Update README.md

README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SmartFit AI - Personalized Workout Engine
+### A Machine Learning & Rule-Based Logic Application
+
+**SmartFit AI** is an intelligent fitness planner designed to create highly personalized workout routines. Unlike standard apps that give generic advice, SmartFit acts as a Digital Personal Trainer that adapts to your:
+* **Biomechanics:** Age, Gender, Weight, Height.
+* **Constraints:** Specific injuries (Knee, Back, Shoulder, etc.).
+* **Equipment:** Real-time filtering based on what you have (Gym, Home Dumbbells, or Bodyweight).
+
+---
+
+## How it Works (The IO Pipeline)
+
+The system uses a unique Hybrid Pipeline combining Machine Learning with a Strict Logic Engine:
+
+1.  **INPUT:** User enters physical stats, goals, and injury data.
+2.  **MODEL (Classification):** A trained Random Forest Classifier analyzes the profile against 10,000 synthetic use-cases to determine the best Strategy Category (e.g., "High Volume Hypertrophy" vs. "Low Impact Stability").
+3.  **LOGIC ENGINE (The Calculator):** A Python-based expert system takes the strategy and:
+    * **Filters:** Removes any exercise dangerous for the specific injury.
+    * **Calculates:** Computes exact target weights (kg) based on user mass and level.
+    * **Randomizes:** Selects exercises dynamically to ensure variety.
+4.  **OUTPUT:** A complete, step-by-step custom plan with warm-ups and finishers.
+
+---
+
+## Data & Model Analysis
+
+### 1. The Dataset
+I generated a synthetic dataset of 10,000 user profiles to train the model. This allowed me to simulate edge cases (like an elderly person with a shoulder injury) that are hard to find in public datasets.
+
+**Data Distribution & Analysis:**
+![Data Analysis](imp1.png)
+*(Above: Analysis of the dataset features and target variable distribution)*
+
+### 2. Embeddings & Clustering
+Using NLP Embeddings, I analyzed the semantic similarity between different user profiles. This visualization proves that the model correctly identifies distinct "clusters" of users (e.g., Bodybuilders vs. Rehab patients).
+
+**User Segmentation (Embeddings):**
+![Embeddings Graph](imp2.png)
+*(Above: Visualizing user groups based on profile similarity)*
+
+---
+
+## Tech Stack
+* **Model:** Scikit-Learn (Random Forest)
+* **Interface:** Gradio (Web UI)
+* **Logic:** Python Custom Algorithms
+* **Deployment:** Hugging Face Spaces
+
+---
+
+### Disclaimer
+This is a final project for an AI course. While the logic is based on fitness principles, always consult a doctor before starting a new training program.