https://github.com/nidhishrathod/exercisecounter
Exercise Counter for bicep curls, pushups and squats using media pipe pose detection
https://github.com/nidhishrathod/exercisecounter
ai fitness gpt2tokenizer mediapipe pose-detection
Last synced: 10 months ago
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Exercise Counter for bicep curls, pushups and squats using media pipe pose detection
- Host: GitHub
- URL: https://github.com/nidhishrathod/exercisecounter
- Owner: NidhishRathod
- Created: 2024-11-12T10:00:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-26T14:58:26.000Z (12 months ago)
- Last Synced: 2025-02-06T07:11:14.417Z (11 months ago)
- Topics: ai, fitness, gpt2tokenizer, mediapipe, pose-detection
- Language: Python
- Homepage:
- Size: 39.6 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI-Powered Workout and Diet Planner
This project is an AI-powered workout and diet planner that provides personalized fitness plans based on user input. It leverages the GPT-2 model for generating tailored workout and diet plans, integrated with a GUI built using Tkinter for an intuitive user experience. Additionally, the project includes a script for exercise counters using computer vision and Mediapipe.
## Features
- **Personalized Workout and Diet Plan**: Based on the user's goal (e.g., Muscle Gain, Weight Loss, Maintenance), age, weight, height, and dietary preferences, the system generates a detailed workout and diet plan.
- **BMI Calculation**: Automatically calculates the user's BMI and provides health status (Underweight, Normal weight, Overweight, Obese).
- **Exercise Counters**: Integrated exercise counter scripts for push-ups, squats, and biceps, utilizing computer vision techniques with Mediapipe.
- **User Progress Saving**: Saves user details and fitness progress in a text file for future reference.
- **GUI Interface**: User-friendly interface built with Tkinter for input collection and displaying the generated plans.
## Requirements
- Python 3.6 or later
- PyTorch
- transformers
- Mediapipe
- OpenCV
- Tkinter
## Installation
1. Create a virtual environment and activate it:
```
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
2. Install the required dependencies:
```
pip install torch torchvision torchaudio transformers mediapipe opencv-python
```
4. Download the pre-trained GPT-2 model and tokenizer:
```
python -c "from transformers import GPT2LMHeadModel, GPT2Tokenizer; model = GPT2LMHeadModel.from_pretrained('gpt2'); tokenizer = GPT2Tokenizer.from_pretrained('gpt2')"
```
5. Run the application:
```
python Bot.py
```
## Usage
- **Start the Application**: After running the application, a window will pop up where you can input your details, select your fitness goal, and choose your dietary preferences.
- **Generate a Plan**: After entering the required details, click "Generate Plan" to receive a personalized workout and diet plan.
- **Exercise Counters**: You can also use the exercise counter feature by selecting a script (Biceps, Push-Up, Squat) and running it.
## Acknowledgements
- GPT-2 model from Hugging Face
- Mediapipe for pose detection
- OpenCV for video processing
- Tkinter for GUI creation