your browser and navigate to:

   http://localhost:5000
   

Usage Guide

1. Registration Flow

• Start at the welcome page
• Click "Create Account"
• Fill in basic registration info
• Complete the 7-step onboarding questionnaire

2. Onboarding Steps

1. Basic Info: Age, gender, height, weight
2. Activity Level: Sedentary, Lightly Active, Moderately Active, Very Active, Extra Active
3. Fitness Goals: Weight loss, strength, muscle building, endurance
4. Dietary Restrictions: Vegetarian, gluten-free, allergies, etc.
5. Physical Limitations: Injuries or health considerations
6. Equipment Access: Available workout equipment
7. Profile Summary: Review and confirm details

3. Dashboard Features

• Workout Tab: Weekly workout schedule with exercise details
• Meals Tab: Daily meal plans with calorie and macro information
• Grocery Tab: Auto-generated shopping list organized by store section
• Interactive Elements:
  • Click grocery items to check them off
  • Mark workouts as complete
  • Log meals as consumed

Technical Details

Database Schema

-- User profiles with all onboarding data
users: id, name, email, password, age, gender, weight, height, 
       activity_level, fitness_goals, dietary_restrictions, 
       physical_limitations, available_equipment, 
       brm, tdee, caloric_target, protein_target_g, carbs_target_g, 
       fat_target_g, created_at

-- Generated workout plans (JSON data)
workout_plans: id, user_id, week_date, plan_data, created_at

-- Generated meal plans (JSON data)  
meal_plans: id, user_id, week_date, plan_data, created_at

-- Generated grocery lists (JSON data)  
grocery_list: id, user_id, week_date, grocery_data, created_at

API Endpoints (Placeholders)

POST /api/generate-workout-plan   # Returns "Work in progress"
POST /api/generate-meal-plan      # Returns "Work in progress"  
POST /api/generate-grocery-list   # Returns "Work in progress"

Sample Data

The prototype includes realistic sample data:

• Workout Plan: 7-day schedule with back-friendly exercises using dumbbells
• Meal Plan: Gluten-free meals totaling 1,650 calories/day
• Grocery List: Organized by store sections with prices (~$89 total)

Next Steps for Production

1. ML Integration: Connect the placeholder API endpoints to your RAG pipeline and LLM services
2. User Management: Add password reset, email verification, profile editing
3. Data Persistence: Move from localStorage to database for user progress
4. Advanced Features:
   • Multiple week plans
   • Exercise instruction videos
   • Recipe details with cooking instructions
   • Grocery store integration
5. Performance: Add caching, database optimization, CDN for assets
6. Security: Environment variables, HTTPS, input sanitization, rate limiting

Testing

Sample User Data

You can test the app with these values:

• Name: John Smith
• Age: 32, Male
• Height: 5'10"
• Weight: 180 lbs
• Goal: Weight Loss
• Restrictions: Gluten-Free
• Limitations: Back Problems
• Equipment: Bodyweight, Dumbbells

The app will generate the sample workout and meal plans automatically after onboarding.