DeepFit is an intelligent AI personal trainer that provides personalized workout plans, real-time form feedback, and progress tracking. Developed by Alikearn Studio, DeepFit uses advanced AI models to create a personalized fitness experience tailored to your goals, equipment, and fitness level.
Meet Max, your AI fitness coach who offers:
- Personalized workout recommendations based on your profile
- Exercise form guidance and corrections
- Answers to any fitness-related questions
- Training advice adapted to your specific situation
Track your fitness journey with detailed analytics:
- Strength progression graphs for each exercise
- Volume tracking across workouts
- Muscle group balance visualization
- Personal record tracking
- Weekly workout frequency analysis
Build the perfect workout routine:
- Comprehensive exercise database organized by muscle groups
- Set tracking with different types (normal, warm-up, drop sets)
- Weight and rep progression tracking
- Save and reuse your favorite routines
Create detailed profiles that help Max understand your needs:
- Fitness level assessment
- Equipment availability tracking
- Physical limitation accommodation
- Training history integration
- Frontend: React, Framer Motion
- State Management: React Context API
- Styling: TailwindCSS
- AI Integration: Gemini API, Moondream API
- Deployment: Netlify Functions, Netlify Hosting
DeepFit uses a combination of AI models to deliver personalized coaching. The application sends user context, questions, and optionally image analysis to provide contextually relevant responses.
// Simplified AI chat integration
const aiMessages = [
{
role: "system",
content: `You are Max, a certified personal trainer and sports coach.
You're knowledgeable about fitness and sports, but you're also a
well-rounded conversation partner with diverse interests and knowledge.
USER PROFILE:
- Name: ${userProfile.name}
- Age: ${userProfile.age}
- Fitness Level: ${fitnessLevel}
- Physical Limitations: ${physicalLimitations.join(', ')}
- Available Equipment: ${equipment.join(', ')}
`
}
];
// Add user message with context
if (imageAnalysis) {
userPrompt = `[Image Analysis: ${imageAnalysis}]\n\n${userPrompt}`;
}
// Send to AI API and get response
const response = await fetch('/.netlify/functions/ai-chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
messages: aiMessages,
imageAnalysis,
userProfile
})
});The app tracks workout performance over time to visualize progress and provide insights for improvement.
// Sample progress visualization logic
const getExerciseProgressData = () => {
if (!selectedExercise) return [];
const filteredHistory = getFilteredHistory();
const progressData = [];
filteredHistory.forEach(workout => {
workout.exercises.forEach(exercise => {
if (exercise.name === selectedExercise) {
// Find the heaviest completed set
let maxWeight = 0;
let volume = 0;
exercise.sets.forEach(set => {
if (set.completed) {
const weight = parseFloat(set.actualWeight);
const reps = parseInt(set.actualReps);
if (!isNaN(weight) && !isNaN(reps)) {
if (weight > maxWeight) maxWeight = weight;
volume += weight * reps;
}
}
});
if (maxWeight > 0) {
progressData.push({
date: new Date(workout.startTime).toLocaleDateString(),
timestamp: new Date(workout.startTime).getTime(),
weight: maxWeight,
volume: volume
});
}
}
});
});
// Sort by date and return for visualization
return progressData.sort((a, b) => a.timestamp - b.timestamp);
};DeepFit uses a context-based system to manage workout data across components:
// WorkoutContext provider excerpt
export const WorkoutProvider = ({ children }) => {
const [workouts, setWorkouts] = useState([]);
const [workoutHistory, setWorkoutHistory] = useState([]);
const [activeWorkout, setActiveWorkout] = useState(null);
const [userProfile, setUserProfile] = useState(null);
// Complete a workout session
const completeWorkout = () => {
if (!activeWorkout) return;
const completedWorkout = {
...activeWorkout,
endTime: new Date().toISOString(),
duration: (new Date() - new Date(activeWorkout.startTime)) / 1000,
isCompleted: true
};
setWorkoutHistory(prev => [completedWorkout, ...prev]);
setActiveWorkout(null);
return completedWorkout;
};
// Other workout management functions...
return (
<WorkoutContext.Provider
value={{
workouts,
workoutHistory,
activeWorkout,
userProfile,
createWorkout,
updateWorkout,
deleteWorkout,
startWorkout,
completeWorkout,
updateWorkoutSet
}}
>
{children}
</WorkoutContext.Provider>
);
};
- Integration with fitness wearables for automatic data collection
- Video analysis for real-time form feedback
- Social features to train with friends
- Expanded exercise database with video demonstrations
- Nutrition tracking and meal planning
Jordan Montée (Alikel/AlikelDev) is a developer focused on creating AI-powered applications that provide real value to users. As the founder of Alikearn Studio, I develop AI assistants and applications that enhance everyday experiences.
- DeepChef - AI culinary assistant (GitHub)
- Alikearn Studio Portfolio
This project is presented for demonstration purposes. The actual codebase is proprietary and not open-source. The concepts, designs, and code snippets shared in this repository are © 2025 Alikearn Studio.