Update README.md

README.md

@@ -1,3 +1,15 @@
+---
+title: Jet Engine Predictive Maintenance
+emoji: ✈️
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+sdk_version: 1.31.0
+python_version: 3.10
+app_file: app.py
+pinned: false
+---
+
 # ✈️ Jet Engine Predictive Maintenance System
 ### B.Tech AI & Data Science - Special Project
 
@@ -5,10 +17,6 @@ This project implements a **Predictive Maintenance (PdM)** solution for industri
 
 ---
 
-## 🚀 Live Demo
-You can access the live interactive dashboard here: 
-**[INSERT YOUR HUGGING FACE SPACE LINK HERE]**
-
 ## 📌 Project Overview
 In industrial settings, equipment failure leads to high costs and safety risks. This project transitions from "Reactive Maintenance" to "Predictive Maintenance" by using machine learning to forecast failures before they occur.
 
@@ -16,39 +24,19 @@ In industrial settings, equipment failure leads to high costs and safety risks.
 * **RUL Prediction:** Estimates the number of operational cycles left before failure.
 * **Interactive Dashboard:** Built with Streamlit for real-time sensor input.
 * **Visual Analytics:** Uses Gauge charts and status alerts (Healthy, Caution, Critical).
-* **AI Model:** Powered by **XGBoost Regression**, optimized for time-series tabular data.
-
----
-
-## 📊 Dataset Information
-The model is trained on the **NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS)**.
-* **Units:** 100 engines.
-* **Sensors:** 21 different sensors (Temperature, Pressure, Fan Speed, etc.).
-* **Goal:** Predict the "cycles" remaining until the engine reaches a failure state.
-
-
+* **AI Model:** Powered by **XGBoost Regression**.
 
 ---
 
 ## 🛠️ Technical Stack
-* **Language:** Python 3.10+
+* **Language:** Python 3.10
 * **ML Framework:** XGBoost
-* **Data Processing:** Pandas, NumPy
 * **UI/Deployment:** Streamlit, Hugging Face Spaces
-* **Visualization:** Plotly, Seaborn
-
----
-
-## 📁 Repository Structure
-* `app.py`: The main Streamlit application code.
-* `train_local.py`: Script used to train the model and generate the `.pkl` file.
-* `engine_model.pkl`: The trained XGBoost model (Serialized).
-* `requirements.txt`: List of Python libraries required for the environment.
+* **Visualization:** Plotly
 
 ---
 
 ## ⚙️ How to Run Locally
-1. **Clone the repository:**
+1. **Install Dependencies:**
    ```bash
-   git clone [YOUR_REPO_URL]
-   cd [REPO_NAME]
+   pip install -r requirements.txt