Update README.md

README.md

@@ -1,19 +1,81 @@
 ---
-title: Engine Predictive Maintenance
-emoji: 🚀
-colorFrom: red
-colorTo: red
+title: Engine Predictive Maintenance App
+emoji: "🛠️"
+colorFrom: purple
+colorTo: pink
 sdk: docker
-app_port: 8501
-tags:
-- streamlit
 pinned: false
-short_description: Streamlit template space
 ---
 
-# Welcome to Streamlit!
+# 🛠️ Smart Engine Predictive Maintenance App
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+This interactive Streamlit application predicts whether an engine is likely to be **Faulty (1)** or **Normal (0)** using real-time sensor readings.  
+It is designed to support **preventive maintenance decision-making** by identifying engines at higher risk of failure before breakdown occurs.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+
+## ✅ Key Features
+
+- **Single Engine Prediction** using manual sensor inputs  
+- **Probability-based output** for Faulty / Normal (where supported by the model)  
+- **Feature engineering built-in** (the app automatically computes engineered features to match the training schema)  
+- **Download engineered input row** as CSV for traceability  
+- **Bulk CSV Prediction** (upload a CSV and generate batch predictions)  
+- **Download bulk predictions** directly from the UI
+
+---
+
+## 🧠 Model Details
+
+- **Algorithm:** Gradient Boosting Classifier  
+- **Training Data:** Engine sensor telemetry dataset  
+- **Target Variable:** `Engine Condition`  
+  - `0 = Normal`
+  - `1 = Faulty`
+
+**Reference Metrics (from model evaluation):**
+- Recall (Faulty): ~0.84  
+- ROC-AUC: ~0.70  
+- PR-AUC: ~0.80  
+
+---
+
+## 🧾 Required Input Features (Single & Bulk)
+
+Your CSV or manual inputs must include **only the raw sensor columns** below:
+
+1. `Engine rpm`  
+2. `Lub oil pressure`  
+3. `Fuel pressure`  
+4. `Coolant pressure`  
+5. `lub oil temp`  
+6. `Coolant temp`
+
+The app computes additional engineered features internally (ratios, indices, and warning flags) to align with the model training pipeline.
+
+---
+
+## 📦 Bulk Prediction Instructions
+
+1. Upload a CSV file with the 6 required raw sensor columns listed above.  
+2. The app will generate:
+   - `Predicted_Class` (0/1)
+   - `Faulty_Probability` (if available)
+
+3. Download the results using the provided **Download Bulk Predictions CSV** button.
+
+---
+
+## 🚀 Deployment
+
+This Space uses a Docker-based deployment with Streamlit running on port **8501**. Hugging Face automatically maps ports during deployment.
+
+---
+
+## 🔗 Project Links
+
+- **Model Hub:** `simnid/predictive-maintenance-model`  
+- **Dataset Hub:** `simnid/predictive-engine-maintenance-dataset`  
+- **GitHub Repository:** *(add your repo link here once finalized)*
+
+---