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| title: SmartFit | |
| emoji: 🏋️ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.3.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # SmartFit | |
| This application utilizes a Random Forest machine learning model trained on a synthetic dataset of 10,000 user profiles to generate personalized workout routines. The system analyzes biological factors including age, gender, weight, and injuries to construct a tailored training regimen customized to the user's specific goals and equipment availability. | |
| ## Usage Instructions | |
| 1. **Input Details:** Enter your biological metrics (Age, Gender, Weight, Height). | |
| 2. **Configuration:** Select your desired Fitness Goal, Available Equipment, and Experience Level. | |
| 3. **Safety:** Indicate any active injuries to adjust the exercise selection logic. | |
| 4. **Generate:** Click Submit to receive a full, step-by-step workout plan. | |
| ## Model Specifications | |
| * **Architecture:** Random Forest Classifier | |
| * **Training Data:** 10,000 Synthetic User Profiles (SmartFit Dataset) | |
| * **Logic Engine:** Python-based rule engine for load calculation and injury management. |